The Noun catchment area (NCA) is located at latitudes 4˚80'N to 6˚60'N and longitudes 10˚10'E to 11˚05'E (Figure 1).

Figure 1. Location, hydrological, and sampling map of the study area. (a) Cameroon in Africa; (b) NCA in Cameroon; (c) NCA location; (d) hydrological and sampling map of NCA.

It drains three regions of Cameroon and ten departments. The watershed covers several major agricultural zones in Cameroon, including Santa, Batcham, Galim, Bamougoum-Baleng, Foumbot, and Bamendjing.

Due to its geographical location, the NCA is subjected to two principal climatic zones: the tropical mountain climate in the west and the equatorial transition climate at its outlet at Bayomen (Central Cameroon). Generally, it is a wet tropical climate, with a 3-month dry season and a 9-month rainy season. According to the work of [7]), this zone is covered by isohyets of 1800 and 1900 mm. It stretches from Dschang to Foumban, and from Bamenda to Nkambé, and covers the mountains of the western regions. It is characterised by much lower temperatures than the rest of Cameroon (average monthly temperatures range from 23.79˚C to 20.89˚C, and the average inter-annual temperature is 21.3˚C) and by an oceanic influence, which results in high rainfall. From a hydrological point of view, the Noun catchment area rises from Mount Oku in the northwest and drains the Ndop plain in the North West region, ending up in a marshy basin. Its main tributaries are: Mifi-Nord, Mifi-Sud, Nkoup, Ngam, and Nde before its confluence with the Mbam. Through the Bamenjing reservoir, the Noun regulates the Sanaga with a reserve of nearly 1.85 billion cubic meters (m³) ([8]).

The geology of the NCA is diverse and composed of different lithological types, bearing witness to numerous geodynamic activities (tectonics, faults, etc.), and above all, its position along the Cameroon line. The latter is a tectonic and plutono-volcanic megastructure stretching between the Lake Chad basin and the Gulf of Guinea over a distance of around 900 km ([15]; [38]; [31]). In the northern part, metamorphic rocks such as trachytes, rhyolite, and granite are predominant. In the southern part of the basin, magmatic rocks such as micaceous quartzites and embrechite gneisses, and anatexites in the south-eastern part of the basin include volcanic rocks, mainly aphyric basalts, anatexites, and aphyric basalts. Several types of soil develop on these different geological formations, specifically ferralitic soils resulting from the alteration of eruptive basaltic and volcanic rocks; red ferralitic soils formed from an accumulation of laterite on metamorphic rocks; hydromorphic soils, formed in areas subject to permanent or frequent water saturation; and alluvial soils, originating from sediments transported by rivers.

To achieve this study, the sampling was made in January 2025 because this month represents the transition period between the rainy and dry seasons. Then, nineteen samples of surface water were taken using the conventional surface water sampling technique described by several authors ([28]; [18]; [24]; [5]; [11]). This method involves manual sampling using a bucket fitted with a rope at a depth of 1 meter in the center of the river, preferably where the current speed is high enough to allow good homogenization of solid particles and dissolved matter. The samples were taken in 1.5-litre plastic bottles that had been cleaned and rinsed with distilled water and then three times with the water to be sampled. The bottles were filled to the brim, sealed to prevent gas exchange, labelled and stored in a cooler at around 4˚C. These were sent to the Laboratoire d’Analyses Géochimiques des Eaux (LAGE/IRGM) at Nkolbisson in Yaoundé, Cameroon. These samples were followed by in-situ measurements of physical parameters such as temperature, pH, electrical conductivity (EC), total dissolved solids (TDS) and salinity using an ORTON multi-parameter calibrated with appropriate solutions. Turbidity was also measured in situ using a turbidimeter that had also been calibrated with 00 and 100 NTU solutions. For the laboratory analyses, the raw water samples were first separated into two phases: a particulate phase for estimating suspended solids (SS) and a dissolved phase (filtrate), using the frontal filtration method with an electric vacuum pump and 0.45 μm millipore cellulose filters (NALGENE filter) of 0.45 μm. The samples were oven-dried at 105˚C for three hours and weighed. The filtrate obtained for each sample was used to determine alkalinity, major cations and anions (Ca²⁺, Mg², Na⁺, K⁺, NH 4 + , Cl⁻, NO 3 − PO 4 2 + SO 4 2 − F⁻ HCO 3 − ). For this research, pearson’s correlation matrix was used to determine the relationship between parameters and several indices. Furthermore, the correlation matrix is a popular method for determining how parameter pairs influence water quality ([24]; [35]).

