Low-, middle- and high-income countries, exhibit indications of risks associated with water quality. The study investigated heavy metal concentrations in surface water and sediments within the Ngerengere River and its tributaries (Kikundi, Bigwa, and Morogoro) drain within in the Morogoro Municipality of Tanzania, an Urban Catchment Area (UCA) with limited available data mainly because of inadequate monitoring and reporting capabilities. Analysis of health and ecological risks associated with heavy metal pollution was also carried out using health risk assessments models, pollution indices, and multivariate analysis techniques. Between the dry and wet seasons of 2023, water and sediment samples from (13) sampling stations strategically established along the Ngerengere river and its tributaries were analyzed for six heavy metals (Pb, Cr, Ni, Cd, Cu and Zn) using the Atomic Absorption Spectrophotometer Model Perking Elmer 850 Graphite Furnace and Perking Elmer AS 800 Auto-sampler coupled with a computer interface for operational, displaying and reading the results. The calculated degree of water contamination (Cd) values in river water in both dry and wet seasons ranged from 0 to 6.803 indicating low and high degrees of contamination respectively. Heavy metal concentration in sediment decreases in the order of Zn>Ni>Cr>Cu>Cd>Pb. The non-cancer risk index (HI) via ingestion and dermal pathways in dry and wet seasons for both children and adult groups was <1 hence no non-cancer risk, However, cumulative dermal and ingestion exposure in both children and adults indicated potential cancer risk in dry and wet season. The analysis of ecological risks associated with heavy metal enrichment in the sediment indicated high enrichment of sediments with Cd, Ni and Zn. Conclusively, in wet months, risk indices tend to be low, while in dry months, they typically remain high.

Keywords: heavy metal, sediment, health risk, ecological risk, Ngerengere river, tributaries

CFs, contamination factors; Cd, degree of water contamination; EDI, estimated daily intake; EFs, enrichment factors; ERI, ecological risk index; HCA, hierarchical cluster analysis; Igeo, geoaccumulation index; NI, nemerow index; PCA, principal component analysis; PLIs, pollution load indices; THQ, target hazard quotient; UCA, urban catchment area; USEPA, the United States environmental protection agency; WHO, World Health Organization

Life relies on water, an indispensable element. Of the Earth's overall water content, only 3% is freshwater.¹ A mere 0.01% of this freshwater is accessible for human consumption. Freshwater sources such as rivers play a crucial role in the terrestrial ecosystem, offering abundant water resources essential for the sustainable development of human society and ecological environments.^2,3 Ensuring access to safe and sufficient drinking water stands as a focal point in the development objectives of any nation, however, the acceleration of industrialization and urbanization, driven by human activities that disregard natural sources have resulted in pollution of rivers marked by elevated levels of potential toxic elements such as heavy metals.⁴ Pollution of heavy metals in river systems poses a significant threat to both aquatic ecosystems and human communities. This is primarily due to the prevalence, long-lasting nature, inherent toxicity, inability to degrade, widespread distribution, accumulation in organisms, and amplification through the food chain.^5–7

In the field of environmental studies, the term "heavy metals" refers to metals and metalloids characterized by a high atomic weight and a specific gravity five times greater than that of water at 4 °C. Hence, heavy metals are categorized into essential ones such as Zinc (Zn), Copper (Cu), Iron (Fe), Manganese (Mn), etc., and non-essential or toxic heavy metals including Arsenic (As), Cadmium (Cd), Lead (Pb), Mercury (Hg), etc. The toxic heavy metals pose significant harm even at low concentrations when consumed over an extended period.⁸ The increased contamination of water from heavy metals directly jeopardizes human health.^9,10 Previous research indicates that individuals, both adults and children, exposed to heavy metals (such as Cu, Zn, Cd, Pb, and Cr) through direct ingestion and dermal absorption, often face health risks, including but not limited to cancerous and non-cancerous, neurological disorders, and intellectual disability.¹¹

While natural sources like rock weathering, soil erosion, atmospheric deposition, and microbial degradation contribute less to heavy metals in rivers, anthropogenic sources, including municipal discharge, agricultural fertilizer, and industrial pollution, are recognized as the primary sources for heavy metal pollution in rivers.¹² In recent decades, heavy metal pollution in rivers has been a prominent focus of research globally, with extensive work conducted on aspects such as the form distribution, migration, release and enrichment, pollution, and risk assessments of heavy metals in river sediments.^13–15 The insights from these studies hold theoretical value in understanding the geochemical cycle of heavy metals in rivers and practical significance in guiding efforts for river pollution control.

On the other hand, the sediment, as a natural component of the aquatic ecosystem, functions as a reservoir for various pollutants.¹⁶ Therefore, the anthropogenic impact leading to an excessive presence of heavy metal loads in the sediment can pose a threat to water supplies and induce changes in environmental conditions. This consideration is crucial, especially since most rivers in developing countries serve as the primary water source. Furthermore, heavy metal contamination in sediments has noteworthy implications for benthic organisms, biota, and water quality, particularly for numerous invertebrates that rely on sediments as a food source. Given the potential bioaccumulation of heavy metals in invertebrate organisms, there is a risk of these metals subsequently entering other components of the trophic chain.¹⁷ Other studies have also established the ecological risks associated with heavy metals in sediment.^18,19 Sediment serves as a suitable indicator of the health of riverine ecosystems because of its crucial function in transporting and storing pollutants, as well as its ability to release them into the water.¹⁸

The Ngerengere River drains within the Morogoro Municipality in Tanzania serves as a crucial freshwater source for urban areas in Morogoro. In this particular area, there's only one dam, the Mindu dam, which plays a vital role as a primary freshwater reservoir for urban regions in Morogoro. It sustains a range of activities including farming and fishing.²⁰ Flowing through a network of main streams and tributaries, known as the Ngerengere-Morogoro River, it traverses diverse landscapes including forest lands, urban residential areas, and farmlands. As it progresses downstream, the Morogoro River, the principal tributary, courses through densely populated residential areas.²¹ The primary contributors to heavy metal pollution in the Ngerengere River are traced back to municipal waste, sewage discharge, pesticide and fertilizer usage, the combustion of fossil fuels, artisanal and small-scale mining, as well as industrial effluents.²²

To date, numerous approaches have been devised for evaluating the health and ecological dangers posed by heavy metals in water bodies.^23,24 The most common approach that has been used recently to assess the health and ecological risks of heavy metals in water and sediments is the use of pollution indices.^25–28 Health Risk Assessment (HRA) indices have been used instead of clinical and epidemiological studies due to economic implications and their ability to estimate and quantify the risk of human exposure to certain pollutants by both deterministic and probabilistic methods.²⁹ The use of sediment pollution indices against the traditional method of comparing the concentrations of heavy metals against the maximum allowable concentration³⁰ was due to their ability to combine pollution risk to ecological systems^27,31 and accounting the influence of anthropogenic activities in sediment pollution.³²

Despite the wide application of pollution indices to study the health and ecological toxicity of heavy metal contamination in the river water and sediment across the globe, there are scarce studies that have been done in Tanzania particularly the Urban catchment of Ngerengere River characterized by an array of point and non-point sources of pollution. Before this study, previous studies in the Ngerengere River catchment have been undertaken to identify heavy metal concentrations present in the waters, sediment and aquatic organisms,^22,33 however, these studies did not establish health and ecological risk assessments associated with heavy metals pollution in the river water and sediment; Furthermore the studies did not adequately take into consideration the influence of tributaries (Kikundi, Bigwa and Morogoro) to the transportation and deposition of heavy metals in the Ngerengere River water and sediments.

This paper offers scientific novelty by concentrating on an area where quality indices for heavy metal pollution have not been previously calculated, despite the presence of numerous pollution sources. The study utilized pollution indices to assess the human health and ecological risks associated with heavy metal (Pb, Cr, Ni, Cd, Cu and Zn) in the water and sediment of Ngerengere River and its tributaries drains within the Morogoro Municipality. To achieve this goal, various specific pollution indices crucial for evaluating the quality of river water and sediments were employed and tested, namely the degree of water contamination (Cd), geo–accumulation index (Igeo), contamination factor (CF), Pollution Load Index (PLI), Nemerow Index, Enrichment Factor and the Potential Ecological Risk Index (RI). The scientific novelty of this paper lies in its focus on the study area, where these quality indices have not been previously calculated, despite the presence of numerous sources of heavy metal pollution.

Therefore, the objectives of this study were (1) To assess and quantify the extent of heavy metal pollution in both the river water and surface sediments of the Ngerengere River in dry and wet seasons, influenced by both point and non-point sources. (2) To evaluate the potential health and ecological risks associated with heavy metal pollution. The study hypothesized that the levels of heavy metal contamination in the river water and surface sediments of the Ngerengere are influenced by both point and non-point sources with significant variability in dry and wet seasons. It further hypothesized that heavy metals contamination in River water and sediment poses potential health and ecological risks to human and aquatic organisms. The findings of this study offer a valuable understanding of the levels of human health and ecological risks associated with heavy metal pollution in the Urban catchment of Ngerengere River. Findings and recommendations from this study provided baseline information regarding health and ecological risk management aimed at mitigating anthropogenic pollution of urban rivers and safeguarding public health and the life aquatic organisms.

