Water quality sampling was conducted by randomly selecting five commonly used shallow wells from the Half-London Ward in Tunduma, Tanzania. These wells were coded as WW1, WW2, WW3, WW4, and WW5. The selection followed the guidelines provided by the Environmental Protection Agency (EPA) as revealed by [22] , which recommend a minimum sample size of five to ten shallow wells when studying groundwater within a specific community. Considering the temporal variation in rainfall patterns, water sampling was carried out monthly over 12 months, from June to May, to capture seasonal changes. Water was collected from the designated points of the five shallow wells, as shown in Figure 1 . The position and elevation of the wells were measured using a Global Positioning System (GPS), as detailed in Table 1 , and the depth of each well was recorded.

Water analysis was performed for biological (faecal coliform and total coliform), selected physical (pH, electrical conductivity, turbidity, total suspended solids, total dissolved solids, and colour), and chemical (nitrate, phosphate, total iron, and BOD) parameters. The parameters were selected based on their public health importance [23] and their relevance to public water consumption [24] . Samples for microbial tests were collected in sterilized glass bottles, which were rinsed three times with the source water to minimize the risk of external contamination. Since all the shallow wells were open, the sample bottle was held by a bottle holder and submerged to a depth of 0.4 m below the water level to avoid collecting floating debris. For bacterial counts (total coliforms and faecal coliforms), the membrane filtration technique was applied, with results expressed as counts per 100 ml for each sample. All procedures for water sample collection and analysis followed the methods prescribed by the American Public Health Association [25] , and the results were compared against the World Health Organization [26] and [27] for water quality to determine the suitability of the water for domestic use.

In addition to traditional water quality assessments, Principal Component Analysis (PCA) was employed as a robust statistical tool to manage and interpret the multidimensional water quality data collected over the year. PCA helps in reducing the complexity of the dataset by identifying key variables that contribute most to the variance in water quality. This method was used to transform the original water quality parameters into a set of principal components, which simplified the data while preserving essential information. The PCA was perceived to be capable of revealing the most significant factors affecting water quality, including biological contamination and chemical pollutants, thus providing clearer insights into the primary sources of contamination. Principal Component Analysis (PCA) was conducted using the Jamovi software [28] , following the method outlined by [29] .

In spite of the water quality from shallow wells being site specific, it also depends on the various parameters. The pollution parameters of the 5 shallow wells have been summarized by the calculation of minimum and maximum, mean (average), median, standard deviation, skewness, kurtosis, and Shapiro-Wilk as shown in Tables 1-5 . When the skewness is considered for all the wells, the values ranged from −2.2 to 11.8 with no zero-value suggesting that the data are skewed. The Kurtosis values for all the wells suggest that the colour for WW1, total dissolved salts for WW2, total dissolved salts and Salinity for WW4 and total dissolved salts for WW5 have their values greater than 3 suggesting that the data are from distribution that has sharper peak and fatter tails compared to a normal distribution [30] .

The correlation matrices for various water quality parameters were analysed using Pearson’s correlation coefficients, as detailed in Tables 6-10 . Pearson’s correlation coefficient (r) values were used to assess the strength and direction of the relationships between variables. According to [25] and [28] correlation values are interpreted as follows: |r| ≥ 0.5 is considered a strong correlation, 0.3 ≤ |r| < 0.5 is moderate, 0.1 ≤ |r| < 0.3 is weak, and 0 indicates no correlation. A positive r indicates that as one variable increases, the other also increases, while a negative r suggests an inverse relationship between the variables.

