The study was conducted in the Kilolo district of the Iringa region, situated at the north-eastern extremity, approximately 37 km from the regional administrative center (Mbungu et al., 2021) (Figure 1). This district covers an area of 7874.6 km², hosting a population of 218,130, with geographic coordinates at 70-8030 S and 340-370 E (URT, 2012). Its elevation ranges from 900 to 2,700 m above sea level, predominantly characterized by alluvial soil. Temperatures oscillate between 15 and 27°C, while annual rainfall varies from 500 to 1,600 mm. Kilolo district is already home to a diverse array of small- and large-scale agricultural and forest plantations (PFP, 2017). The region primarily cultivates maize, legumes, fruits, and vegetables, contributing significantly to the nation’s breadbasket (URT, 2012).

The research used a cross-sectional research design, enabling the collection of data at a given time from a selected sample intended to accurately represent the entire population.

Data were collected in the field through direct observation, household questionnaires, and a physical–chemical analysis of water parameters. The water parameter data were collected during the month of February 2023, and household questionnaires and direct observation were conducted during the month of May 2023.

The data were statistically analyzed using the Statistical Package for Social Sciences (SPSS), version 20. A one-sample t-test was used to measure the significance of the difference between the measured water quality parameter and the water quality criteria established by the WHO (2017) and the TBS (2006), with significance determined at a level of p < 0.05. Microsoft Excel software was used for additional data analysis.

In order to obtain a comprehensive picture of the entire surface water quality within the catchment, the water quality index (WQI) was used to determine the overall water quality status of the catchment. The WQI is used to simplify and convey scientific water quality information by combining the influence of various water quality parameters into a single-digit score that describes the overall water quality in a watershed (Koleka et al., 2023). Policymakers and the general public can obtain scientifically based information on river quality status through the index, which provides meaningful and comprehensible information on water quality (Wong et al., 2021). The weighted arithmetic index method was used to calculate the WQI. The method categorizes water quality based on purity levels by analyzing commonly measured water quality variables (Kizar, 2018; Paun et al., 2016). This approach has been extensively utilized by researchers (Singh et al., 2013). The obtained results were assigned to the classification of water quality based on the calculated WQI (Table 2).

Step 1: Calculation of the unit weight (Wn) factors for each parameter by using the formula:

where:

Sn = Standard desirable value of the nth parameters.

On summation of all selected parameters unit weight factors, WN = 1 (unity).

Step 2: Calculation of the sub-index (QN) value by using the formula.

where:

Vn = Mean concentration of the nth parameters.

Sn = Standard desirable value of the nth parameter.

Vo = Actual value of the parameters in pure water (generally Vo = 0, for most parameters except for pH and DO).

Step 3: Combine steps 1 and 2. The WQI was calculated as follows:

The identified human activities along the LRC were gardening, agriculture, livestock keeping, sand mining, brick-making, car washing, and fishing (Figures 3, 4). These results were consistent with Munishi et al. (2011), who found that agricultural production was practiced by over 98% of the population in the GRR basin, followed by livestock grazing and fishing. Agriculture involves the large-scale cultivation of crops and livestock for food, fiber, and other products, typically involving mechanized processes and often conducted for commercial purposes. In contrast, gardening is the cultivation of plants, typically on a smaller scale, for personal purposes. Farmers in the LRC heavily relied on inorganic fertilizers, pesticides, and irrigation to enhance their crop yields. The majority of respondents reported using inorganic fertilizers (95% in agriculture and 86% in gardening) and pesticides (91% in agriculture, 72% in gardening) (Tables 3, 4), highlighting the extensive use of these chemicals along the LRC. The chi-square test results for both agriculture and gardening activities indicated highly statistically significant associations between these activities and the use of inorganic fertilizers and pesticides (p = 0.000***). The pesticide and inorganic fertilizer applications on crops contributed to chemical deposits in the soil, which are carried through runoff and leaching into rivers, lakes, and groundwater; these bring about adverse effects to the living organisms and the environment in general (Massawe and Nyoki, 2019). Moreover, irrigation practices, which were adopted by 90% of agricultural operations and 83% of gardening activities (Table 5), were aligned well with the observations made during data collection; both pumping and scheme-based irrigation methods were observed in the field (Figure 5). Due to these practices, the water supply from the Lukosi River to the GRR either ceases or becomes minimal during the dry season, leading to the complete drying up of the GRR during the dry season over the past 10 years (Cour et al., 2005). This dryness had implications for the livelihood of the people, the economy, and significant biodiversity.