Description of the Study area

The Ngerengere River Catchment (NRC) area is positioned centrally within the Wami-Ruvu sub-basin, located between approximately 6° 30′ 00″ and 7° 10′ 00″ South latitude, and 37° 58′ 26″ and 38° 31′ 30″ East longitude, covering an area of approximately 2780 square kilometers. Originating from the Uluguru mountains, this river extends across a significant portion of the Morogoro region, encompassing both the Morogoro Urban District and parts of the Morogoro Rural District.³⁴ Within this catchment, the Mindu dam stands as the sole dam, playing a critical role in providing freshwater to urban areas in Morogoro, supporting various activities such as agriculture and fishing. Eventually, it merges with the lower Ruvu River, contributing to its flow towards the Indian Ocean. This study focuses on the segment of the Ngerengere River and its three tributaries—Bigwa, Kikundi, and Morogoro—within Morogoro Municipality, aligning with specific research objectives. The urban center of Morogoro Municipality, inhabited by over 471,409 individuals, is the point where the Ngerengere River drains.

The current state of these water sources is characterized by heightened levels of pollution, originating from household waste, sewage, industrial effluents, agricultural practices, and fishing activities.³⁵ Additionally, the Ngerengere River has been integrated into the urbanized area due to rapid urban expansion witnessed in recent decades. Furthermore, the depletion of vegetation along the riverbanks is emerging as a pressing issue, disrupting the river's ecological balance.³⁶ Nevertheless, inhabitants, particularly those residing in the Morogoro Municipality communities along the river, depend on the river water and nearby natural wells for their daily necessities.

Situated within the tropical climate belt, the catchment experiences two primary rainy seasons. Annual precipitation across much of the catchment ranges from 800 to 1,000 mm per year, while in the Uluguru Mountains, it exceeds 1,500 mm per year.³⁷ During sampling periods, the average monthly temperature, rainfall and evaporation in the dry and wet seasons were 24.75⁰C, 2.3 mm, 161. 1 mm and 27.1⁰C, 117.3, 158.1mm respectively. These climatic fluctuations potentially affecting water quality particularly transport and deposition of heavy metals in water and sediment. The local geology of Morogoro town comprises the Usagaran unit, a Precambrian basement complex featuring high-grade metamorphic rocks like amphibolite, gneiss, and granulites. Furthermore, the area is characterized by Neogene formation containing a substantial accumulation of red soil which is also known as "mbuga" soil, and alluvium, dominant soil textural classes are silt clay and loamy sand.³⁸

Sampling and analysis

A total of thirteen (13) water and sediment samples from the main river (Ngerengere) and three tributaries namely (Morogoro, Bigwa and Kikundi) were collected in the dry and wet periods of the months of September and December 2023, respectively. Locations of sample collection (Figure 1 and Table 1) were geo-referenced through a handheld global GPS unit (Map 62, Garmin), and relevant observations to describe sampling location characteristics were recorded onsite.

Figure 1 A map of Ngerengere River and its three tributaries flowing within the Morogoro Municipality showing sampling locations for heavy metal analysis.

Collected samples of water were sampled in 1000 ml polyethylene bottles previously rinsed with water from the river followed by acidification with nitric acid before being stored at 4°C cooling temperature for the subsequent laboratory analysis. The goal of acidification is to avoid complexation processes between certain ions and adsorption/desorption from colloids or other biochemical reactions. The detailed description of this method has been also reported in other previous studies.^13,39 Sediment samples were collected by stainless scoops at 0-10 cm depth and stored in polyethylene bags while hand-operated manual augers were used in high-velocity areas, The collected samples were collected into polyethylene bags and kept in ice-cooled container maintained at 4°C to avoid cross-contamination. No further chemical pretreatments were done rather than laboratory digestion of sediment samples using standardized procedures.⁴⁰

All the samples were analyzed for the contents of Fe, Cu, Zn, Cr, Cd, Pb and Ni using Atomic Absorption Spectrophotometry (AAS) at the Chemistry laboratory in Sokoine University of Agriculture and Ardhi University - environmental engineering laboratory, the AAS model Perking Elmer 850 Graphite Furnace and Perking Elmer AS 800 Auto-sampler with a computer interface for operational, reading and displaying the results. The reagents used included distilled water, aqua regia 1:3 by volume (1 concentrated HCl: 3 concentrated HNO₃ (65-68%) and Sulphuric Acid (H₂SO₄) for digestion and extraction. The detection limit of the instrument was set to 0.01 mg/l, while the accuracy for each experimental run was above 98%.

The concentrations of heavy metals in water were expressed in mg/L while in the sediment the heavy metal concentrations were exposed in mg/kg and compared against the acceptable thresholds for heavy metals in drinking water established by various organizations such as the Tanzania Bureau of Standards, United States Environmental Protection Agency (USEPA), World Health Organization, WHO (Table 2). The use of these standards to benchmark the comparison of water quality with international and national standards was also observed in the previous study.⁴¹ To date, there are no Tanzania standards on the sediment quality hence literature sources were consulted for benchmarking the discussion and comparing the obtained sediment quality with previous studies.⁴²

Health risk assessment and study area population

According to the Tanzania census of 2022, the population of Morogoro Municipality was 471,409 residents translates to a population density of approximately 1,814.65 persons per square kilometre. The total number of households in the study area is 133,809 while the average household size of 3 individuals.

Health risk assessment was based on the carcinogenic and non-carcinogenic heavy metals. The Reference Doses (RfD) and slope factors (SF) values for non-carcinogens and carcinogens respectively have been obtained from various sources including the US EPA toxicological database. For risk assessment purposes Reference Dose (RfD) and Reference Concentration (RfC) which are protective even for the most sensitive groups of the population were determined.

Non-carcinogenic risks of exposure to heavy metals

The risks of non-cancer related to heavy metals exposure were estimated by calculating the risk quotient adopted from US EPA. Heavy metal concentrations inform exposure models for calculating ingestion and dermal pathways. An additional non-carcinogenic hazard index, dividing calculated lifetime daily exposure by reference dose, was derived. Health risk assessments employed parameters recommended by the US EPA, including estimated daily intake (EDI) and target hazard quotient (THQ). A hazard index below 1 indicates no health risk. For carcinogenic risks, daily intake (mg/kg/day) multiplied by the slope factor determines risk levels, with distinctions between adults and children due to children's higher sensitivity to heavy metals. These two indices have been also used in previous studies^24,29,43,44 to assess the impact of heavy metals on the health of populations.

$HQ ing/derm=\frac{\text{D} ing/derm }{\text{RfD} ing/derm}$   (1)

Wherethe risk quotient per ingestion or dermal contact. represents the reference dose by ingestion or dermal contact and is expressed in mg/kg/d and is the exposure dose by ingestion or dermal contact expressed in mg/kg/d and calculated according to equations 2 and 3 adopted from US EPA and in other scientific research.³⁰ An HQ below 1 is deemed safe and denotes significant non-carcinogenicity. However, if the HQ exceeds 1, it suggests a potential health hazard for individuals exposed to the contaminant at those levels.

$\text{D} ing=\frac{Cw\times IR\times EF\times ED}{Bw\times AT}$   (2)

$\text{D}derm=\frac{Cw\times SA\times KP\times EF\times ED\times ET}{BW\times AT}$   (3)

$\text{HI}=\Sigma HQ$   (4)

In this context, Ding represents the intake dosage via water consumption (in μg/kg/day), Dderm signifies the intake dosage through skin absorption (in μg/kg/day), and Cw denotes the recorded metal concentration in water (in μg/L). IR stands for the rate of water intake per unit time (in L/day), estimated at 2.2 L/day for adults and 1.8 L/day for children. EF refers to the frequency of exposure (350 days/year), while ED represents the duration of exposure (70 years for adults and 6 years for children). BW signifies the average body weight (70 kg for adults and 15 kg for children). AT represents the average lifespan, calculated as 66 years multiplied by 365 days, resulting in 25,550 days for children, and for adults, the average exposure duration is 24,090 days. SA stands for the area of exposed skin (18,000 cm²), ET is the duration of exposure (0.58 hours/day), CF represents the conversion factor (0.001 L/cm³), and Kp indicates the coefficient of dermal permeability (in cm/h). Table 3 and Table 4 visualize in summary, the input parameters used in this study.

Carcinogenic risks of exposure

The risks of cancer are estimated from the Excess Life Cancer Risks (ELCR) and the risk index (RI) according to equations 5 and 6.