In WW1 ( Table 6 ), pH showed strong correlations with temperature (Temp), total suspended solids (TSS), nitrate ( ${\text{NO}}_3^{-}$), and faecal coliforms (FC). Electrical conductivity (EC) correlated with ${\text{NO}}_3^{-}$ and biochemical oxygen demand (BOD). Total dissolved solids (TDS) showed strong correlations with ${\text{NO}}_3^{-}$, iron (Fe²⁺), FC, and total coliforms (TC), highlighting the connection between runoff and microbial contamination. Salinity (Sal) correlated with phosphate ( ${\text{PO}}_4^{3-}$), BOD, and magnesium (Mg²⁺). Chloride (Cl⁻) was negatively correlated with TC, suggesting reduced microbial contamination with higher Cl⁻ concentrations. Other significant correlations included sulphate ( ${\text{SO}}_4^{2-}$) with calcium (Ca²⁺), and ${\text{NO}}_3^{-}$with Fe²⁺, FC, and TC, pointing to potential contamination from fertilizers and human waste sources [14] [31] .

For WW2 ( Table 7 ), EC was negatively correlated with coliforms (Col), while temperature (Temp) was positively correlated with Col and Ca²⁺ but negatively correlated with BOD. TDS showed significant correlations with hardness (Hd), ${\text{NO}}_3^{-}$, Mg²⁺, Fe²⁺, and TC. Alkali (Alk) was correlated with ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$, BOD, and Mg²⁺, reflecting the geological influence on water chemistry. Similar patterns were observed for Cl⁻, which was positively correlated with ${\text{PO}}_4^{3-}$, BOD, and Mg²⁺. These relationships indicate that both geological strata and human activities, such as pit latrine use and agricultural runoff, significantly influence groundwater quality [32] [33] .

In WW3 ( Table 8 ), pH correlated with hardness (Hd), while EC correlated with Alk, reflecting mineral dissolution. Col was positively correlated with turbidity (Turb) and Alk. Strong correlations were found between TSS, Cl⁻, and Fe²⁺, indicating contamination from runoff and potential industrial activities. Additionally, ${\text{PO}}_4^{3-}$ and ${\text{NO}}_3^{-}$ were strongly correlated with Ca²⁺, Mg²⁺, FC, and TC, pointing to contamination from fertilizers and human waste sources. These findings align with studies highlighting the role of agricultural runoff and sanitation practices in groundwater contamination [7] [34] and [35] .

For WW4 ( Table 9 ), EC was negatively correlated with Col and positively correlated with Alk. Temperature was negatively correlated with TSS and Hd but positively correlated with Sal and Fe²⁺, indicating a complex relationship between water temperature and chemical composition. Turb was correlated with ${\text{NO}}_3^{-}$, ${\text{PO}}_4^{3-}$, and other dissolved solids, indicating pollution from both agricultural and geological sources. The correlation between ${\text{SO}}_4^{2-}$, BOD, and Mg²⁺ further supported the role of mineral dissolution and organic pollution in influencing water quality [35] [37] .

In WW5 ( Table 10 ), pH was significantly correlated with several variables, including Turb, TDS, Hd, Alk, Sal, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$, BOD, and Mg²⁺. Negative correlations were observed between EC and Col, while temperature was correlated with Col but negatively correlated with Hd, Mg²⁺, and BOD. TDS, Hd, and Alk were positively correlated with ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, and ${\text{NO}}_3^{-}$, indicating pollution from fertilizers and waste sources. These results demonstrate the influence of human activities and natural processes on groundwater quality in WW5 [29] .

The water quality from WW1 can be effectively described by five principal components (PC1, PC2, PC3, PC4, and PC5), accounting for 87.53% of the total variability in the dataset ( Table 11 ). The variance contributions for each component are: 33.05% for PC1, 25.97% for PC2, 14.56% for PC3, 8.44% for PC4, and 5.50% for PC5, indicating PC1 and PC2 are the most influential. The Sum of Squared Loadings (SS Loadings) also explain 87.53% of the variability, with PC1, PC2, PC3, PC4, and PC5 contributing 29.47%, 18.47%, 17.96%, 14.15%, and 7.47%, respectively. These results indicate that the first two components capture the majority of the water quality variability, consistent with findings in similar environmental studies [15] [40] .

