The work presented here aims to carry out a physical characterisation of soils to understand their hydrodynamic behaviour and estimate the susceptibility of each group of soils to microbiological pollution. The field work associated with the laboratory work consisted of monitoring the hydrodynamic behaviour of previously identified and selected well waters, measuring the permeabilities of the soil samples and analysing the bacteriological parameters of the sampled well waters. At the end of this work, the piezometric data show a dominant SE-NW and ENE-OSW flow direction. The water levels do not vary significantly between the wet and dry seasons. The granulometric analysis of the soils reveals CU>3 showing a spread out granulometry with very low permeabilities of the order of 1.58×10−7 m/s and moderately high permeabilities of the order of 1.86×10−5 m/s. The microbiological data show pollution of human origin with a high bacterial load in the rainy season represented by a maximum of 240 CFU of Escherichia coli exceeding the WHO standards for drinking water. The majority of the polluted samples come from soils developed on granites and basalts, which are the most susceptible to pollution, making the groundwater vulnerable.

  • Wells on ignimbritic formations are shallow in the rainy season compared to the dry season.

  • The permeability of soils on granitic formations is of the order of 10−5 m/s, classified as moderately high.

  • There is microbiological pollution of human origin in the town of Bafoussam.

  • Soils on granitic and basaltic formations are more susceptible to pollution and therefore make groundwater vulnerable to pollution.

Graphical Abstract

Graphical Abstract
Graphical Abstract

According to the Seine-Normandy Water Agency, a groundwater table is water within the saturated zone of an aquifer. Only water capable of circulating is part of the aquifer (Bugeat 2016). This groundwater circulates in a formation called an aquifer. An aquifer (from the Latin aqua fero: to carry water) is a permeable geological structure (a layer of land or rock) containing water, such as river alluvium, fissured granites, a limestone plateau, etc. (Gilli et al. 2008). The latter is a natural reservoir of freshwater that can be exploited (Castany 1980). However, water is an entity, which may be: precipitation water, surface water, ice, groundwater that participates in a perpetual cycle. The water contained in an aquifer circulates in the subsoil and feeds one or more springs, or is discharged in a hidden way into the sea, lake, river or other aquifer (Gilli et al. 2008). An aquifer is a hydrological, hydrodynamic system. It is characterised by its geometry, surface area, depth and by the intrinsic characteristics (lithology, porosity, permeability, fracturing, homogeneity) of the rock from which it is formed, (Gilli et al. 2008). Depending on their hydrodynamic conditions, most rocks and soils naturally contain a certain percentage of empty space that can be occupied by water or other fluids. This property is called the porosity of the medium and is expressed quantitatively as the ratio between the volume of pores (interconnected or not) and the total volume of the medium. There are three types of aquifers (porous, fissured, karstic). Depending on the nature, i.e., the origin of the voids containing the water, different types of aquifers can be distinguished: if the voids are intergranular spaces of sedimentary origin (pores in the strict sense) or diagenetic (ghost crystals), we speak of intergranular porosity or true porosity. These aquifers will be called porous aquifers. Porous aquifers are made up of boulders, pebbles, gravels, sands, siltstones, sandstones, chalk, biolithites, bioclastic carbonates, volcanic slag, granitic arenas (Gilli et al. 2008). These aquifers can therefore be made up of loose formations. Fissured aquifers are developed in magmatic rocks (granite massifs, gabbros, effusive rock flows, etc.), but also in metamorphic rocks (gneiss, micaschists, pelites, etc.) or sedimentary rocks (sandstone, carbonates, etc.). The base of the aquifer, called bedrock, is an impermeable hydrogeological formation. On the other hand, the upper limit of the aquifer is of three types: hydrodynamic with free fluctuation: free-table aquifer; geologically impermeable: confined-table aquifer; and geologically semi-permeable: semi-confined-table aquifer (Castany 1980). The possibility of accessing water from crystalline formations is expensive and almost inaccessible to households, who resort to the altered water table closer to the ground surface (Adoua et al. 2012). The majority of the inhabitants of the town of Bafoussam rely on groundwater, 67% of whom 30% use it as drinking water (Donfack et al. 2020). In the case of Bafoussam, these aquifers are degraded by human activities (Mpakam et al. 2009; Défo et al. 2015; Santsa & Ndjouenkeu 2018). Thus, these activities are the cause of waterborne diseases which represent the second cause of mortality and infant morbidity after malaria in the city of Bafoussam (Donfack et al. 2020). This raises the problem of the quality, especially the microbiological quality, of the water table in the town of Bafoussam, which is influenced by the proximity of the piezometric surfaces to the surface of the soil and therefore its intrinsic permeability. Hence the importance of this study which updates the microbiological quality of well water in the town of Bafoussam and the hydrodynamic properties of soils developed on the geological formations identified in the field. The aim of this study is to identify how the microbiological properties of groundwater evolve with the hydrodynamic characteristics of aquifer in the study area.