$\text{ELCR}=D \times SF$   (5)

Where SF represents the slope factor of each selected pollutant.

$\text{RI}=\Sigma ELCR$   (6)

To avoid overestimating risk, the analysis did not rely solely on the maximum concentration, which might occur only once. Instead, it considered mean and minimum concentrations, which are potentially more representative, alongside the maximum concentration in risk assessment.^{Top of Form47}

Water quality based on the degree of contamination (Cd)

The degree of contamination of water by heavy metals is given by the following formula.¹⁵

$\text{Cd}={\displaystyle \sum _i^N\text{FCi}}$   (7)

$\text{FCi}=C_{Ai}/C_{Ni}$   (8)

Cd represents the degree of metallic contamination. FC, the contamination factor and i the parameter considered (heavy metal). C_Ai and C_Ni are the field measured value and the limit value respectively. Depending on the Cd value the waters can be slightly polluted, moderately polluted and highly polluted (Table 5).⁴⁸

Sediment pollution assessment indices

Potential ecological risk index due to heavy metal pollution of the sediment

The study used Lars Hakanson's potential ecological risk index (RI) method to evaluate the potential ecological risk of heavy metals in sediments (equation 9)

${\text{C}}_{\text{f}}^{\text{i}}=\frac{{\text{C}}_{\text{D}}^{\text{i}}}{{\text{C}}_{\text{R}}^{\text{i}}}$   (9)

${\text{E}}_r^i={\text{T}}_{\text{r }}^{\text{i}}\times {\text{ C}}_{\text{f }}^{\text{i}}$   (10)

$\text{RI}={\displaystyle \sum _{i=1}^n{\text{E}}_{\text{r}}^{\text{i}}}$   (11)

Where is the pollution index for a given heavy metal, is the reference value of the heavy metal in the sediment, is the present concentration of heavy metal, is heavy metal potential ecological risk factor, is the toxic response factor for a single heavy metal contamination, and RI is the total potential ecological risk index for heavy metals. The background values were obtained from the world surface rock average during the pre-industrial era.⁴⁹ In this study the background values were 127, 49, 16, 32, 0.2, 71 and 35,900 mg/kg for Zn, Ni, Pb, Cu, Cd, Cr and Fe respectively. When RI < 150, the risk level is low ; when 150 ≤ RI < 300, the risk level is medium ; when 300 ≤ RI < 600, the risk level is high ; and when RI ≥ 600, the risk level is very high. The toxic response factor for Cu, Zn, Cr, Ni, Pb and Cd is 5, 1, 2, 5,5 and 30 respectively.¹⁹

The geoaccumulation index

The geoaccumulation index (I_geo) method was used to evaluate the pollution level of heavy metals in sediments in the study area. This method eliminated the influence of natural geological accumulation.

${\text{I}}_{\text{geo}}=\log 2\frac{Ci}{1.5Bi}$   (12)

Where Ci is the heavy metal real concentration in the studied site ; and Bi would be the reference sample background value. Generally, the Igeo consists of 7 grades in the range of 5 < Igeo ≤ 0 in which minimum values indicate the soil has not been contaminated, while maximum values show it has been extremely contaminated. Igeo ≤ 0 means that the soil is not contaminated ; 0 < Igeo ≤ 1 indicates uncontaminated up to moderately contaminated degrees ; 1 < Igeo ≤ 2 presents a moderately contaminated degree ; 2 < Igeo ≤ 3 means moderately up to strongly contaminated degrees ; 3 < Igeo ≤ 4 indicates a strongly contaminated degree; 4 < Igeo ≤ 5 presents strongly up to extremely contaminated degrees, and lastly Igeo > 5 shows that the soil has been extremely contaminated.²⁶

Nemerow index

The Nemerow index (PN) can take into account the contents of all heavy metals and make a comprehensive evaluation of the pollution level of heavy metals in sediments.¹¹

$P_N=\sqrt{\text{Avg}{P}_i^2+\text{ Max}{P}_i^2/2}$   (13)

Where Pi is a single pollution index, Pi= Ci/Si. Ci is the measured concentration and Si is the pollutant concentration standard value. MaxPi and AvgPi are the maximum and average values of all index Pi, respectively. The method divides pollution into five levels : PN ≤ 0.7, safety domain ; 0.7 < PN ≤ 1.0, precaution domain; 1.0 < PN ≤ 2.0, slightly polluted domain; 2.0 < PN ≤ 3.0, moderately polluted domain; PN > 3, seriously polluted domain.¹⁹

Contamination factor (Cf)

The contamination factor (CF) was employed for assessing the contamination level of sediments, calculated by dividing the concentration of each heavy metal in the sediment (Cm) by its background concentration (Bm) as depicted in equation 14.

$Cf=\frac{\text{Cm }}{\text{Bm}}$   (14)

C_m is metal concentration in samples ; Bm represents background metal concentration ; Contamination factor (CF) CF < 1 ... low degree 1 ≤ CF < 3 ... moderate degree 3 ≤ CF < 6 ... considerable degree CF > 6 ... very high degree.⁵⁰

Enrichment Factor (EF)

Enrichment factors (EF) were utilized to assess potential anthropogenic contributions to the observed metal content in sediments and were computed following the method described:

$\frac{Cm}{Fe} sample/ \frac{Cm}{Fe} background$   (15)

Where ; Fe is iron concentration because of its abundance, Cm heavy metal concentration in the sediment sample; Enrichment factor (EF) EF ≤ 2 ... minimal enrichment 2<EF<5... moderate enrichment 5 < EF < 20 ... significant enrichment 20 < EF ≤ 40 ... very high enrichment EF > 40 ... extremely high enrichment.

Pollution load index

The PLI serves as a method for evaluating the overall extent of sediment pollution worldwide, factoring in the levels of various heavy metals. It is determined by considering the contamination factors (CF) of each metal as outlined in Equation (14). When computing the PLI for each sampling site, all heavy metal pollutants are considered.⁵¹

$\text{PLI = }{\left({\text{CF}}_{\text{1}}{\text{× CF}}_{\text{2}}{\text{× CF}}_{\text{3}}{\text{× CF}}_{\text{4}}\text{…}{\text{. × CF}}_{\text{n}}\right)}^{\text{1/n}}$   (16)

Where CF refers to the contamination factor for each pollutant; PLI<1 (indicates the uncontaminated degree of the sediments) and PLI>1 (indicates the contaminated degree of the sediments).

Statistical analysis

The Geoaccumulation Index (Igeo), Enrichment Factors (EFs), Contamination Factors (CFs), Pollution Load Indices (PLIs), Ecological Risk Index, Nemerow Index and Degree of Water Contamination were computed. Statistical analysis was conducted on the data. Pearson correlation analyses were utilized to discern relationships among the concentrations of various heavy metals, while Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were carried out to pinpoint potential sources of heavy metals across the Ngerengere River and its tributaries. All statistical procedures were executed using the OriginPro 2024 software. Key findings were presented in tables and figures.

Heavy metal concentration in surface water in the dry season

The study revealed that the order of magnitude of the recorded concentrations of heavy metals is Cu > Cr > Ni > Pb > Zn > Cd. The level of Pb concentrations ranged from >0.01 mg/L (which was below the detection limit) to 0.04±0.01 mg/L to 0.09±0.01 mg/L which exceeds the maximum permissible limits established by Tanzania Standards, WHO and US EPA. Higher concentrations of Chromium ranging from 0.02±0.01 mg/L to 0.25±0.01 mg/L exceed Tanzania, WHO and US EPA standards. The aforementioned levels of heavy metals were higher compared to the maximum allowable limits set by TBS, WHO and US EPA. The midstream and downstream areas of the Ngerengere River in Morogoro municipality showed higher heavy metal pollution compared to the upstream sections. This stems from industrial operations, agricultural runoff, the discharge of household sewage, and insufficient waste management practices, notably at the Mafisa dumpsite, which lacks proper containment facilities for leachate. Other studies have also reported the influence of human activities on heavy metal pollution in surface water.^45,52,53 The study revealed that the highest concentrations of Cd, Cu, Zn and Ni were 0.03±0.02 mg/L, 0.73±0.04 mg/L, 0.03±0.02 mg/L and 0.19±0.02 mg/L respectively. These findings are quite similar to other studies that reported the same concentrations ranges of heavy metals.^41,44,54 The recorded concentrations values are not in agreement with the recorded higher heavy metal concentrations in the Mara River in the same sampling campaigns.⁵⁵ This disagreement is attirbuted to the effect of mining activities ranging from artisanal to large-scale mining operations in the Mara area. Metals like Lead (Pb) and Cadmium (Cd) exhibit toxicity even at low concentrations and lack essential functions in the human body.⁵⁶ Consequently, they are categorized as non-essential or toxic metals. In the wet season the order of magnitude for heavy metals concentrations in river water was Zn> Ni>Cr>Pb>Cu >Cd with the concentrations of 4.07±0.081, 3.07±0.04, 0.053±0.04, 0.01±0.001, 0.007±0.001 and 0.002±0.001 for Zn, Ni, Cr, Pb, Cu and Cd respectively. The decrease in the concentration of Pb, Cr, Cd and Cu in the wet season accounts for the effect of dilution.⁵⁷ The increase in Zn and Ni concentration in the wet season is associated with an increase in sediment due to water erosion.⁵⁸

The findings on heavy metals concentration in the river water sampled during the dry season and wet season have been presented in Figure 2.