Figure 3 presents the factor scores for the five principal components (PCs) that explain 87.53% of the total variance in the water quality data from WW1. These scores indicate the contribution of each variable to the variance explained by each component, providing insight into the underlying factors driving water quality variations.

PC1 (33.05%) is strongly influenced by pH, electrical conductivity (EC), temperature, total suspended solids (TSS), total dissolved solids (TDS), hardness (Hd), phosphate ( ${\text{PO}}_4^{3-}$), nitrate ( ${\text{NO}}_3^{-}$), iron (Fe²⁺), faecal coliforms (FC), and total coliforms (TC). This suggests that surface runoff is a major factor, contributing to increased EC, Temp, TSS, and TDS, while contamination from pit latrines is indicated by high FC and TC levels. The presence of agrochemical fertilizers is highlighted by the elevated levels of ${\text{PO}}_4^{3-}$ and ${\text{NO}}_3^{-}$. Interestingly, chloride (Cl⁻) has a negative effect, implying an inverse relationship with the contaminants driving PC1. Variables such as alkalinity (Alk), biochemical oxygen demand (BOD), calcium (Ca²⁺), coliforms (Col), magnesium (Mg²⁺), salinity (Sal), sulphate ( ${\text{SO}}_4^{2-}$), and turbidity (Turb) do not significantly influence PC1. This aligns with studies showing that microbial and chemical contaminants are frequently associated with surface runoff and poor sanitation practices [14] and [41] .

PC2 (25.97%) is characterized by the positive contribution of EC, hardness (Hd), Alk, Sal, BOD, Ca²⁺, Mg²⁺, and FC, pointing to the influence of geological formations and pit latrine contamination. High alkalinity, hardness, and salinity often result from water interacting with geological strata, such as limestone or dolomite, which release calcium and magnesium ions into the water [42] . The presence of faecal coliforms further suggests the proximity of contamination sources like pit latrines. Variables such as Cl⁻, Col, Fe²⁺, ${\text{NO}}_3^{-}$, pH, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, TC, TDS, Temp, TSS, and Turb do not significantly contribute to PC2, reinforcing the idea that geological processes dominate in this component [43] .

PC3 (14.56%) is dominated by coliforms (Col), turbidity (Turb), TSS, Sal, and Cl⁻, suggesting that surface runoff and microbial contamination play a key role. The positive loadings of TSS and Turb indicate the presence of suspended particles typically carried by runoff from urban or agricultural lands. The strong association between salinity and chloride further suggests influence from both surface runoff and geological sources, as these ions can enter the water through the dissolution of rock salts or human activities like road de-icing [41] . The negative contribution of EC and ${\text{PO}}_4^{3-}$ may suggest areas where high microbial contamination correlates with lower concentrations of these chemicals, potentially due to dilution or other local environmental factors.

PC4 (8.44%) is defined by the positive loadings of hardness (Hd), ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, Ca²⁺, and Mg²⁺, which are indicative of mineral dissolution from geological sources. The significant presence of sulphate and phosphate suggests the influence of sulphate-bearing minerals like gypsum or anthropogenic sources such as agricultural fertilizers. The negative effect of ${\text{PO}}_4^{3-}$ points to the possibility of competing interactions between geological sources and human activities for phosphorus levels in the water. These findings align with studies indicating that areas with high sulphate and hardness often reflect regions where groundwater interacts with mineral-rich bedrock [42] .

PC5 (5.50%) reflects the influence of pH, temperature (Temp), Cl⁻, and ${\text{SO}}_4^{2-}$, suggesting a combination of geological and human influences. Human activities such as water treatment or industrial discharges can impact pH and temperature, while the presence of sulphate and chloride may indicate inputs from both natural sources (e.g., mineral dissolution) and urban activities. The negative contributions of salinity and Mg²⁺ suggest that these factors are less influenced by surface interactions and may reflect deeper groundwater characteristics. This component aligns with findings from studies where industrial or urban activities have a significant impact on water temperature and chemical composition [14] .