Location

Bafoussam is the capital city of the West region of Cameroon. Its geographical coordinates are: between latitudes 5°26′ and 5°31′ North; and longitudes 10°21′ and 10°30′ East; average altitude: 1,450 m (Mpakam et al. 2009). Located in the West Cameroon Highlands, Bafoussam has a population of 437,000 in 2020 (World-Bank 2020), and is situated at the intersection of three chiefdoms: Bafoussam in the South, Baleng in the North and Bamougoum in the West. Figure 1 below shows the location of the study site based on data from the INC 2011 processed in ArcGIS 10.8 software.

Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

Biophysical environment

The hydrography in the western highlands is marked by straight stretches of structural control and numerous waterfalls and cascades. The two largest collectors in the region are the Noun and the Nkam (Nono et al. 2009). A large part of the plateau is drained by the Noun River, which flows roughly north-south and separates the two ethnic groups Bamileke and Bamoun. The rivers that feed the castles of the city of Bafoussam are Métchié. Bameka and Nlem. (Kankeu & Yemmafo 2010).

The town of Bafoussam is made up of Cenozoic volcanic rocks and Pan-African granitoids aged between 500 Ma and 600 Ma (Kwékam et al. 2013). The water sampling points will be presented on the soil parent rock map of the area, which was produced by identifying, describing and georeferencing outcropping rocks by GPS and then processed in ArcGIS 10.8 software.

The soils are ferralitic, hydromorphic and not very developed. The ferralitic soils, developed on ancient bedrock and volcanic cover, are subdivided into subgroups according to the nature of the parent rock. The pedology in this sector as a whole is made up of hydromorphic, brown, red and greyish brown soils (Gountie et al. 2011). In the specific case of the study site, the natural vegetation has practically disappeared from the Bafoussam where man has completely transformed the landscape into a bocage characteristic of the country and its dispersed habitat to favour the growth of the town.

Assessment of hydrogeological characteristics

The hydrogeological parameters considered in this groundwater vulnerability study are mainly the characteristics including permeability, effective porosity, degree of saturation, void index and soil density of the saturated (SZ) and unsaturated (UZ) zones of the wells, followed by the piezometry study.

Soil physical parameters

The determination of these parameters starts in the field with the refreshment of road trenches and pit sections, observations and descriptions with the help of the Mundsel code of the soil samples, followed by their collection.

Texture

In the field, tactile assessment was done by kneading between the thumb, index and middle finger a few cm3 of fine soil as Flisch et al. (2017).

The samples were weighed in the laboratory in the wet state and the bulk densities were obtained by the following Equation (1).
formula
(1)
da=bulk density, Mh=mass of the wet sample and v=volume of the cylinder. The weights of the samples will then be determined using Equation (2) below:
formula
(2)

Porosity, void index, degree of saturation, specific gravity

These different parameters are calculated from laboratory measurements and presented in Table 1 below.

Table 1

Physical soil parameters

FactorsEquation
Water content (ω 
Porosity (η 
Void index (e 
Degree of saturation (Sr%)  
Specific gravity (Gs 
FactorsEquation
Water content (ω 
Porosity (η 
Void index (e 
Degree of saturation (Sr%)  
Specific gravity (Gs 

With Vs: volume of solid grains. Vv (volume of voids)=Va+Vw, V (total volume of the soil)=Vv+Vs, Ww is weight of water, Ws is weight of solid grains, W (total weight)=Ww+Ws, γs is the volume weight of solid grains, γw is the volume weight of water.