Figure 2 Heavy metals concentration in the river water sampled during wet season (a) and dry season (b).

Degree of water contamination

The index for metallic water contamination revealed the low and high degree of contamination. The calculated Cd values in river water samples in both dry and wet seasons ranged from 0 to 6.803 indicating low and high degrees of Contamination respectively (Table 6). The findings indicated that the highest heavy metal contamination was experienced during the dry season compared to the wet season. In Mara River, similar results were reported with a Cd value of less than 1.5 for all sampling locations.⁴⁸

Health risk assessment

Noncarcinogenic risk assessment

The estimated daily intake for children and adults via ingestion and dermal exposure in dry and wet seasons and its associated non-carcinogenic health risks related to the studied heavy metals are shown in the Table 7. The HI through ingestion and dermal exposure in dry and wet seasons for both children and adult groups was less than 1 in all sampling points. Hence, the study did not find evidence for potential noncarcinogenic risk related to heavy metals (Pb, Cd, Cr, Ni, Cu and Zn) within the urban catchment of Ngerengere River in Morogoro Municipality. The total HI for both exposure pathways across all sampling seasons for children and adults were 1.37×10^-1 and 4.70×10^-1 respectively (Table 7). Similar results were obtained in the previous study,¹⁰ that reported a total HI of 3.31×10^-3, 2.15×10^-6, and 3.32×10^-3 for ingestion and dermal exposure in the adult population in Iran. In Great Ruaha River low HI for dermal exposure but higher HI for ingestion exposure for the exposed groups were recorded.⁴⁶ Furthermore similar HI was obtained in the assessment of non-cancer risks associated with an exposure to fish cultured in selected private fishponds in Dar es Salaam, Tanzania.²⁴ In this study the contribution of six heavy metals to non-carcinogenic risk for children and adults via both ingestion and dermal exposure was in the order of Ni>Cr>Cd>Pb>Zn>Cu. Despite of lower HI for the population exposed to river water further investigation should be done to assess the non-cancer risks related to exposure from vegetables irrigated by river water. This is due to the phytoaccumulation potential of heavy metals.⁶ Furthermore other health implications that are not related to cancer might be attributed to other parameters such as pathogenic microorganisms.⁵⁹

Cancer risk assessment

Cancer risk assessment was conducted for metals Pb, Cr, Ni and Cd which are carcinogenic in nature only.⁵ For a particular heavy metal, an Individual Lifetime Cancer Risk (ILCR) below 1×10-6 is considered as insignificant, indicating negligible cancer risk. Conversely, an ILCR exceeding 1×10-4 is regarded as harmful, signifying significant cancer risk. As for the cumulative exposure to all heavy metals across various routes, the tolerable threshold is 1×10-5.^60,61 This study revealed that cancer risk via cumulative dermal and ingestion exposure in both children and adults was high in both wet and dry seasons. The potential cancer risks for children ranged from 2.4×10^-3 to 2.5×10^-1 for the dermal pathway and 1.4×10^-2 to 1.5×10^-1 for the ingestion pathway, while for the adult group cancer risk ranged from 1.4×10^-2 to 7.9×10^-1 for dermal pathway and 2.6×10^-2 to 5.8×10^-1 for ingestion pathway. Overall, the calculated cancer risk for children and adults through dermal and ingestion exposure were 4.14.1×10^-1 and 1.41 respectively. The study revealed that Cr had the highest average contribution of ILCR with its highest individual ILCR values of 8.1×10^-1 and 5.7×10^-1 recorded for adults and children respectively via ingestion exposure while Cd has the lowest cancer risk of 1.3×10^-7. The health implications of the studied heavy metals including Lead (Pb), for example, possess carcinogenic properties and can negatively affect the respiratory and digestive systems, as well as suppress the immune system. It is particularly harmful to children, impacting their intelligence and nervous systems.⁶² Cadmium (Cd) tends to accumulate in the circulatory system, kidneys (especially the renal cortex), lungs, and heart, posing toxicity to bones and gonads. These risks are acknowledged by the International Agency for Research on Cancer and the National Toxicology Program,⁶³ with Cd classified as a Group 1 carcinogen. Chromium (Cr) can take on various oxidation states, with hexavalent chromium (VI) being highly soluble and mobile, causing harm to the skin, liver, kidneys, and respiratory organs. It leads to ailments such as dermatitis, renal tubular necrosis, nasal septum perforation, and lung cancer.⁶⁴ Nickel (Ni) tends to accumulate primarily in the spinal cord, brain, and organs due to its mutagenic and carcinogenic properties.⁶⁵ These results clearly show that adults are more vulnerable to health risks associated with drinking water than children. In Great Ruaha River, Tanzania health risk assessment reported that adults were most vulnerable to health risks due to exposure to heavy metals contamination.⁴⁶ These findings signified the contamination of the Ngerengere Urban River water and justified the need for prior treatment of river water for healthy human consumption. The use of water with Pb, Cr, Ni and Cd levels higher than the permissible limit can also be of health risk to the aquatic organisms that live in the water.⁴³

Uncertainty analysis

In health risk assessment, errors may arise due to various factors. Future exposure assessment relies on factors such as the fate and transport of heavy metals, estimations, remedial options, land use projections, and assumptions about the frequency and duration of exposure, all of which can introduce uncertainties. In our study, we made the assumption that water for domestic use is consumed every day of the year, and cancer risk was assessed over a span of 70 years of exposure. However, it's important to note that individuals may not necessarily reside in Morogoro Municipality for the entirety of those years.

Sediment ecological risk assessment

Heavy metals in sediments

The results of heavy metal concentration in the sediments are shown in Figure 3. In the wet season the highest mean concentration was 0.117 mg/kg, 0.98 mg/kg, 6.238 mg/kg, 0.041 mg/kg and 12.755 mg/kg for Pb, Cr, Ni, Cu and Zn respectively. Cd concentration was below the detection limit in the wet season. In dry season highest mean concentrations were 4.11 mg/kg, 1.078 mg/kg, 1.312 mg/kg, 0.814, 2.533 mg/kg and 0.639 for Zn, Cu, Ni, Cd, Cr and Pb respectively. The higher concentrations of heavy metals in sediment are due to the deposition of heavy metals in river water and the adsorption process in riverbed sediments.²⁶ While river sediments can absorb certain heavy metals, thus mitigating water pollution to some extent, they can also leach these metals back into the water, resulting in secondary pollution that proves challenging to manage.⁶⁶ Furthermore, these results are not consistent with other studies that reported the elevated concentrations of heavy metals in areas surrounded by mining activities.^15,67 There is no specified sediment quality guideline, therefore the findings from this study were compared with US EPA sediment quality guideline. Based on the US EPA guidelines the maximum concentration of the heavy metals in the sediment should be <40 mg/kg, <25 mg/kg, <25 mg/kg and <90 mg/kg for Pb, Cr, Cu and Zn respectively. The obtained concentrations of heavy metals in sediment were within the US EPA guidelines. Physical parameters of the sediments were analyzed for soil moisture, total carbon and textural classes. Moisture percentage in the soil sample ranged from 9.187±0.314% to 41.455±0.598% while total carbon ranged from 1.03±0.241% to 5.231±0.158% in the dry season. For the wet season moisture percentage in the sediment samples ranged from 11.612±0.013% to 41.419±0.036%, and total carbon ranged from 0.259±0.023% to 3.375±0.032%. Textural classification revealed that the dominant texture properties of the collected sediments were composed of loamy sand and silt clay. Other study, indicated that a notable section of Morogoro Municipality, particularly its central regions, is defined by silty clay soil, while loamy sand predominates in the peripheral areas.³⁸

Figure 3 Heavy metal concentrations (mg/kg) in dry (a) and wet season (b) for the selected sediments sample.