The factor scores reveal a complex interplay between surface runoff, agricultural practices, pit latrine contamination, and geological processes. PC1 emphasizes the impact of surface runoff and microbial contamination, PC2 and PC4 highlight the role of geological formations, while PC3 and PC5 show the combined effects of human activities and natural processes. These findings are consistent with previous research that demonstrates the significant contributions of both natural and anthropogenic factors to water quality variability [41] and [42] .

The water quality from WW2 can be effectively described by six principal components (PCs): PC1, PC2, PC3, PC4, PC5, and PC6, which together account for 92.55% of the total variability in the original dataset ( Table 12 ). The contribution of each PC to the variance is as follows: 36.17% for PC1, 22.72% for PC2, 11.17% for PC3, 9.35% for PC4, 8.13% for PC5, and 5.00% for PC6. Additionally, the Sum of Squared Loadings (SS Loadings) for these PCs explain 92.55% of the total variance, with each PC contributing 25.97%, 18.08%, 17.19%, 14.85%, 9.13%, and 7.34%, respectively. These results indicate that the first two components, PC1 and PC2, capture the majority of the variability, highlighting their importance in explaining the water quality variation in WW2 [29] .

Figure 4 illustrates the factor scores for the six principal components (PCs) that explain 92.55% of the total variability in water quality data. These PCs highlight the dominant variables influencing water quality, providing insight into both natural processes and anthropogenic activities that impact the study area.

PC1, accounting for 36.17% of the total variance, is driven by pH, electrical conductivity (EC), temperature (Temp), coliforms (Col), turbidity (Turb), total suspended solids (TSS), total dissolved solids (TDS), salinity (Sal), chloride (Cl⁻), sulphate ( ${\text{SO}}_4^{2-}$), magnesium (Mg²⁺), and iron (Fe²⁺). These variables indicate the influence of surface runoff (EC, Temp, Col, TSS, TDS), which contributes to elevated suspended solids and microbial contamination, and geological formations (pH, Sal, Cl⁻, ${\text{SO}}_4^{2-}$, Mg²⁺, Fe²⁺), which affect water chemistry through mineral dissolution. Variables such as hardness (Hd), alkalinity (Alk), phosphate ( ${\text{PO}}_4^{3-}$), nitrate ( ${\text{NO}}_3^{-}$), and faecal coliforms (FC) show no significant effect on PC1, aligning with studies that link surface runoff and geological strata as major drivers of water quality variation [42] .

PC2, contributing 22.72% of the variance, is influenced by Col, Turb, TSS, TDS, Hd, Alk, Sal, Cl⁻, and Mg²⁺. This reflects surface runoff (Col, Turb, TSS, TDS) and geological processes (Hd, Alk, Sal, Cl⁻, Mg²⁺). Runoff from urban or agricultural areas increases suspended solids, while mineral dissolution contributes to hardness and alkalinity in the groundwater. Variables such as pH, EC, Temp, ${\text{PO}}_4^{3-}$, and ${\text{NO}}_3^{-}$ show no effect. These patterns are consistent with studies of water quality where runoff and mineral dissolution affect the chemical composition of water sources [41] .

PC3, explaining 11.17% of the variance, is dominated by TSS, TDS, Alk, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, and ${\text{NO}}_3^{-}$, indicating surface runoff (TSS, TDS), agrochemical influences ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$), and geological factors (Alk, ${\text{SO}}_4^{2-}$). The presence of phosphates and nitrates suggests contamination from agricultural fertilizers, while alkalinity and sulphates point to mineral dissolution. Chloride (Cl⁻) and Fe²⁺ contribute negatively, possibly indicating dilution or lower concentrations of these ions in areas dominated by agricultural inputs. These results are in line with research showing the impact of fertilizers on water quality in regions with high agricultural activity [43] .