The results obtained from the laboratory in this study were processed using the standard method of hydrochemistry for Statistical Analysis, and the calculation of the Water Quality Index (WQI). In addition, the geostatistical approach for spatialization of WQI, CE, Salinity, HCO 3 − , and PO 4 2 − was also achieved ([24]; [35]; [29]; [19]; [32]; [20]).

2.5.1. Statistical Analysis

Statistical analysis consisted to determine a descriptive parameters as minimum, maximum and mean among chemical values of different samples, for assessing water chemical quality with respect to drinking water standards ([34]). This calculation was followed by determination of Pearson correlation coefficient, using Statistica 7.0 software to establish relationships among many variables and their hydrochemical roles ([10]; [6]). This aims to analyze the degree of dependence between variables in the form of a matrix (Belkhiri and Mouni 2012). The correlation coefficient may have a value from (−1) to (+1). (+1) means a perfect positive relationship exists between the variables, while (−1) indicates a perfect inverse relationship ([17]; Kazi et al., 2009), and a zero value (0) indicates the absence of a relationship between variables ([3]; [17]; [36]). Pearson correlation values of r > 0.7 are generally considered highly correlated, while r values ranging from 0.5 to 0.7 are moderately correlated. The dependence of the variables on each other is represented by the Pearson correlation matrix.

2.5.2. Water Quality Index (WQI) Calculation

The Water Quality Index (WQI) is a quantification that defines the main reasons for inconsistencies in water quality ([30]; [9]; [26]). It was calculated on eleven (11) water quality parameters (pH, EC, Mg²⁺, Ca²⁺, Na⁺, NH 4 + , K⁺, Cl⁻, HCO 3 − ⁻, SO 4 2 − , F⁻) to ascertain the suitability of surface water in the study area for drinking purposes ([25]; [32]; [20]). The WQI is calculated as follows (Table 1; Equation (1)): each chemical parameter was assigned different weights (wi) on a scale of 1 (least effect on water quality) to 5 (highest effect on water quality). This is based on their perceived effects on primary health and according to their relative importance in drinking water quality ([25]; [32]; [20]). The highest weight of 5 was assigned to parameters that have critical health effects and whose presence above the critical concentration limits could limit the usability of the resource for domestic and drinking purposes ( NO 3 − ); the minimum weight of 1 was assigned to K⁺ because of its insignificant role in water quality assessment. Other parameters such as pH, EC, TDS, salinity, HCO 3 − , Cl⁻, SO 4 2 − , F⁻, Ca²⁺, Mg²⁺, were assigned weights between 2 and 4 based on their relative significance in water quality evaluation ([25]). For this study, the weighted arithmetic WQI was calculated as follows ([2]; [21]):

where:

Si is the WHO standard for drinking water of parameter (i).wiis the relative weight (Wi); the values of each parameter are given inTable 1.qi represents the quality rating scale,Ci is the observed value of each chemical parameter in mg/L.and Ci is the concentration of parameter (i).

The computed WQI values of data from Table 2 are classified into five categories as follow:

WQI ≤ 25%, Excellent water;25 < WQI < 50%, Good Water;50 < WQI < 75%, Poor water;75 < WQI < 100%, Extremely poor Water;100 ≤ WQI, Unsuitable for drinking purposes.

Table 1. Weight and relative weight of each parameter used for the WQI calculation.