Multivariate analytical tests for heavy metal loading in the sediment

It's crucial to pinpoint the sources of this pollution to establish an efficient action plan. Multivariate analyses have proven successful in numerous studies^8,68 serving as effective tools for identifying the sources of heavy metal pollution in sediment.^69,70 A Pearson correlation test was conducted to explore the connections between the concentrations of various heavy metals (Table 8). In the dry season strong positive correlations were found between Cr and Cu (with a correlation coefficient (of 0.78224, p<0.01), Cu and Zn (0.76809, p<0.01). Moderate positive correlation between Cr and Ni (0.59746, p<0.01), Ni and Cu (0.55327, p<0.01), and low positive correlation for Pb and Cd (0.44617, p<0.01), Cr and Zinc (0.36139, p<0.01), Pb and Zinc (0.3273, p<0.01), Pb and Cr (0.30172, p<0.01), Pb and Cu (0.28838, p<0.01), Ni and Zn (0.20374, p<0.01), Cd and Zn (0.18603, p<0.01) , Cd and Cu (0.17202, p<0.01) Pb and Ni, (0.01617, p<0.01), Cr and Cd (0.13303, p<0.01) Pb and Cr (0.30172, p<0.01). A negative correlation was also recorded between Cd and Ni (-0.05191, p<0.01). Therefore, based on these findings it can be concluded that in the dry season there was a positive correlation of heavy metals concentrations across the sampling stations except for Cd and Ni.

The sources of the heavy metals found in the sediments in the Ngerengere River and its tributaries were analyzed using PCA and HCA. Both PCA and HCA were used to determine whether the heavy metals (Pb, Cr, Cd, Ni, Cu and Zn) had a common source. The PCA results for the heavy metal concentrations in dry season are shown in Table and HCA is presented in Figure 4. Principal Component Analysis (PCA) was conducted utilizing Varimax rotation and Kaiser Normalization. The outcomes of the PCA suggest that the variables can be categorized into two principal components, PC1 and PC2.

Figure 4 PCA Bi-plot for heavy metal variance in sediment during dry season (a) and wet season (b).

Component 1 (PC1) is positively loaded with the Pb, Cr, Ni, Cd, Cu and Zn concentrations. Component 2 (PC2) is associated with the Pb, Cd and Zn concentrations. PC1 and PC2 explain 47.81% and 23.22%, respectively, of the total variance. Pb, Cd and Ni have a positive loading for both PC1 and PC2 (Table 9).

Hierarchical Cluster Analysis (HCA) was conducted on standardized data employing Z-scores, utilizing Ward's method and the Euclidean distance metric.Top of Form The analyzed parameters were initially divided into two major clusters (Figure 5). Cluster 1 consists of twelve sampling stations. Cluster 2 consists of only one sampling station. C1 and C2 include the sampling points of the upstream to downstream and midstream reaches, respectively. The heavy metal concentration value of C2 was roughly twice as much as that of C1, indicating that there were significant impacts of human activities in the dry season. In the wet season the study observed a positive high correlation between Ni and Zn (0.76995, p<0.01), a moderate positive correlation between Pb and Cr (0.52943, p<0.01), a weak positive correlation between Cr and Cu, Cr and Ni, Cr and Zn, Cu and Zn. A negative correlation was observed for Pb and Ni, Pb and Cu as well as Ni and Cu (Table 10).

Figure 5 HCA dendrogram for clustered pollution sources in dry season (a) and wet season (b).

Furthermore, the PCA yielded two significant components of pollution with eigenvalues >1.00, accounting for a total of 61.85% of the variation in heavy metal concentration (Table 11). The first principal component (PC1), 36.31% of the calculated variance, exhibited a high positive load for Zn, but a low positive load for Cu, Ni and Cr. Chromium and Nickel are commonly found together in various types of rocks and consequently can be present in soils derived from these rock formations and their concentration can be elevated by an influence of anthropogenic activities.⁷¹ This was confirmed by our study, obtaining a positive correlation coefficient between them in both dry and wet seasons. The second principal component, which explained 25.54% of the total variance, showed a strongly positive load of Pb, a moderate positive loading for Cr but a low positive loading of Cu. Cu is an abundant metal in nature and it has wide application in industrial activities.⁷² The two elements (Pb and Cr) recorded a high and moderate positive loading are associated with close geo-chemical dependence as the iron family in the natural soils,⁷³ which is presented again in the current results with a positive correlation coefficient of r = 0.52943 and 0.30172 in both wet and dry season respectively. A negative load was recorded for Ni and Zn.

In the wet season, the spatial cluster analysis CA generated a dendrogram (Figure 5), where all thirteen sampling sites were divided into two statistically significant clusters. Cluster 1 comprised two sampling sites (S-1 and site-13), while Cluster 2 comprised the remaining eleven sampling sites (site-2, site-3, site-4, site-5, site-6, site-7, site-8, site-9, site-10, site-11, and site-12). The classification of clusters varied depending on the significance level due to the similarity in characteristic features and anthropogenic or natural background source types among the sites. Cluster 1 represented low-contaminated sites (upstream reaches of the Ngerengere River and its tributaries, whereas Cluster 2 represented highly contaminated sites (midstream and downstream reaches of the Ngerengere River and its tributaries). These observations are similar to a previous study.⁵⁰

Indices for sediment pollution assessment

Pollution load index

The classifications for PLI classes are as follows: PLI < 1 indicates sediments in excellent condition; PLI = 1 suggests sediments are at a baseline quality level; and PLI > 1 indicates a progressive deterioration of the site.⁵¹ In this study, data from the dry period showed higher PLI levels compared to those from the dry period (Figure 7a). Moreover, neither the dry nor the wet period had PLI values ≥ 1, indicating that the PLI values reflected sites in good ecological health. Generally, the individual PLIs for Cd for all sampling stations were > 1, indicating that the sediment at those sampling stations was contaminated with Cd, especially from anthropogenic activities.⁷⁴

Enrichment factor

Human activities' impact on heavy metal concentrations in shallow sediments of the Ngerengere River and its tributaries in Morogoro Municipality was studied by assessing anthropogenic sources via EF calculations. Iron (Fe) was used as a reference element to differentiate between anthropogenic and natural sources and has been previously employed for this purpose.⁷⁰ Top of Form

EF values below 2 indicate minimal enrichment of a heavy metal or metalloid, while values between 2 and 5 suggest moderate enrichment. A value exceeding 5 but below 20 signifies significant enrichment, and values surpassing 20 indicate very high enrichment. EF values beyond 40 indicate extremely high enrichment. The order of EF in the dry season and wet season were Cd>Pb>Cu>Zn>Cr>Ni with EF values of 341, 5, 3, 2.9, 2.8 and 2 respectively. In this study, during the dry season there was extremely high enrichment for Cd. These similar findings indicated extremely heavy metal enrichment in Mara River sediments were also reported in other research.¹⁵ EF values for Cd during the dry season across all sampling stations were considerably above 40. For Pb in the dry season, moderate sediment enrichment was observed at sampling stations S2, S6, S8, S9, S10 and S11, with EF values of 5, 3, 2, 2, 2 and 3, respectively. The study findings revealed that EF values for Cr, Ni, Cu, and Zn for the most of sampling stations were well below 2 (Figure 6), indicating minimal enrichment of these two toxic heavy metals during the dry month. In the wet season the order of EF values was in the order of Ni>Zn>Cr>Pb>Cu>Cd with EF values of 93, 73, 4, 3, 0.5, and 0 respectively. The study revealed that extremely high enrichment was recorded for Ni and Zn with EF values of 93 and 73 respectively. Moderate enrichment was observed for Pb and Cr while EF values for Cd and Cu in wet seasons were below, which suggests minimum enrichment of C and Cu in Ngerengere river sediment within Morogoro Municipality in wet season attributed to dilution due to flooding. Extremely heavy metals enrichment across the sampling station in both dry and wet seasons indicated the influence of anthropogenic activities such as agriculture activities, industrial and domestic sewage⁷⁵ and vehicles and spillage particularly from Tanzam highway Municipality.^22,76

Figure 6 EF values in the wet (a) and dry season (b).

Geoaccumulation index

The mean Igeos for the sampling points are shown in Table 12. The mean I_geos indicate that sediment at most of the sampling points was uncontaminated (Igeo≤0) with heavy metals but that sediment at some sampling points during the dry was contaminated with Cd. Sediment at Sediment at S5, S6, S7 and S11 was moderately contaminated with Cd (1≤Igeo≤2). The mean Igeos for both dry and wet seasons decreased in the order Cd (0.382) >Pb (0.0048) >Zn (0.0043) >Cu (0.0041) > Cr (0.0035) > Ni (0.0027).

Contamination factor

During the wet season, all the heavy metal CFs were below 1, indicating a low level of contamination. In the dry season the CFs for Pb, Ni, Cr, Cu and Zn for all the sampling points indicate moderate contamination (1<CF< 6). The mean CFs decreased in the order Ni (2.287) >Cd (2.194) >Co (0.794) >Cr (0.793) > Pb (0.609)>Fe (0.552)>Mn (0.517)>Cu (0.503)>Zn (0.355). In contrast, during the dry season, CF values for Cd and across all sites ranged between 1 and 3, suggesting a moderate degree of contamination (Figure 7). There was a considerable degree of contamination to pose ecological implications ¹⁹ due to high CFs for Cd at sampling stations S6, S7, and S8 which represent the midstream and downstream reaches of the Ngerengere River and tributaries It is recognized that seasonal changes in temperature and rainfall can impact the levels and distribution of specific heavy metals in aquatic environments. These climatic variations demonstrate specificity in their effects on different heavy metals.⁷⁷

Figure 7 CF values for sediment samples collected from the Ngerengere River and its tributaries in the dry (a) and wet seasons (b).