PC4, which accounts for 9.35% of the total variance, is influenced by Turb, TDS, Cl⁻, BOD, Mg²⁺, and Fe²⁺. Surface runoff (Turb, TDS) and contamination from pit latrines (BOD) are key factors, while Mg²⁺ and Fe²⁺ suggest mineral dissolution from geological strata. The negative contributions from Ca²⁺ and ${\text{NO}}_3^{-}$ imply less influence from these variables in this component, which may be explained by their absence in areas of higher pit latrine contamination or geological settings less rich in these minerals. Such findings are consistent with studies in regions where sanitation and runoff significantly affect water quality [14] .

PC5 contributes 8.13% of the variance and is dominated by Temp, Sal, ${\text{NO}}_3^{-}$, and Mg²⁺, indicating a combination of agrochemical ( ${\text{NO}}_3^{-}$) and geological influences (Mg²⁺, Sal). The presence of ${\text{NO}}_3^{-}$ suggests contamination from agricultural sources or pit latrines, while the influence of salinity and magnesium reflects interactions with geological formations. The negative contribution of FC implies lower microbial contamination in areas influenced by geological and agrochemical sources, potentially due to deeper groundwater sources being less exposed to surface contamination [41] .

PC6, explaining 5.00% of the variance, is primarily driven by ${\text{NO}}_3^{-}$ and FC, indicating contamination from both agrochemical runoff and pit latrines. The strong presence of nitrates and faecal coliforms in this component suggests areas where agricultural and sanitation practices significantly affect water quality. Other variables have minimal influence, consistent with studies that highlight nitrate and microbial contamination as major issues in regions affected by human and agricultural waste [14] .

The six principal components reflect a complex interplay of surface runoff, geological factors, agrochemical pollution, and sanitation-related contamination. PC1 and PC2 highlight the dominant roles of surface runoff and geological strata, while PC3 and PC5 emphasize the contributions of agrochemicals and sanitation practices. These results align with previous studies, confirming that both natural processes and human activities significantly impact water quality in the study area [42] .

The quality of the water from the WW2 can be sufficiently described by 4 PCs namely PC1, PC2, PC3 and PC4. These PCs contribute 84.61% of the variability of the original results ( Table 13 ). The variances of the PCs are 39.63%, 18.79%, 14.15% and 12.04% for PC1, PC2, PC3 and PC4 respectively. On the other hand, the sum of the square loading (SS Loading) for the PCs are shown and the variance contribute to 84.61% of the variability of the SS loading. The variances of the PCs are 34.27%, 19.84%, 15.4% and 15.09% for PC1, PC2, PC3 and PC4 respectively.

Figure 5 provides the factor scores for the five principal components (PCs) that explain water quality variability. Each component reflects a combination of factors such as surface runoff, geological formations, sanitation, and agrochemical use. The higher the score value of a variable, the greater its contribution to the variability of the corresponding component.

PC1 contributes 36.17% of the variance and is driven by pH, electrical conductivity (EC), temperature (Temp), total dissolved solids (TDS), hardness (Hd), salinity (Sal), chloride (Cl⁻), phosphate ( ${\text{PO}}_4^{3-}$), calcium (Ca²⁺), magnesium (Mg²⁺), faecal coliforms (FC) and total coliforms (TC). This component indicates a blend of influences: Geological strata (pH, Hd, Sal, Cl⁻, Ca²⁺, Mg²⁺). These parameters are typically associated with natural mineral dissolution as groundwater interacts with subsurface formations. Elevated hardness and salinity often reflect regions rich in minerals like limestone or gypsum, contributing to higher concentrations of calcium, magnesium, and salts in water [43] . Surface runoff (TDS, Cl⁻): Runoff from urban or agricultural areas can introduce dissolved solids and chloride, often due to fertilizers. This results in elevated levels of TDS and chloride in surface and shallow groundwater. Sanitation-related contamination (FC, TC). The presence of faecal coliforms and total coliforms suggests contamination from pit latrines or poorly maintained sanitation systems, which can leach into groundwater. This is consistent with studies showing microbial contamination in areas with inadequate waste management [32] . Agrochemical influence ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$). Phosphates and nitrates are commonly associated with fertilizer runoff, highlighting the impact of agricultural activities on water quality. Interestingly, EC and Temp have negative effects on PC1, suggesting that in areas with high contributions from other variables (e.g., TDS and coliforms), conductivity and temperature may have less of an impact. Variables such as coliforms (Col), turbidity (Turb), TSS, alkalinity (Alk), sulphate ( ${\text{SO}}_4^{2-}$), BOD, and Iron (Fe²⁺) do not contribute significantly to PC1.