Livestock keeping was also identified along the LRC, and this activity can cause different environmental challenges such as water pollution, habitat degradation, and biodiversity loss. Similar to our findings, Kumar et al. (2013) emphasized the environmental challenges posed by livestock keeping, such as water pollution and habitat degradation due to manure runoff, resulting in eutrophication and harmful algal blooms. George et al. (2004) also reported that unrestricted livestock access to rivers results in direct deposition of waste, introducing harmful pathogens, nutrients, and sedimentation, adversely impacting water quality, and aquatic health (George et al., 2004). Additionally, overgrazing practices led to soil erosion, compaction, and habitat degradation, diminishing ecosystem resilience and biodiversity (Matano et al., 2015).

Sand mining and brick-making activities were also identified along the LRC, and these activities may cause riverbank erosion, alteration of flow, and disturbance to aquatic organisms. Similar to our findings, Dufour et al. (2018) and Raphael and Makarius (2022) highlighted the environmental impacts of these activities, such as riverbank erosion, alteration of flow, and disturbance to aquatic organisms.

Car washing and fishing, though less identified, also can contribute to environmental harm through chemical use and wastewater generation. Our study identified car washing as a contributor to water pollution, which aligns with Powell et al. (2011), who noted the presence of pollutants such as petroleum hydrocarbons and heavy metals in car wash effluents.

pH serves as a critical indicator of the acidic or basic character of a solution, determined by the chemical compounds and biochemical reactions within it. In aquatic environments, pH levels hold paramount importance in governing physiological functions. These encompass vital aspects such as enzyme activity, metabolic rates, and ion exchange. Hence, maintaining an optimal pH range is crucial for ensuring the overall health and well-being of aquatic life forms. The measured mean pH values were 7.76 ± 0.006 for Ls1, 7.47 ± 0.01 for Ls2, and 7.47 ± 0.006 for Ls3. The pH values observed at all three sampling sites consistently fall within the acceptable range as recommended by both TBS and WHO standards (Table 6). This alignment indicates favorable conditions for the majority of aquatic organisms. The pH of an aquatic system is an important indicator of the water quality and the extent of pollution in the watershed areas. This finding was consistent with studies by Tembo (2009) and Turner and Chislock (2010) that emphasized the importance of pH in aquatic environments.

An increase in temperature brought on by climate change could alter the composition of species and lead to the extinction of endemic species (Carr and Rickwood, 2008). According to David et al. (2017), a change in temperature can alter the chemical properties of a wide range of parameters. Numerous studies (Koch et al., 2013; Pansch and Hiebenthal, 2019) reported that temperature variations were the most frequent and potent driver of changes in marine systems. The temperature measurements for Ls1, Ls2, and Ls3 were recorded as 22.47 ± 0.058°C, 22.73 ± 0.058°C, and 22.77 ± 0.12°C, respectively. The mean water temperature of the Lukosi River was within the acceptable range for aquatic life according to both TBS and WHO standards (Table 6). This indicates that the river’s temperature is suitable for supporting aquatic organisms. These findings are consistent with previous research, which reported temperature ranges of 20.5–22°C (Chiroma et al., 2012) and 19.01–23.93°C (Okweye, 2013).