Ecological risk index

The risk index (RI) classes are categorized as follows: typically, RI values below 150 indicate a low potential for ecological risk; 150 ≤ RI < 300 suggest a moderate potential ecological risk; RI values when 300 ≤ RI < 600, indicate a considerable ecological risk; and RI values exceeding 600 indicate sediment quality posing a significant environmental health risk. Findings from the current study indicate that, in both dry and wet seasons all sampling sites for the main river and its tributaries had RI values below 150 (Table 13), suggesting that the sites studied were at a low potential for ecological risk. Generally, across all sampling stations, the RI values were higher for samples collected during the dry period compared to those from the wet period.

Nemerow Index of Heavy Metals contamination in the sediments

The analysis of Nemerow's synthetic contamination index (PN), derived from the single pollution index (Pi), indicated the safety domain group of sediment contamination status for the Ngerengere River and its tributaries with PN values ≤ 0.7 in both wet and dry season. PN values obtained from this study were 0.541465 and 0.520241 for the wet and dry seasons.

Broad health and ecological implications of heavy metal pollution

This study examined six harmful substances (Pb, Cr, Ni, Cd, Cu, and Zn) in the Urban catchment of the Ngerengere River, Tanzania. Research has shown heavy metals severely affect human health, impacting systems like hematopoietic, nervous, endocrine, and cardiovascular.⁵ Heavy metals concentrations, were found to be higher in both sediment and water samples around the midstream and downstream reaches of the Ngerengere river and its tributaries, likely due to the presence of point and non-point sources of pollution.⁷ Generally, for water samples, the highest heavy metal concentrations were in dry season where the recorded concentrations were 0.09±0.01 mg/L, 0.25±0.01 mg/L, 0.03±0.02 mg/L, 0.73±0.04 mg/L, 0.03±0.02 mg/L and 0.19±0.02 mg/L for Pb, Cr, Cd, Cu, Zn and Ni respectively. The observed results poses serious health risks to humans and has toxic effects on fish and other aquatic organisms.⁷⁸

Noncarcinogenic risk assessments found that daily intake (EDI) for children and adults, via ingestion and dermal exposure in both dry and wet seasons, never exceeded a hazard index (HI) of 1 at any sampling point. This suggests no evidence of potential noncarcinogenic risks from heavy metals (Pb, Cd, Cr, Ni, Cu, and Zn) in the studied area. These findings align with other previous studies that reported the same EDI values.^10,46 Despite the low hazard indices for populations exposed to river water, further investigation is needed to assess the noncancer risks associated with exposure from vegetables irrigated by river water, considering the potential phytoaccumulation of heavy metals. The cancer risk assessment, the study focused on metals Pb, Cr, Ni, and Cd, known for their carcinogenic properties.⁷⁹ The individual lifetime cancer risk (ILCR) values for these metals were found to be high, exceeding the threshold of significance (1×10^-6) across various exposure pathways for both children and adults in both wet and dry seasons. Particularly high ILCR values were observed for Cr, with the highest individual values recorded for adults and children via ingestion exposure.

The highest metal concentrations in the sediment during wet season were 0.117 mg/kg, 0.98 mg/kg, 6.238 mg/kg, 0.041 mg/kg and 12.755 mg/kg for Pb, Cr, Ni, Cu and Zn respectively. Cd were not detected in all samples during the wet season due to its known mobility properties in aquatic environment.⁸⁰ During the dry season, the maximum average concentrations were 4.11 mg/kg for Zinc (Zn), 1.078 mg/kg for Copper (Cu), 1.312 mg/kg for Nickel (Ni), 0.814 mg/kg for Cadmium (Cd), 2.533 mg/kg for Chromium (Cr), and 0.639 mg/kg for Lead (Pb). Across all sampling seasons strong positive correlations of heavy metals in the sediment were found between Cr and Cu; Cu and Zn, suggesting co-occurrence properties of these metals.⁴⁰

The PLI values provide insights into the overall pollution status of sediments. In our study, PLI values were consistently below 1 during both the dry and wet seasons, indicating good ecological health of the sites. This suggests that the sediments are relatively unpolluted and maintain a baseline quality level. However, individual PLIs for Cd exceeded 1 at all sampling stations, indicating Cd contamination likely originating from anthropogenic activities.⁸¹ The EF values offer a deeper understanding of the influence of human activities on heavy metal concentrations in sediments. During the dry season, extremely high enrichment for Cd was observed across all sampling stations, indicating significant anthropogenic contributions. Moderate enrichment of Lead (Pb) was also observed at several sampling stations, this has been also reported in Msimbazi river, which is one among the large Urban Rivers in Tanzania.⁸² In the wet season, Ni and Zn showed extremely high enrichment, contrasting with the dry season. These results highlight the influence of human activities like agriculture, industry, and vehicular emissions on heavy metal contamination. Most sediment samples were uncontaminated according to the Geoaccumulation Index, except for Cd contamination at some points during the dry season. This suggests localized contamination possibly due to anthropogenic inputs.⁸¹ The CF values indicate the degree of sediment contamination with heavy metals. During the dry season, moderate contamination was observed for Pb, Ni, Cr, Cu, and Zn across all sampling points, highlighting the influence of anthropogenic activities. Cd contamination was particularly significant at certain sampling stations, indicating ecological implications for the affected areas. In contrast, the wet season showed lower CF values, suggesting reduced contamination levels attributed to dilution effects from flooding. The RI values assess the potential ecological risk posed by sediment contamination. All sampling sites during both dry and wet seasons had RI values below 150, indicating a low potential for ecological risk, these results are slightly coincide with other study reported low RI of Cr, Cu and Pb.⁸³ However, RI values were generally higher during the dry season compared to the wet season, reflecting seasonal variations in heavy metal distribution and contamination levels. The PN values indicate the overall contamination status of sediments. In our study, PN values ≤ 0.7 were obtained for both wet and dry seasons, suggesting that sediments in the Ngerengere River and its tributaries fall within the safety domain group of contamination status.

Heavy metal concentrations in the water and sediment of the Ngerengere River and its tributaries were assessed to evaluate health and ecological risks associated with heavy metal contamination in the surface water and sediment. The study focused on the urban catchment of the Ngerengere River, including urban centers in Morogoro Municipality, where the river and its tributaries drain. Various pollution indices, such as Igeo, EFs, CFs, PLIs, Ecological Risk Index, Nemerow Index, and Degree of Water Contamination, were computed to analyze water and sediment pollution and associated ecological risks. The contamination level of river water by degree of water contamination was assessed based on observed concentration levels. During both dry and wet seasons, measured degree of water contamination values ranged from 0 to 6.803, showing contamination variability, with highest concentrations in the dry season. The study found varying potential cancer risks from dermal and ingestion exposure among children and adults. Children had slightly higher ingestion risks, while adults had higher dermal risks, though lower overall than children. Cancer risks from cumulative exposure were elevated in both groups in both seasons. Noncancer risks were >1, indicating lower risk via dermal and ingestion exposure. Mean sediment heavy metal concentrations in decreasing order for dry and wet seasons were Zn>Ni>Cr>Cu>Pb>Cd, with highest Zn and Ni concentrations in dry season and Pb, Cr, Cd, and Cu in wet season, indicating seasonal variation. Lowest Pb, Cd, and Cu concentrations in wet season suggested heavy rainfall effects during sampling, increasing river hydrodynamics and sediment transport, signifying higher heavy metal pollution input.

Pollution indices and statistical analysis supported observed variability and ecological implications. The mean Igeos for both dry and wet seasons decreased in the order of Cd>Pb>Zn >Cu>Cr> Ni. The order of EF in the dry season and wet season were Cd>Pb>Cu>Zn>Cr>Ni with high EF values of Cd. The mean CF values for all heavy metals in the wet season were <1, while the highest CF was 3.325 for Cd at sampling station S7. The PLIs indicate that in the wet season all the sampling points were uncontaminated. However, in the dry season sediments were contaminated with Cd in most of the sampling stations. The results of the present study reveal that during both the dry and wet seasons, all sampling sites along the main river and its tributaries exhibited Ecological Risk index (RI) values below 150. The examination of Nemerow's synthetic contamination index (PN), which is derived from the single pollution index (Pi), revealed a category of sediment contamination considered within the safety domain for the Ngerengere River and its tributaries.