Intrinsic permeability

This factor was determined by sieving the samples collected after drying at 105°C in a Fisher oven (isotemp 500 series) for 24 hours. A battery of 8 sieves was used, including 2 mm, 1.4 mm, 710 μ, 355 μ, 180 μ, 90 μ, 60 μ, 45 μ. The granulometry-permeability relationship of (Kozeny 1927) on soils with known porosity was used. This relationship allowed the calculation of the intrinsic permeability in m/s. The relationship is as shown in Equation (3) next:
formula
(3)
With being a constant approximately equal to 1, di diameter in centimetres of the grain corresponding to a rejection of i %; k permeability estimated from the particle size analysis in m/s, xi weight percentage of the soils of diameter di, n the porosity. The equivalent permeabilities were determined by the relation of Equation (4) of the vertical flow extracted from the general Darcy's law. For here it is assumed that the pollutant flows from the surface to the free groundwater in a vertical direction.
formula
(4)
With hi the average height of the soil horizons, Heq the average depth of the wells. The transmissivity (T) is determined by the following Equation (5):
formula
(5)

Piezometric monitoring

A total of 220 wells were explored during the first field campaign and then a selection of 116 wells was made for the wells to be retained in the piezometric monitoring. The wells selected were those on each geological formation encountered at mid, foot and top of slope in each neighbourhood. Water depth measurements in the wells were carried out with a sound and light probe (100 m). The piezometric maps were developed based on the piezometric level data calculated at the 116 water points monitored between December 2020 and September 2021. In addition, the piezometric data corresponding to the dry season and the rainy season were grouped to produce the low water (December 2020 and February 2021) and high water (April to September 2021) maps of the study area. ARcGIS 10.8 software was used to plot the hydroisohypses using kriging as an interpolation method and an exponential variogram. The measurement committees were set up to obtain the piezometric data after each month of the measurement period. The piezometric data collected allowed the calculation of the piezometric level of each monitored water point according to Equation (6), allowing the establishment of piezometric maps:
formula
(6)
where NP is the piezometric level; Z is the altitudinal elevation of the water point and NS is the static level of the water point.

Inventories and resource selection

In the present study, only wells are of interest (these are the free groundwater most used by the population), and 161 wells were inventoried. The abandoned wells are located either on abandoned construction sites or because of the very poor quality of the water, which is marked by red colouring. In addition, a total of 38 positive water wells were selected for bacteriological analysis.

Sampling technique

Sampling for bacteriological analyses from 0.5-litre bottles previously made aseptic in an oven and kept in a cooler. In the field, the use of blue flame from ethyl alcohol and cotton maintained the aseptic nature of the vial during partial filling. The well water samples collected were transported to the laboratory of animal physiology and microbiology of the FASA (Faculty of Agronomy and Agricultural Sciences) of the University of Dschang for analysis of indicators of pollution of faecal origin and of certain other bacteria. Field observations and measurement of sample temperatures by an electrode probe for temperature were used.

Research into indicator germs of faecal pollution

The search for indicator germs of faecal pollution (total and faecal coliforms) were determined by various procedures. Prior to the identifications by filtration, the general liquid enumeration method by most probable number (MPN) is applied to determine the most probable number of coliforms in 100 ml of the sampled water.

Identification of faecal coliforms

First, the sample must be filtered through a cellulose ester membrane with a porosity of 0.45 μ. Fecal coliforms are identified by counting the colonies on petri dishes incubated at 44 °C for 24 hours. Specific faecal coliform bacteria determined include Enterobacteria spp, Escherichia coli spp, Streptococcus spp, Salmonella spp and Shigella spp:

  • For E. coli spp, the culture media used are Lactose agar with isoamyl alcohol and nitric acid as reagents. E. coli cause a distinct orange coloration in colonies;

  • For Salmonella spp, Shigella spp and Enterobacter spp the culture media are bright green and phenol red agar. The enrichment medium is tetrathionate broth. For selective isolation, Salmonella-Shigella agar was used. Thus colourless colonies with a black transparent centre represent Salmonella, bluish colonies represent Shigella and red colonies represent Enterobacter.