A statistical summary of physicochemical parameters has been done. Thus, the pH of overall samples varied from 7.32 to 8.85 with a mean of 8.18. Most of the samples from the NCA fall within the permissible limits of [34] (6.50 - 8.50) and ANOR (6.5 - 9), except for 3 samples from Koup in Baigom, Bameka, and Noun in Dioma, which are slightly above the standard limit. This shows that most of the sampled water was basic, reflecting the highly intensive agricultural activity zones dominated by the use of chemical fertilizers. The pH values observed in NCA are generally higher than those observed in other forest areas of southern Cameroon ([27]; [32]). This factor generally influences the activity of enzymes, the solubilization and uptake of specific ions, and the distribution of biodiversity in aquatic environments ([1]). The values of pH of the water samples analyzed in this research fell within the suggested range ([33]). Electrical conductivity (EC) is a material’s capacity to conduct an electric current; a high EC value suggests that salts have accumulated in surface water ([32]). The range of electrical conductivity (EC) measured in the NCA samples was between 35.50 and 258 µs/cm (Table 2), with a mean of 61.69 µs/cm. All the water samples of the study area show low to medium mineralization (up to the permissible limit prescribed by [34]. This type of water is very weakly to weakly mineralized ([12]), like most of the water in the woody savannah and forested parts of western and southern Cameroon respectively, which flows on a plutono-metamorphic basement, contrary to water in the sedimentary milieu which is very mineralized ([24]; [11]). The quality of irrigation and drinking water is determined by the total dissolved solids (TDS) level. It is also crucial for maintaining a balanced cell density in aquatic life ([32]; [20]). The range of total dissolved solids in the study area varies between 25 mg/l and 182 mg/l, with an average of 63.35 mg/l (Table 2). The WHO does not set health guidelines for TDS, but a TDS concentration above 1000 mg/l may affect the acceptability of water for drinking purposes. These values indicate that the water in the study area is classified as fresh (TDS < 1000 mg/L) according to [20]. According to [32], temperature is a critical factor governing species’ distribution, growth, survival, and reproduction within an ecosystem. The typical ambient temperature of the Noun river is reflected in the temperature values of water samples, which vary from 17˚C (E7Ba) to 24˚C (E19Mk), with an arithmetic mean of 20.91 ˚C (Table 2).

Table 2. Statistical values of physicochemical parameters of samples in the study area.

NM = not mentioned.

The major cations evaluated are sodium, calcium, potassium, magnesium, and ammonium, whereas anions are bicarbonate, chloride, nitrate, sulphate, and fluoride. The order of abundance of cations in the water samples of the study area is as follows: Ca²⁺ > Mg²⁺ > NH 4 + > Na⁺ > K⁺, and anions were in the decreasing order of HCO 3 − > PO 4 2 + > Cl⁻ > NO 3 − > SO 4 2 − > F⁻. Ca²⁺ in the samples ranges from 7.54 to 13.67 mg∙L⁻¹ with mean values of 10.06 mg∙L⁻¹. Mg²⁺ in samples ranges from 5.36 to 9.83 with an arithmetic value of 7.66 mg∙L⁻¹. NH 4 + in samples varies from 0.01 to 5.12 mg∙L⁻¹ with mean values of 0.55 mg∙L⁻¹. Na⁺ in samples varies from 1.10 to 5.10 mg∙L⁻¹, with mean values of 2.60 mg∙L⁻¹. Sodium in the study can be attributed to silicate weathering, mineral dissolution, and anthropogenic ([27]). All samples are noted below the permissible limit of [34] and ANOR. The release of Ca²⁺ and Mg²⁺ into the water is made through the ion exchange process during water–rock interaction and mineral dissolution. The K⁺ concentrations in the samples vary from 0.10 to 4.80 mg∙L⁻¹ with mean values of 2.00 mg∙L⁻¹. The main cause of K⁺ in the water is K^‐ minerals such as micas and orthoclase or from anthropogenic sources of potassium, such as potash fertilizers due to the agricultural practices in the study area. In addition, the HCO 3 − is the most abundant anion in the study area. It ranges from 2.01 to 199.84 mg∙L⁻¹ and the mean value is 44.40 mg∙L⁻¹. The abundance of HCO 3 − is an indication of the mineral dissolution process ([27]). The PO 4 2 + in water samples varies from 0.05 to 1.42 mg∙L⁻¹, with the arithmetic mean of 0.46 mg∙L⁻¹. The Cl⁻ ranges from 0.12 to 1.32 mg∙L⁻¹ with an average of 0.37 mg∙L⁻¹. NO₃⁻ concentration varies from 0.01 to 0.95 mg∙L⁻¹ with an average of 0.14 mg∙L⁻¹. The highest nitrate value during this study is recorded from E15NT in Tonga, another agricultural zone where agricultural practices used intensive fertilizers. Thus, this can be attributed to the higher values of nitrate in the water. The same result was obtained by [4] in the east coast of Tamil Nadu and Puducherry in India, and also by [27] in Thoothapuzha River Basin, Kerala, South India. The SO 4 2 − concentration in the samples varies from 0.12 to 0.85 mg∙L⁻¹ with mean values of 0.48 mg∙L⁻¹. The watery old soil consisting of sedimentary materials in this part of the study area can be the main source of all these elements.