These findings build on previous Ngerengere River studies, using health risk and sediment pollution indices to assess heavy metal impacts. Cd and Pb present risks to the river ecosystem, likely from agricultural pesticide and fertilizer use in Kichangani and Chamwino areas, fuel use, corrosion-resistant paint on fishing boats, untreated aquaculture wastewater, and settlement runoff. The urban river serves over 471,409 residents and faces risks to human health, aquatic organisms, and ecosystem services from these toxic metals. Short-term measures should include regular water quality monitoring and treatment, while long-term efforts should focus on river rehabilitation and restoration. Authorities need robust measures to prevent heavy metal pollution, particularly Cd, Pb, Cr, and Ni. Further research should be done to investigate ecotoxicity on Ngerengere River biota.

The authors would like to express their gratitude to Sokoine University of Agriculture for funding this research. Special thanks are extended to Mr. Mpeji Mbulume and Ado Ndimbo for their invaluable assistance as laboratory technicians in the analysis of samples. The authors also acknowledge the significant contribution of Eng. Nancy Nyenga, Professor Stelyus L. Mkoma and Dr. Amina Hamad in shaping the ideas behind this research. Additionally, sincere appreciation goes to the Director of Morogoro Municipality for granting the permit to conduct this research.

Author’s contribution

S.S.M designed the study, conducted data collection and analysis, interpretation of findings, S.L.M, and A.A.M provide technical inputs and review of the manuscript, supported the final write up of the manuscript.

Availability of data and material

The data sets used and analyzed during the study are available and still under analysis for subsequent publications but will be available upon request from authors

This research received support from the Sokoine University of Agriculture through its capacity-building program for staff. The funder had no influence on the analysis and interpretation of the findings presented in this study.

The authors declare that they have no competing interests.