Identification of total coliforms

Total coliforms are identified by counting colonies on petri dishes incubated at 37°C for 24 hours. Specific total coliform bacteria determined include Staphylococcus spp, Vibrio spp and streptococcus spp:

  1. For Staphylococcus spp, the culture media and reagents were Chapman's Mannitol Medium. Inoculation was done by subculturing onto a Staphylococcus test broth. The presence of Staphylococcus is confirmed by microscopic examination after Gram staining of ordinary nutrient broth.

  2. For Vibrio spp, nutrient agar medium at pH 9 was used: the identification of Vibrio colonies are their white and fine colours.

  3. The test that can confirm the presence of faecal Streptococcus spp is to filter the sample onto the membrane and incubate at 37°C for 24–48 hours. After incubation, all colonies are red, pink or black in colour and are counted. Since faecal streptococci are more resistant than faecal coliforms, their determination constitutes additional information about faecal pollution. The identification of the specific bacteria is also done by membrane filtration, plating and enumeration.

Once the different coliform bacteria had been identified, principal component analyses (PCA) of the variables were carried out using SPSS 20 software for the two seasons (rainy and dry) to determine the two main dimensions that characterise the microbiological parameters of the study area.

Soil's granulometry

It follows from this description that soils from basaltic and ignimbritic formations are particularly rich in clay in their organo-mineral horizons, whereas those from granitic and gneissic formations contain more sand along their profiles. The results of the particle size analysis show a varied particle size distribution presented in Tables 2 and 3 of the cumulative percentage particle size analyses in the SZ and UZ below. In Table 2, Gran 1 and Gneiss 1 represent respectively the soil horizons on granitic and gneissic rocks in the SZ. Gran 2 and Gran 3 represent soil horizons on granitic bedrock in the UZ. Gneiss 2 and Gneiss 3 represent the soil horizons on gneissic bedrock in the UZ. In Table 3, Ignim 1 and Basalt 1 represent respectively the soil horizons on ignimbritic and basaltic rock in the SZ. Ignim 2 and Ignim 3 represent the soil horizons on ignimbritic bedrock in the UZ. Basalt 2 and Basalt 3 represent the soil horizons on basaltic bedrock in the UZ. Hydromorphic soils are represented by Hydro-gnim, Hydro-gra and Hydro-basa to identify hydromorphic soils on ignimbritic, granitic and basaltic bedrock respectively. these codes is valid for Tables 4 and 5.

Table 2

Results of grain size analysis in cumulative percentages in SZ and UZ on granite-gneiss

FormationsGranites
Gneiss
ZoneSZUZ
SZUZ
DiametersGran 1Gran 2Gran 3Gneiss 1Gneiss2Gneiss 3
99.52 98.88 98.88 99.68 99.52 99.75 
1.4 91.59 98.887 98.88 98.41 91.59 94.06 
0.71 66.44 95.65 93.214 89.72 66.44 71.01 
0.355 45.58 90.17 81.98 77.29 45.58 51.91 
0.18 29.36 62.02 64.50 67.13 29.36 35.55 
0.09 17.13 35.93 35.82 56.98 17.13 22.86 
0.06 8.55 13.21 10.96 22.62 8.55 11.77 
0.045 2.68 2.20 1.75 2.74 2.68 2.59 
FormationsGranites
Gneiss
ZoneSZUZ
SZUZ
DiametersGran 1Gran 2Gran 3Gneiss 1Gneiss2Gneiss 3
99.52 98.88 98.88 99.68 99.52 99.75 
1.4 91.59 98.887 98.88 98.41 91.59 94.06 
0.71 66.44 95.65 93.214 89.72 66.44 71.01 
0.355 45.58 90.17 81.98 77.29 45.58 51.91 
0.18 29.36 62.02 64.50 67.13 29.36 35.55 
0.09 17.13 35.93 35.82 56.98 17.13 22.86 
0.06 8.55 13.21 10.96 22.62 8.55 11.77 
0.045 2.68 2.20 1.75 2.74 2.68 2.59 
Table 3

Results of granulometric analysis in cumulative percentage in SZ and UZ on hydromorphic and volcanic soil