PC2 explains 22.72% of the total variance and is influenced by Temp, Col, Turb, TSS, Sal, Cl⁻, ${\text{PO}}_4^{3-}$, BOD, Ca²⁺ and Fe²⁺. This component suggests a mix of surface and subsurface influences: Surface runoff (Temp, Col, Turb, TSS): Runoff from urban areas or agricultural lands can introduce suspended solids, turbidity, and coliform bacteria into the water, increasing contamination. High temperature often indicates surface water interaction or shallow groundwater exposed to climatic variations [15] . Geological influence (Sal, Cl⁻, Ca²⁺, Fe²⁺): These variables point to mineral dissolution processes, particularly in groundwater rich in iron and calcium due to the presence of minerals such as calcite or dolomite. Agrochemical contamination ( ${\text{PO}}_4^{3-}$). Elevated phosphate levels suggest fertilizer runoff, indicating agricultural activities as a major influence on water quality. Phosphates in groundwater are often linked to excessive use of chemical fertilizers, leading to nutrient pollution. Variables such as pH, EC, TDS, Hd, Alk, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$, Mg²⁺, FC, and TC show no significant effect, highlighting that PC2 is primarily dominated by runoff and specific geological interactions.

PC3, accounting for 11.17% of the variance, is driven by Temp, TDS, ${\text{PO}}_4^{3-}$, and Mg²⁺, while Col, Sal, ${\text{SO}}_4^{2-}$, and Ca²⁺ show negative effects: Surface runoff (TDS, Temp): This highlights the influence of dissolved solids from runoff, where temperature plays a role in the rate of contamination. Geological strata (Mg²⁺): The presence of magnesium reflects mineral interactions within groundwater aquifers, pointing to natural sources of contamination. Agrochemical inputs ( ${\text{PO}}_4^{3-}$): Again, phosphates are a strong indicator of agricultural runoff, emphasizing the impact of farming activities on groundwater quality. Negative contributions from Col, Sal, ${\text{SO}}_4^{2-}$, and Ca²⁺ may suggest that in regions with high phosphate or magnesium concentrations, these variables are less influential. Other variables such as pH, EC, Turb, TSS, Hd, Alk, ${\text{NO}}_3^{-}$, BOD, Fe²⁺, FC, TC, and Cl⁻ do not show a significant impact, implying a more localized influence of surface runoff and geological features in PC3.

PC4 explains 9.35% of the variance, with positive contributions from EC, Alk, and BOD, and negative contributions from Temp, Col, ${\text{NO}}_3^{-}$, and TC: Geological influence (EC, Alk): The presence of alkalinity and conductivity suggests mineral dissolution, particularly in regions where groundwater interacts with calcareous formations, contributing to higher alkalinity. Pit latrine contamination (BOD): Elevated BOD indicates organic pollution, typically associated with human waste leaching into water sources, often from pit latrines. Negative contributions from temperature, coliform bacteria, and nitrates suggest that microbial contamination may be less impactful in areas dominated by mineral dissolution or deeper groundwater sources. This finding reflects regions where organic pollution from pit latrines is more significant than surface contamination. Variables such as pH, Turb, TSS, TDS, Hd, Sal, Cl⁻, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, Ca²⁺, Mg²⁺, Fe²⁺, and FC do not show significant influence, emphasizing geological and sanitation-related processes as dominant in this component.