1. Naiman RJ, Dudgeon D. Global alteration of freshwaters: influences on human and environmental well‐being. Ecological Research. 2011;26(5):865–873.
2. Miraji M, Liu J, Zheng C. The impacts of water demand and its implications for future surface water resource management: the case of Tanzania’s Wami Ruvu Basin (WRB). Water. 2019;11(6):1280.
3. McNally CG, Gold AJ, Pollnac RB, et al. Stakeholder perceptions of ecosystem services of the Wami River and Estuary. E&S. 2016;21(3):art34.
4. Wang F, Dong W, Zhao Z, et al. Heavy metal pollution in urban river sediment of different urban functional areas and its influence on microbial community structure. Sci Total Environ. 2021;778:146383.
5. Zhong WS, Ren T, Zhao LJ. Determination of Pb (Lead), Cd (Cadmium), Cr (Chromium), Cu (Copper), and Ni (Nickel) in Chinese tea with high–resolution continuum source graphite furnace atomic absorption spectrometry. J Food Drug Anal. 2016;24(1):46–55.
6. Kacholi DS, Sahu M. Levels and health risk assessment of heavy metals in soil, water, and vegetables of Dar es Salaam, Tanzania. Journal of Chemistry. 2018;2018:1–9.
7. Njuguna SM, Githaiga KB, Onyango JA, et al. Ecological and health risk assessment of potentially toxic elements in Ewaso Nyiro River surface water, Kenya. SN Appl Sci. 2021;3(2):148.
8. Saha P, Paul B. Assessment of heavy metal pollution in water resources and their impacts: a review. 2016;3(8).
9. Mahmoud M, Hamouda M, Al Kendi R, et al. Health risk assessment of household drinking water in a district in the UAE. Water. 2018;10(12):1726.
10. Guo X, Xiao Y, Zhao L, et al. Spatio–temporal analysis and health risk assessment of heavy metals in water from the Fuhe River, South China. Water. 2023;15(4):641.
11. Calmuc VA, Calmuc M, Arseni M, et al. Assessment of heavy metal pollution levels in sediments and of ecological risk by quality indices, applying a case study: the lower Danube River, Romania. Water. 2021;13(13):1801.
12. Li K, Cui S, Zhang F, et al. Concentrations, possible sources and health risk of heavy metals in multi–media environment of the Songhua River, China. Int J Environ Res Public Health. 2020;17(5):1766.
13. Ochieng EZ, Lalah JO, Wandiga SO. Analysis of heavy metals in water and surface sediment in five rift valley lakes in Kenya for assessment of recent increase in anthropogenic activities. Bull Environ Contam Toxicol. 2007;79(5):570–576.
14. Varol M, Şen B. Assessment of nutrient and heavy metal contamination in surface water and sediments of the upper Tigris River, Turkey. CATENA. 2012;92:1–10.
15. Nkinda MS, Rwiza MJ, Ijumba JN, et al. Quantitative assessment of metal contamination and associated pollution risk in sediments from the Mara River in Tanzania. Environ Monit Assess. 2020;192(11):721.
16. Iordache M, Popescu LR, Pascu LF, et al. Environmental risk assessment in sediments from Jiu River, Romania. REV CHIM. 2015;(8).
17. Demirak A, Yilmaz F, Levent Tuna A, et al. Heavy metals in water, sediment and tissues of Leuciscus cephalus from a stream in southwestern Turkey. Chemosphere. 2006;63(9):1451–1458.
18. Salcedo Sánchez ER, Martínez JME, Morales MM, et al. Ecological and health risk assessment of potential toxic elements from a mining area (Water and Sediments): The San Juan–Taxco River System, Guerrero, Mexico. Water. 2022;14(4):518.
19. Liu J, Wu H, Feng J, et al. Heavy metal contamination and ecological risk assessments in the sediments and zoobenthos of selected mangrove ecosystems, South China. CATENA. 2014;119:136–142.
20. Schaefer MP, Dietrich O. Water resources situation in the Ngerengere river basin. 2016.
21. Chen SS, Kimirei IA, Yu C, et al. Assessment of urban river water pollution with urbanization in East Africa. Environ Sci Pollut Res. 2022;29(27):40812–40825.
22. Mdegela RH, Braathen M, Pereka AE, et al. Heavy metals and organochlorine residues in water, sediments, and fish in aquatic ecosystems in urban and peri–urban areas in Tanzania. Water Air Soil Pollut. 2009;203(1–4):369–379.
23. Kumar V, Sharma A, Pandita S, et al. A review of ecological risk assessment and associated health risks with heavy metals in sediment from India. International Journal of Sediment Research. 2020;35(5):516–526.
24. Leonard LS, Mahenge A, Mudara N. Assessment of heavy metals contamination in fish cultured in selected private fishponds and associated public health risk concerns, Dar es Salaam, Tanzania. Marine Science and Technology Bulletin. 2022;11(2):246–258.
25. Salam MA, Paul SC, Shaari FI, et al. Geostatistical distribution and contamination status of heavy metals in the sediment of Perak River, Malaysia. Hydrology. 2019;6(2):30.
26. Liu J, Wu J, Feng W, et al. Ecological risk assessment of heavy metals in water bodies around typical copper mines in China. IJERPH. 2020;17(12):4315.
27. Zhang S, Chen B, Du J, et al. Distribution, assessment, and source of heavy metals in sediments of the Qinjiang River, China. Int J Environ Res Public Health. 2022;19(15):9140.
28. Mollo VM, Nomngongo PN, Ramontja J. Evaluation of surface water quality using various indices for heavy metals in Sasolburg, South Africa. Water. 2022;14(15):2375.
29. Jiménez–Oyola S, Escobar Segovia K, García–Martínez MJ, et al. Human health risk assessment for exposure to potentially toxic elements in polluted rivers in the Ecuadorian Amazon. Water. 2021;13(5):613.
30. Sun C, Zhang J, Ma Q, et al. Human health and ecological risk assessment of 16 polycyclic aromatic hydrocarbons in drinking source water from a large mixed–use reservoir. IJERPH. 2015;12(11):13956–13969.
31. Soliman NF, Nasr SM, Okbah MA. Potential ecological risk of heavy metals in sediments from the Mediterranean coast, Egypt. J Environ Health Sci Engineer. 2015;13(1):70.
32. Schertzinger G, Ruchter N, Sures B. Metal accumulation in sediments and amphipods downstream of combined sewer overflows. Sci Total Environ. 2018;616–617:1199–1207.
33. Groffen T, Rijnders J, Van Doorn L, et al. Preliminary study on the distribution of metals and persistent organic pollutants (POPs), including perfluoroalkylated acids (PFAS), in the aquatic environment near Morogoro, Tanzania, and the potential health risks for humans. Environ Res. 2021;192:110299.
34. Shagega FP, Munishi SE, Kongo VM. Prediction of future climate in Ngerengere river catchment, Tanzania. Physics and Chemistry of the Earth, Parts A/B/C. 2019;112:200–209.
35. Mtalo F, Mulungu D, Mwanuzi F, et al. Hydrological analysis for the Eastern ArcMountain forests. Conservation and Management of the Eastern Arc Mountain Forests—Forestry and Beekeeping Division, Dar er Salam. 2005.
36. Lalika M. The potential of riparian forests in anthropogenic stressed river ecosystems. 2021.
37. Natkhin M, Dietrich O, Schäfer MP, et al. The effects of climate and changing land use on the discharge regime of a small catchment in Tanzania. Reg Environ Change. 2015;15(7):1269–1280.
38. Mkumbo NJ, Mussa KR, Mariki EE, et al. The use of the DRASTIC–LU/LC model for assessing groundwater vulnerability to nitrate contamination in Morogoro Municipality, Tanzania. Earth. 2022;3(4):1161–1184.
39. Onjefu SA, Shaningwa F, Lusilao J, et al. Assessment of heavy metals pollution in sediment at the Omaruru River basin in Erongo region, Namibia. Environmental Pollutants and Bioavailability. 2020;32(1):187–193.
40. Liu L, Wang Z, Ju F, et al. Co–occurrence correlations of heavy metals in sediments revealed using network analysis. Chemosphere. 2015;119:1305–1313.
41. Bamuwamye M, Ogwok P, Tumuhairwe V, et al. Human health risk assessment of heavy metals in Kampala (Uganda) drinking water. JFR. 2017;6(4):6.
42. Guidance manual for the use of production–based pretreatment standards and the combined wastestream formula.
43. Isa BK, Amina SB, Aminu U, et al. Health risk assessment of heavy metals in water, air, soil and fish. Afr J Pure Appl Chem. 2015;9(11):204–210.
44. Janoska O, Gruszecka–Kosowska A. Water quality and human health risk assessment: a case study of the Czarna Przemsza river source in Zawiercie, Poland. Human and Ecological Risk Assessment: An International Journal. 2020;26(3):757–781.
45. Mohammadi AA, Zarei A, Majidi S, et al. Carcinogenic and non–carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran. MethodsX. 2019;6:1642–1651.
46. Ngowi JG, Saria JA. Human health risks from exposure to heavy metals in water from great Ruaha River Serving Domestic Purpose in Pawaga Division. 2023;5.
47. Mahmoud M, Hamouda M, Al Kendi R, et al. Health risk assessment of household drinking water in a district in the UAE. Water. 2018;10(12):1726.
48. Nkinda MS, Rwiza MJ, Ijumba JN, et al. Heavy metals risk assessment of water and sediments collected from selected river tributaries of the Mara River in Tanzania. Discov Water. 2021;1(1):3.
49. Martin JM, Meybeck M. Elemental mass–balance of material carried by major world rivers. Marine Chemistry. 1979;7(3):173–206.
50. Varol M. Dissolved heavy metal concentrations of the Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin, Turkey. Chemosphere. 2013;93(6):954–962.
51. Tomlinson DL, Wilson JG, Harris CR, et al. Problems in the assessment of heavy–metal levels in estuaries and the formation of a pollution index. Helgolander Meeresunters. 1980;33(1–4):566–575.
52. Xiao J, Wang L, Deng L, et al. Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci Total Environ. 2019;650:2004–2012.
53. Huang L, Rad S, Xu L, et al. Heavy metals distribution, sources, and ecological risk assessment in Huixian Wetland, South China. Water. 2020;12(2):431.
54. Kihampa C. Heavy metal contamination in water and sediment downstream of municipal wastewater treatment plants, Dar es Salaam, Tanzania. 2013;3.
55. Nkinda MS, Rwiza MJ, Ijumba JN, Njau KN. Heavy metals risk assessment of water and sediments collected from selected river tributaries of the Mara River in Tanzania. Discov Water. 2021;1(1):3.
56. Resma NS, Meaze AMH, Hossain S, et al. The presence of toxic metals in popular farmed fish species and estimation of health risks through their consumption. Physics Open. 2020;5:100052.
57. Wen Y, Yang Z, Xia X. Dissolved and particulate zinc and nickel in the Yangtze River (China): Distribution, sources and fluxes. Applied Geochemistry. 2013;31:199–208.
58. Song Y, Ji J, Mao C, et al. Heavy metal contamination in suspended solids of Changjiang River — environmental implications. Geoderma. 2010;159(3–4):286–295.
59. Nimusiima D, Byamugisha D, Omara T, et al. Physicochemical and microbial quality of water from the Ugandan stretch of the Kagera Transboundary River. Limnological Review. 2023;23(3):157–176.
60. Wcisło E, Ioven D, Kucharski R, et al. Human health risk assessment case study: an abandoned metal smelter site in Poland. Chemosphere. 2002;47(5):507–515.
61. Cao S, Duan X, Zhao X, et al. Health risks from the exposure of children to As, Se, Pb and other heavy metals near the largest coking plant in China. Sci Total Environ. 2014;472:1001–1009.
62. Preuss HG. A review of persistent, low–grade lead challenge: neurological and cardiovascular consequences. J Am Coll Nutr. 1993;12(3):246–254.
63. Davis AC, Wu P, Zhang X, et al. Determination of cadmium in biological samples. Applied Spectroscopy Reviews. 2006;41(1):35–75.
64. Ražić S, Đogo S. Determination of chromium in Mentha piperita L. and soil by graphite furnace atomic absorption spectrometry after sequential extraction and microwave–assisted acid digestion to assess potential bioavailability. Chemosphere. 2010;78(4):451–456.
65. Soylak M, Tuzen M, Souza AS, et al. Optimization of microwave assisted digestion procedure for the determination of zinc, copper and nickel in tea samples employing flame atomic absorption spectrometry. J Hazard Mater. 2007;149(2):264–268.
66. Wang Z, Hong C, Xing Y, et al. Spatial distribution and sources of heavy metals in natural pasture soil around copper–molybdenum mine in Northeast China. Ecotoxicol Environ Saf. 2018;154:329–336.
67. Chen Y, Jiang X, Wang Y, et al. Spatial characteristics of heavy metal pollution and the potential ecological risk of a typical mining area: A case study in China. Process Safety and Environmental Protection. 2018;113:204–219.
68. Casado–Martínez MC, Forja JM, DelValls TA. A multivariate assessment of sediment contamination in dredged materials from Spanish ports. J Hazard Mater. 2009;163(2–3):1353–1359.
69. Afri‐Mehennaoui F, Sahli L, Mehennaoui S. Assessment of sediment trace metal level and biological quality of Rhumel river by using multivariate analysis. Environmetrics. 2004;15(5):435–446.
70. Sahli L, Belhiouani H, Burga Pérez KF, et al. Assessment of freshwater sediment quality: potential ecological risk and ecotoxicity tests as complementary approaches. Chemistry and Ecology. 2021;37(3):219–233.
71. Nazneen S, Singh S, Raju NJ. Heavy metal fractionation in core sediments and potential biological risk assessment from Chilika lagoon, Odisha state, India. Quaternary International. 2019;507:370–388.
72. Sfakianakis DG, Renieri E, Kentouri M, et al. Effect of heavy metals on fish larvae deformities: A review. Environ Res. 2015;137:246–255.
73. Oze C, Bird DK, Fendorf S. Genesis of hexavalent chromium from natural sources in soil and groundwater. Proc Natl Acad Sci USA. 2007;104(16):6544–6549.
74. Algül F, Beyhan M. Concentrations and sources of heavy metals in shallow sediments in Lake Bafa, Turkey. Sci Rep. 2020;10(1):11782.
75. Algül F, Beyhan M. Concentrations and sources of heavy metals in shallow sediments in Lake Bafa, Turkey. Sci Rep. 2020;10(1):11782.
76. Akhtar N, Syakir Ishak MI, Bhawani SA, et al. Various natural and anthropogenic factors responsible for water quality degradation: a review. Water. 2021;13(19):2660.
77. Chiba Wac, Passerini Md, Baio Jaf, et al. Seasonal study of contamination by metal in water and sediment in a sub–basin in the southeast of Brazil. Braz J Biol. 2011;71(4):833–843.
78. Mohammadi AA, Zarei A, Majidi S, et al. Carcinogenic and non–carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran. MethodsX. 2019;6:1642–1651.
79. Ahmed MF, Mokhtar MB. Assessing cadmium and chromium concentrations in drinking water to predict health risk in Malaysia. Int J Environ Res Public Health. 2020;17(8):2966.
80. Kubier A, Wilkin RT, Pichler T. Cadmium in soils and groundwater: A review. Appl Geochem. 2019;108:104388.
81. Yuan Z, Luo T, Liu X, et al. Tracing anthropogenic cadmium emissions: From sources to pollution. Sci Total Environ. 2019;676:87–96.
82. Sawe SF, Shilla DA, Machiwa JF. Lead (Pb) contamination trends in Msimbazi estuary reconstructed from 210 Pb–dated sediment cores (Msimbazi River, Tanzania). Environmental Forensics. 2021;22(1–2):99–107.
83. Ahmad W, Alharthy RD, Zubair M, et al. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci Rep. 2021;11(1):17006.