Relationships between the major hydrochemical variables were assessed by the Pearson correlation coefficient, which is either positive or negative (Table 3). Then, the values presented three categories of relationships:

Table 3. Correlation matrix of physico-chemical parameters in the water of the study area.

Variables with correlation values greater than 0.90 indicate a high correlation. Therefore, we have a high and strong positive correlation of TDS with CE, Salinity, HCO 3 − , between CE with Salinity, HCO 3 − , and between HCO 3 − and Salinity. Variables with correlation values ranging between 0.50 and 0.70 indicate a moderate correlation. According to Table 3, we have a moderate positive relationship between NH 4 + with HCO 3 − and Salinity, and between PO 4 − and Salinity. Variables with correlation values less than 0.50 indicate a weak correlation, such as K⁺ with other variables like Salinity, TDS, CE, and Mg⁺. These correlation values among variables (Table 3) mean that paired variables have a strong to moderate influence on water mineralization ([6]). Moreover, these correlations show that the order of contribution in water mineralization by ions is as follows: HCO 3 − , Ca²⁺, SO 4 2 − , Cl⁻. The strong to moderate positive relationship between major ions implies that these ions were derived from the same source, which is the leaching of some materials in this area. In addition, the strong correlation between salinity and the electrical conductivity implies the presence of salt in the study area.

The water quality indices vary between 4.065 and 215.9, with an average of 28.31. Three categories types of water are recorded for the 19 samples: water of excellent quality, water of poor quality, and water unsuitable for drinking purposes ([20]). According to the water quality index classification, 10.53% of this water falls into Class V: unsuitable for drinking purposes ([26]), and is represented by the water from the E9KM and E10KG samples, which are drained by one of the most important agricultural basins of the study area. In contrast, 84.21% of the waters are of excellent quality, consisting of the E1BB, E2Mb, E3MN, E4Ke, E5NJ, E6KB, E7Ba, E8PN, E11Ma, E12NZ, E14Mr, E15NT, E18NM, E19Mk, E21ND, and E22BY samples. Moreover, 5.26% have poor water quality (E20NM) (Table 4).

Table 4. WQI values location for all the sampling sites.

The unsuitable and poor water quality represented 15.79% of the entire water in the research zone. These waters are subject to mineralogical and metallic pollution from human activities, probably agriculture, which is the main activity in that area. Therefore, they can be useful for household chores such as washing, bathing, washing dishes, and laundry. For consumption, this water requires prior treatment to comply with the standards prescribed by the WHO ([33]) and ANOR ([20]). The spatial distribution map of WQI shows the highest concentration in the central part of the study area (Figure 2).

Figure 2.Spatial distribution of WQI values in the study area.

This suggests that the water in this part needs to be seriously treated before any use in conformity with the standards prescribed by the WHO and ANOR.

The spatial distribution of HCO 3 − , EC, Salinity, and PO 4 − in water is displayed in Figure 3. In these figures, yellow and red colors denote higher concentrations, and blue denotes lower concentrations. Since EC is a material’s capacity to conduct an electric current, a high EC value suggests that salts have accumulated in surface water. The EC values for this investigation vary from 35.5 µS/cm to 258 µS/cm (Table 2). The northern and central parts of the study area exhibit high EC values, whereas the southern part does not. In contrast, the lowest values are dispersed unevenly throughout the region, according to the spatial distribution map (Figure 3).

Fresh water availability was indicated by higher EC values in the north and central part of the study area and lower values in the south and their surroundings (Figure 3). As shown by the map, salt is present everywhere in that zone since the conductivity of water depends on the amount of salt in the area. The PO 4 − is present in the northern part of the study area, whereas the HCO 3 − is mainly found in the center of the study area.

Figure 3.Spatial distribution of HCO 3 − (A), EC (B), PO 4 − (C), and Salinity (D) in the study area.