Dissolved oxygen serves as a regulator of organisms’ metabolic processes, which in turn controls the metabolism of the entire biological community (Khan et al., 2012). Due to the fact that DO is essential for respiration, enough DO levels are absolutely necessary for the survival of aquatic species. The DO levels in the water samples collected from Ls1, Ls2, and Ls3 locations exhibited noticeable variations. The measured mean DO values were as follows: 3.57 ± 0.06 mg/L for Ls1, 2.61 ± 0.41 mg/L for Ls2, and 1.81 ± 0.25 mg/L for Ls3. The highest DO levels were observed in the Ls1 area, while the lowest DO levels were recorded in the Ls3 area. The obtained Do levels in all three sampling sites were found to be below the recommended limits set by both TBS and WHO, as indicated in Table 6. Our study observed DO levels below recommended limits, posing challenges for aquatic organisms. This aligns with findings by Mugidde et al. (2005) and Addo et al. (2013), who reported that low DO levels lead to hypoxia, endangering aquatic life and disrupting ecosystem dynamics. This shows that aquatic organisms may face difficulties surviving in the Lukosi River due to lower oxygen levels. The obtained DO level was due to human activities such as agriculture, livestock, and gardening activities, primarily occurring in the midstream and downstream areas of the river. In addition, according to Aiyesanmi et al. (2006), organic wastes and other nutrient inputs from industrial and sewage discharges, agricultural runoff, and urban runoff might also cause lower oxygen levels. Thus, the DO values measured in the Lukosi River were not suitable for the life of the aquatic organisms. Aquatic life is negatively impacted by DO concentrations below 5.0 mg/L (Sinha and Biswas, 2011).

The term “BOD” refers to the quantity of oxygen that bacteria need in order to decompose the degradable organic matter in water into simpler compounds (Suthar et al., 2010). Since it indicates the level of pollution, BOD is a crucial indicator for the aquatic ecosystem (Mbaruku, 2016). From the Lukosi River, the BOD levels showed variations across the sampling sites (Ls1, Ls2, and Ls3). In Ls1, a low BOD value of 5.33 ± 0.58 mg/L was observed, indicating minimal organic pollution. In Ls2, the BOD levels rose to 9.0 ± 1.0 mg/L, suggesting a moderate level of organic matter. LS3 had the highest BOD value of 15.33 ± 0.57 mg/L, indicating a high level of pollution potentially impacting water quality. The obtained values of Ls1 and Ls2 fall within the recommended values set by the WHO and TBS, while the values of Ls3 appear to be above the recommended value set by the WHO and TBS (Table 6). The high BOD value at Ls3 may be caused by agricultural operations, surface runoff, livestock keeping, and underground water movement containing leachates from a solid waste landfill located near the Lukosi River, all of which could exacerbate organic pollution (Al-badaii et al., 2013). A higher BOD indicates a more rapid depletion of oxygen in the water body, as microorganisms consume DO. Similar results were found in previous studies, which demonstrated that a high BOD level lowers the amount of DO in the river (Omary et al., 2023).

Total dissolved solid is an indicator of the salinity behavior of water (Dahiya and Kaur, 1999). The TDS measurements were conducted and resulted in an average mean value of 50.02 ± 0.02 mg/L for Ls1, 45.3 ± 0.2 mg/L for Ls2, and 45.37 ± 0.32 mg/L for Ls3. All these values were found to be lower than the permissible limits stipulated by both TBS and WHO (Table 6), which appear to be unsuitable conditions for aquatic organisms. According to Mbaruku (2016), TDS measures the capacity of water to dissolve various inorganic and certain organic minerals or salts, such as sulfates, magnesium, chlorides, bicarbonate, sodium, calcium, and potassium. Furthermore, in accordance with Mezgebu et al. (2019), elevated TDS concentrations hinder algal productivity and growth, serving as an indicator of poor water quality.

Electric conductivity is a measurement of the ability of water to carry electric current. It denotes the quantity of all dissolved salts in general. Conductivity measures the overall ionic content of water, providing an indication of whether the water is fresh or not. According to Olatayo (2014), the conductivity of water is directly related to its overall salt content. Calcium ion (Ca2+), magnesium (Mg 2+), hydrogen trioxocarbonate (HCO 3), trioxocarbonate (iv) (CO 3), trioxonitrate (v) (NO 3), and tetraoxophosphate (vi) (PO 4) are the main components of the dissolved chemicals in water (Olatayo, 2014). The measured EC values were 10.69 ± 0.39 μS/cm for Ls1, 13.69 ± 0.49 μS/cm for Ls2, and 13.85 ± 0.59 μS/cm for Ls3. The recorded EC values from all three sampling sites were below the acceptable range established by the WHO standards (Table 6), suggesting unfavorable conditions for the growth and well-being of aquatic species.