FormationsHydromorphic
Ignimbrites
Basalts
zoneHydromorphic
SZUZ
SZUZ
DiametersHydro-gnimHydro-graHydro-basaIgnim 1Ignim 2Ignim 3Basalt 1Basalt 2Basalt 3
99.64 98.29 99.18 99.86 99.43 98.55 99.16 99.78 99.07 
1.4 99.18 88.68 92.86 87.28 77.32 92.21 98.50 97.78 81.85 
0.71 89.39 52.54 71.43 73.91 60.92 81.78 89.70 95.54 48.70 
0.355 76.41 36.84 53.63 53.73 45.54 67.80 76.72 81.68 33.37 
0.18 58.146 23.31 32.99 36.79 31.18 43.71 58.46 62.00 21.51 
0.09 40.77 12.65 15.62 21.86 19.66 19.59 38.99 44.61 11.94 
0.06 18.06 6.44 5.83 10.34 9.31 8.250 17.27 10.60 5.454 
0.045 3.37 1.92 2.04 3.06 1.83 4.11 3.37 0.87 1.253 
FormationsHydromorphic
Ignimbrites
Basalts
zoneHydromorphic
SZUZ
SZUZ
DiametersHydro-gnimHydro-graHydro-basaIgnim 1Ignim 2Ignim 3Basalt 1Basalt 2Basalt 3
99.64 98.29 99.18 99.86 99.43 98.55 99.16 99.78 99.07 
1.4 99.18 88.68 92.86 87.28 77.32 92.21 98.50 97.78 81.85 
0.71 89.39 52.54 71.43 73.91 60.92 81.78 89.70 95.54 48.70 
0.355 76.41 36.84 53.63 53.73 45.54 67.80 76.72 81.68 33.37 
0.18 58.146 23.31 32.99 36.79 31.18 43.71 58.46 62.00 21.51 
0.09 40.77 12.65 15.62 21.86 19.66 19.59 38.99 44.61 11.94 
0.06 18.06 6.44 5.83 10.34 9.31 8.250 17.27 10.60 5.454 
0.045 3.37 1.92 2.04 3.06 1.83 4.11 3.37 0.87 1.253 
Table 4

Diameters used to calculate the permeability following Kozeny method

CodeFormationsThicknesses measuresD10D30D50D60D70D90
Hydro-gnim Hydromorphs 0.3 0.52 0.075 0.14 0.19 0.27 0.7 
Hydro-gra 0.3 0.079 0.25 0.63 0.82 1.7 
Hydro-basa 0.3 0.07 0.17 0.31 0.45 0.69 1.4 
Ignim 1 Ignimbrites SZ 0.06 0.14 0.3 0.44 0.62 1.7 
Ignim 2 Ignimbrites UZ 0.06 0.17 0.34 0.69 1.1 1.8 
Ignim 3 0.065 0.13 0.21 0.29 0.69 1.3 
Gneiss 1 Gneiss SZ 0.05 0.065 0.081 0.11 0.22 0.7 
Gneiss 2 Gneiss UZ 0.6 0.57 0.14 0.33 0.47 0.69 1.3 
Gneiss3 2.15 0.064 0.19 0.41 0.58 0.79 1.4 
Gran 1 Granites SZ 0.061 0.19 0.31 0.59 0.8 1.4 
Gran 2 Granites UZ 0.4 0.058 0.82 0.14 0.18 0.22 0.38 
Gran 3 0.6 0.06 0.082 0.13 0.17 0.22 0.6 
Basalt 1 Basaltes SZ 4.5 0.05 0.079 0.14 0.19 0.29 0.5 
Basalt 2 Basaltes UZ 1.4 0.06 0.078 0.12 0.17 0.24 0.55 
Basalt 3 0.6 0.084 0.3 0.62 0.9 1.2 1.7 
CodeFormationsThicknesses measuresD10D30D50D60D70D90
Hydro-gnim Hydromorphs 0.3 0.52 0.075 0.14 0.19 0.27 0.7 
Hydro-gra 0.3 0.079 0.25 0.63 0.82 1.7 
Hydro-basa 0.3 0.07 0.17 0.31 0.45 0.69 1.4 
Ignim 1 Ignimbrites SZ 0.06 0.14 0.3 0.44 0.62 1.7 
Ignim 2 Ignimbrites UZ 0.06 0.17 0.34 0.69 1.1 1.8 
Ignim 3 0.065 0.13 0.21 0.29 0.69 1.3 
Gneiss 1 Gneiss SZ 0.05 0.065 0.081 0.11 0.22 0.7 
Gneiss 2 Gneiss UZ 0.6 0.57 0.14 0.33 0.47 0.69 1.3 
Gneiss3 2.15 0.064 0.19 0.41 0.58 0.79 1.4 
Gran 1 Granites SZ 0.061 0.19 0.31 0.59 0.8 1.4 
Gran 2 Granites UZ 0.4 0.058 0.82 0.14 0.18 0.22 0.38 
Gran 3 0.6 0.06 0.082 0.13 0.17 0.22 0.6 
Basalt 1 Basaltes SZ 4.5 0.05 0.079 0.14 0.19 0.29 0.5 
Basalt 2 Basaltes UZ 1.4 0.06 0.078 0.12 0.17 0.24 0.55 
Basalt 3 0.6 0.084 0.3 0.62 0.9 1.2 1.7 
Table 5