The four principal components reveal critical influences on water quality, including surface runoff, geological interactions, agricultural activities, and sanitation-related contamination. PC1 highlights the role of runoff and geology, while PC2 and PC3 emphasize the impact of agrochemical runoff and geological processes. PC4 points to contamination from pit latrines. The analysis underscores the complex interaction between natural processes and human activities in shaping groundwater quality [42] .

The water quality of WW4 can be adequately described by six principal components (PC1-PC6), which together explain 90.83% of the total variability in the original dataset ( Figure 6 ). The variance contributions for each PC are 36.17% for PC1, 22.72% for PC2, 11.17% for PC3, 9.35% for PC4, 8.13% for PC5, and 5.00% for PC6, making them sufficient to represent the underlying water quality patterns. The Sum of Squared Loadings (SS Loadings) for these PCs explain 92.55% of the total variance, with each PC contributing 25.97%, 18.08%, 17.19%, 14.85%, 9.13%, and 7.34%, respectively, indicating a substantial representation of the original data [29] ( Table 14 ).

As shown in Table 12 , PC1 has an eigenvalue of 8.71, accounting for 43.53% of the variance, followed by PC2 at 3.89 (19.44%), PC3 at 2.81 (14.04%), PC4 at 1.73 (8.66%), and PC5 at 1.03 (5.17%). These components cumulatively explain 90.83% of the variance, indicating that the majority of the variability in water quality across WW4 can be captured by these five components [44] .

Figure 6 shows the factor scores for the five PCs. The higher the score value of the variable, the more the variable contributes to the variability of the particular PC. PC1 (43.53% variance). 12 variables contribute positively, including Turb, TDS, Alk, Sal, Cl⁻, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$, BOD, Ca²⁺, Mg²⁺ and FC. This indicates influences from surface runoff (Turb, TDS), pit latrines ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$, BOD, FC), and geological strata (Alk, Sal, Cl⁻, Ca²⁺, Mg²⁺), as well as agrochemical fertilizers ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$). Variables such as pH, EC, Temp, Col, TSS, Hd, Fe²⁺, and TC do not contribute significantly [42] .

PC2 (19.44% variance): Positive contributors include Temp, TDS, Sal, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, Fe²⁺, FC, and TC, while Hd has a negative effect. This suggests that surface runoff (Temp, TDS), pit latrine contamination (FC, TC), and geological factors (Sal, Fe²⁺) are key drivers, along with agrochemical inputs ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$) [36] .

PC3 (14.04% variance): Positive contributions from pH, Col, Turb, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, and ${\text{NO}}_3^{-}$ suggest influences from surface runoff (Col, Turb), pit latrines ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$), geological factors (pH), and agrochemical use. Negative effects from EC and TC indicate areas where these variables are less influential [14] .

PC4 (8.66% variance): Only pH and TSS contribute positively, while Temp and Cl⁻ have negative effects. This suggests the role of surface runoff (TSS) and geological influence (pH), with other variables showing minimal impact.

PC5 (5.17% variance): pH, EC, and ${\text{PO}}_4^{3-}$contribute positively, indicating influences from surface runoff ( ${\text{PO}}_4^{3-}$), geological strata (pH, EC), and agricultural fertilizers. Other variables, including Temp, Col, Turb, TSS, Hd, Sal, and Fe²⁺, have no significant contribution.

The principal component analysis reveals that water quality in WW4 is strongly influenced by surface runoff, geological factors, pit latrine contamination, and agrochemical inputs. The first three components capture the majority of variability, with PC1 highlighting the combined effect of geological and human influences, particularly from sanitation and agricultural practices [36] [45] .

The water quality of WW2 can be effectively described by six principal components (PC1-PC6), accounting for 92.51% of the total variability in the original dataset ( Figure 7 ). The variance contributions of each PC are 42.73% for PC1, 18.26% for PC2, 12.17% for PC3, 8.46% for PC4, 5.71% for PC5, and 5.18% for PC6, making these components sufficient to capture the underlying water quality variation. The Sum of Squared Loadings (SS Loadings) further explain 92.55% of the variance, with contributions of 35.21%, 15.04%, 14.92%, 11.06%, 8.79%, and 7.50% for the respective PCs ( Table 15 ) [29] .