Turbidity, particularly in water, is a crucial factor in determining the quality of the water. The mean measured turbidity values from the Lukosi River were 43.33 ± 1.04 NTU for Ls1, 102.67 ± 1.52 NTU for Ls2, and 185.33 ± 1.53 NTU for Ls3. The turbidity values in all three sampling sites were higher than the recommended limits set by both WHO and TBS standards (Table 6). This suggests that the river as a whole is generally contaminated and endangering aquatic life. This may be due to the daily human activity such as washing of different vehicles, surface runoff, and other activities (Alemayehu, 2001). Aquatic species' behavior and feeding habits may be affected by this rise in turbidity, which may alter light penetration and visibility. Similar findings were reported by Joshi et al. (2009) and Murhekar (2011), who highlighted the impact of high turbidity on aquatic species’ behavior and visibility.

Total hardness is a property of water that hinders lather formation with soap and increases the boiling point of water (Mezgebe et al., 2015). It primarily depends on the presence of Ca or Mg salts, or both (Trivedy and Goel, 1986). Water is divided into three groups according to its hardness: soft water (0–75 mg/L), moderately hard water (76–150 mg/L), and hard water (151–300 mg/L). The measured total hardness values for Ls1, Ls2, and Ls3 were found to be 28.8 ± 0.1, 28.5 ± 0.5, and 32.31 ± 0.25, respectively. In all sampling sites, total hardness was found to be below the acceptable range recommended by the TBS (Table 6). The hardness values recorded for this study were within the soft classification. Standard total hardness levels are essential for the health and survival of aquatic organisms, as they provide the necessary minerals for their physiological processes. Other studies on river water similarly found lower values for total hardness (Joshi et al., 2009; Sharma et al., 2013), indicating that the water is classified as soft and suitable for aquatic organisms.

Ca and Mg are prevalent components found in natural water sources, and their respective salts significantly contribute to water hardness. In the present study, Ca concentrations were measured with average mean values of 8.12 ± 0.16 mg/L for Ls1, 7.44 ± 0.22 mg/L for Ls2, and 8.22 ± 0.18 mg/L for Ls3. Conversely, Mg levels were measured, resulting in average mean values of 2.06 ± 0.056 mg/L for Ls1, 1.73 ± 0.065 mg/L for Ls2, and 1.97 ± 0.075 mg/L for Ls3. The recorded values for both Ca and Mg were below the permissible limits set by the TBS. Although Ca and Mg are essential minerals for the development and health of aquatic organisms, the recorded levels were not suitable for their well-being.

NO₃ plays a crucial role as a vital nutrient in aquatic ecosystems, promoting plant growth, while simultaneously acting as a limiting factor for algal growth (Bwalya, 2015). Nitrogen-containing elements are essential for all biotic processes in the aquatic environment (Bwalya, 2015). The recorded average mean nitrate values were 12.28 ± 0.64 mg/L for Ls1, 47.69 ± 0.3 mg/L for Ls2, and 79.04 ± 0.73 mg/L for LS3. The LS1 and Ls2 sites were within the recommended range set by both WHO and TBS; however, at the Ls3 site, the levels exceeded the limits recommended by both TBS and WHO (Table 6). According to Mwangi (2014), human activities taking place along the river cause an increase in nitrate levels. Water bodies become contaminated when runoff from agricultural activity introduces fertilizers to the watercourses during heavy rainfall. The increase in nitrate leads to excessive algal growth, which reduces oxygen levels as it decomposes and affects certain aquatic organisms that cannot withstand anaerobic conditions (Mwangi, 2014).