Hydrogeological parameters in SZ and UZ

CodeFormationsCU=d60/d10Water contentsPorositiesVoid indicesDegrees of saturationDensity kg/m3Keq in m/sTeq in m2/s
Hydro-gnim Hydromorphic 0.36 0.0263 0.2934 0.5602 0.0953 11643.258 1.861×10−5 3.165×10−5 
Hydro-gra 10.37 0.0325 0.3907 0.6412 0.1189 17867.841 1.364×10−6 2.319×10−6 
Hydro-basa 6.42 0.0232 0.2798 0.3885 0.0840 12663.283 2.816×10−7 4.788×10−7 
Ignim 1 Ignimbrites SZ 7.33 0.0381 0.4592 0.8490 0.0980 16073.068 1.580×10−7 2.684×10−7 
Ignim 2 Ignimbrites UZ 11.5 0.0271 0.3258 0.4833 0.1056 15665.035 
Ignim 3 4.46 0.0418 0.5029 1.0117 0.1162 18655.373 
Gneiss 1 Gneiss SZ 2.2 0.0427 0.5135 1.0556 0.0919 15893.305 4.444×10−7 7.554×10−7 
Gneiss 2 Gneiss UZ 9.6 0.0353 0.4248 0.7385 0.0890 14674.913 
Gneiss 3 0.82 0.0402 0.4834 0.9359 0.0966 16147.255 
Gran 1 Granites SZ 9.67 0.04 0.0495 0.5961 1.4757 0.086 1.517×10−6 2.579×10−6 
Gran 2 Granites UZ 2.82 0.0566 0.6813 2.1381 0.0630 14127.065 
Gran 3 0.04 0.0495 0.5961 1.4757 0.0860 16007.440 
Basalt 1 Basalts SZ 3.8 0.0560 0.6743 2.0700 0.0769 15693.569 8.126×10−7 1.381×10−6 
Basalt 2 Basalts UZ 2.83 0.0447 0.5377 1.1631 0.0946 16446.860 
Basalt 3 10.71 0.0379 0.4560 0.8381 0.0949 15679.302 
CodeFormationsCU=d60/d10Water contentsPorositiesVoid indicesDegrees of saturationDensity kg/m3Keq in m/sTeq in m2/s
Hydro-gnim Hydromorphic 0.36 0.0263 0.2934 0.5602 0.0953 11643.258 1.861×10−5 3.165×10−5 
Hydro-gra 10.37 0.0325 0.3907 0.6412 0.1189 17867.841 1.364×10−6 2.319×10−6 
Hydro-basa 6.42 0.0232 0.2798 0.3885 0.0840 12663.283 2.816×10−7 4.788×10−7 
Ignim 1 Ignimbrites SZ 7.33 0.0381 0.4592 0.8490 0.0980 16073.068 1.580×10−7 2.684×10−7 
Ignim 2 Ignimbrites UZ 11.5 0.0271 0.3258 0.4833 0.1056 15665.035 
Ignim 3 4.46 0.0418 0.5029 1.0117 0.1162 18655.373 
Gneiss 1 Gneiss SZ 2.2 0.0427 0.5135 1.0556 0.0919 15893.305 4.444×10−7 7.554×10−7 
Gneiss 2 Gneiss UZ 9.6 0.0353 0.4248 0.7385 0.0890 14674.913 
Gneiss 3 0.82 0.0402 0.4834 0.9359 0.0966 16147.255 
Gran 1 Granites SZ 9.67 0.04 0.0495 0.5961 1.4757 0.086 1.517×10−6 2.579×10−6 
Gran 2 Granites UZ 2.82 0.0566 0.6813 2.1381 0.0630 14127.065 
Gran 3 0.04 0.0495 0.5961 1.4757 0.0860 16007.440 
Basalt 1 Basalts SZ 3.8 0.0560 0.6743 2.0700 0.0769 15693.569 8.126×10−7 1.381×10−6 
Basalt 2 Basalts UZ 2.83 0.0447 0.5377 1.1631 0.0946 16446.860 
Basalt 3 10.71 0.0379 0.4560 0.8381 0.0949 15679.302 