As presented in Table 15 , PC1 has the highest eigenvalue of 8.55, explaining 42.73% of the variance, followed by PC2 at 3.65 (18.26%), PC3 at 2.43 (12.17%), PC4 at 1.69 (8.46%), PC5 at 1.14 (5.71%), and PC6 at 1.04 (5.18%). The cumulative variance explained by these components is 92.51%, indicating that the six PCs sufficiently capture the essential patterns in the water quality data [44] .

PC1 (42.73% variance): 11 variables contribute positively, including pH, turbidity (Turb), total dissolved solids (TDS), hardness (Hd), alkalinity (Alk), salinity (Sal), phosphate ( ${\text{PO}}_4^{3-}$), sulfate ( ${\text{SO}}_4^{2-}$), nitrate ( ${\text{NO}}_3^{-}$), biochemical oxygen demand (BOD), and magnesium (Mg²⁺). Temperature (Temp) has a negative effect. This indicates influences from surface runoff (Turb, TDS), pit latrines ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$, BOD), geological strata (pH, Hd, Alk, Sal), and agrochemical fertilizers ( ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$). EC, Col, TSS, Cl⁻, Ca²⁺, Fe²⁺, FC, and TC show no significant effect [42] .

PC2 (18.26% variance): Positive contributors include TDS, Sal, ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$, Ca²⁺, FC, and TC, suggesting influences from surface runoff (TDS), pit latrines ( ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$, FC, TC), geological strata (Ca²⁺, Sal), and agrochemical fertilizers ( ${\text{PO}}_4^{3-}$, ${\text{SO}}_4^{2-}$, ${\text{NO}}_3^{-}$). Variables such as pH, EC, Temp, Col, Turb, TSS, Hd, Alk, and BOD have no significant effect [41] .

PC3 (12.17% variance): Four variables—Turb, Cl⁻, BOD, and Fe²⁺—contribute positively, while TDS, Sal, ${\text{PO}}_4^{3-}$, ${\text{NO}}_3^{-}$, and TC negatively affect this component. This suggests the influence of surface runoff (Turb), pit latrine connections (BOD), and geological strata (Cl⁻, Fe²⁺) [14] .

PC4 (8.46% variance): Positive contributions from Turb, Col, and Temp suggest the impact of surface runoff (Turb, Col, Temp). Ca²⁺ has a negative effect, with no significant influence from other variables [15] .

PC5 (5.71% variance): EC, Alk, Cl⁻, and ${\text{SO}}_4^{2-}$ contribute positively, while Col and Mg²⁺ have negative effects. This suggests influences from geological strata (EC, Alk, Cl⁻, ${\text{SO}}_4^{2-}$) and surface runoff (Turb, Col) [42] .

PC6 (5.18% variance): Positive contributors are TSS, Ca²⁺, and Mg²⁺, suggesting influences from surface runoff (TSS) and geological strata (Ca²⁺, Mg²⁺). pH negatively affects this component.

The analysis of the six principal components (PCs) highlights the complex interactions among natural and human factors influencing the water quality of WW2. The first three PCs explain the majority of the variance, underscoring the significant role of surface runoff, pit latrines, geological strata, and agro-chemical usage. Surface runoff acts as a key vector for transporting natural sediments and anthropogenic pollutants, while pit latrines contribute to microbial and nutrient contamination, especially in proximity to water sources [46] and [47] . Geological formations regulate water quality through mineral leaching and filtration, mediating the impacts of agricultural and human activities [38] and [48] . Agro-chemical application further exacerbates nutrient loading, contributing to water quality degradation. These findings align with predictive models demonstrating the long-term effects of runoff and leaching on water systems, emphasizing the need for comprehensive land-use and sanitation management [42] . Together, these components reveal the necessity of integrating natural geological characteristics with mitigation strategies to address anthropogenic impacts sustainably.