NH₃ and SO₄: NH3 levels were measured at 0.67 ± 0.32 mg/L for Ls1, 4.24 ± 0.68 mg/L for Ls2, and 5.32 ± 0.095 mg/L for Ls3. Ls1 falls below the TBS recommended range, whereas Ls2 and Ls3 exceed it (Table 6). The elevated levels of ammonia in Ls2 and Ls3 may be attributed to various human activities occurring in those regions. For instance, the increased use of fertilizers in agricultural practices within Ls2 and Ls3 could be a significant contributor to the higher ammonia levels. Monitoring and appropriately managing elevated ammonia levels is crucial, as they can pose toxicity risks to aquatic organisms. On the other hand, the sulfate (SO4) concentrations were determined to be 14.41 ± 0.32 mg/L for Ls1, 18.27 ± 0.58 mg/L for Ls2, and 28.32 ± 1.04 mg/L for Ls3. All of these measurements fall below the recommended range established by both TBS and WHO. Sulfate plays a crucial role in water chemistry, and the recorded levels were below the acceptable limit for sustaining aquatic life.

The presence of high levels of Cl in river waters may be a sign of sewage or industrial waste pollution or the entry of saltwater into freshwater systems. The Cl concentrations were measured, resulting in an average value of 0.22 ± 0.096 mg/L for Ls1, 8.53 ± 0.56 mg/L for Ls2, and 13.32 ± 0.39 mg/L for Ls3. All of these measurements fall below the allowable limit stipulated by both TBS and WHO (Table 6). The reported amounts of chloride were below acceptable ranges for the health of aquatic life, which depends on it as an important electrolyte.

Fe, F, and Mn: Fe concentrations were measured at 1.59 ± 0.16 mg/L for Ls1, 2.0 ± 0.11 mg/L for Ls2, and 3.56 ± 0.15 mg/L for Ls3. Ls1 and Ls2 values were within the TBS and WHO recommended range, but Ls3 exceeded the limit. Fe is an essential micronutrient for aquatic organisms. The elevated levels observed were likely due to soil erosion, which was exacerbated by human activities near the river, leading to bare soil and subsequent erosion of the riverbanks. It is important to assess the level of Fe to ensure it remains within the acceptable standards for the health and suitability of aquatic life. F levels were assessed, yielding mean values of 0.15 ± 0.053 mg/L for Ls1, 0.13 ± 0.01 mg/L for Ls2, and 0.19 ± 0.102 mg/L for Ls3. These measurements fall below the range recommended by both TBS and WHO. The Mn concentrations measured in Ls1, Ls2, and Ls3 were found to be 0.059 ± 0.011 mg/L, 0.133 ± 0.004 mg/L, and 0.95 ± 0.32 mg/L, respectively. These values fall within the acceptable range recommended by TBS. Mn, an essential trace element for aquatic organisms, supports their well-being, and the recorded levels provide the necessary support.

Cu and Zn: Cu levels were measured at 0.073 ± 0.07 mg/L for Ls1, 5.01 ± 0.53 mg/L for Ls2, and 6.65 ± 0.48 mg/L for Ls3. LS1 falls within the recommended TBS range, while Ls2 and LS3 exceed the recommended limit. Elevated Cu levels in aquatic environments can pose a significant threat to the health and well-being of aquatic organisms. The obtained results primarily come from agricultural runoff, especially due to the use of inorganic fertilizer, pesticide applications, and residential waste. Therefore, monitoring and effective management strategies are strongly recommended to mitigate potential harm. The Zn concentrations in all three areas (0.14 ± 0.19 mg/L for Ls1, 0.026 ± 0.038 mg/L for Ls2, and 0.036 ± 0.025 mg/L for Ls3) were below the acceptable range defined by the TBS. The obtained Zn levels were not suitable for aquatic species, where it is needed for a number of physiological processes such as enzyme activity, growth, and reproduction (Nogawa et al., 2004).

The calculated WQI was used to assess the overall water quality status of the entire catchment. The results revealed a WQI of 300.3252 for Ls1, 405.7578 for Ls2, and 763.4208 for Ls3 (Tables 7–9). These values indicate that the water in the catchment was unsuitable for sustaining aquatic organisms and was unfit for domestic use (Table 2).