The readings on the various particle size curves made it possible to determine the effective diameters of the soil samples analysed, which are presented in the following Table 4. It appears that the soils with the smallest particles from the depth to the surface of the wells are those on basaltic and gneissic bedrock and hydromorphic soil. The soils with particle diameters of around 2 mm are those on granitic and ignimbritic bedrock.

Table 5 shows the results of the hydrogeological parameters of the soils in the unsaturated and saturated zones of the free aquifer of the town of Bafoussam. The table shows that hydromorphic soils have the highest permeability values, particularly those on granite. The permeability results obtained are similar to those of Ngouh et al. (2020), which are of the order of 10−5 and 10−7 on ferralitic soil in the city of Yaoundé in Cameroon, carried out according to the method of Porchet (1931). These values are slightly different from those of Nsangou et al. (2022) 9.2×10−6 and 8.3×10−4 m/s who also used the porchet method in the same locality, but this time specifically on sandy soils. The equivalent permeability (Keq) of soils on ignimbrites is also good because the particle size of the saturated zone is slightly larger than that of the unsaturated horizons.

Figures 2 and 3 present respectively the permeabilities for each horizon along the profile and the equivalent permeabilities along each profile.

Figure 2

Permeability according to each soil horizon along the profile.

Figure 2

Permeability according to each soil horizon along the profile.

Close modal
Figure 3

Equivalent soil permeability along the soil profiles.

Figure 3

Equivalent soil permeability along the soil profiles.

Close modal

The peak observed on the organo-mineral horizon of the soil on gneiss is due to its very high sand enrichment along the horizon from the soil surface. This may be related to the fact that the well was drilled on a site where the presumed organo-mineral horizon was subjected to inputs of more particulate material such as low-grade sands which generally have permeabilities of the order of 10−5, in agreement with the work of Milandou (2018) in the Mansimou and Mayanga districts in the south of Brazzaville (Congo), which present permeabilities of the same order (2×10−5 m/s) indicating a good aptitude for water infiltration, with surface formations consisting of 93% sand.

In Figure 3 it can be seen that soils on granitic formations are the most permeable, followed by soils on gneissic formations, which differs from the observations made in Figure 2, where soils on gneissic formations have the highest permeability values. The observations in Figure 2 showing the particle size of the soil samples per horizon are quite different from those in Figure 3 of the equivalent permeability per profile because the relationship for the calculation of the intrinsic permeability per horizon does not include the thickness of the layers impacting on the permeability values when they have to become equivalent permeabilities.

Piezometric characteristics of aquifers

The averages of the piezometric data from the dry season and the rainy season were used to produce Figure 4, which shows the variation in piezometric levels between the high and low water periods.

Figure 4

Variation in piezometric levels (PL) between the dry and rainy seasons.

Figure 4

Variation in piezometric levels (PL) between the dry and rainy seasons.

Close modal

It is noticeable that the water level has increased slightly in the rainy season, naturally due to the infiltration of rainwater to recharge the water table. At some piezometers the water level is higher in the dry season than in the rainy season. These are specifically the wells in the Kamkop military camp area, which are located in the ignimbritic formations. This absence of water in the rainy season could be linked to the fact that the recharge of the water table is done laterally from another region or even another country, arriving slowly due to the low permeability of the soil. This lateral recharge would be similar to that observed by Huang et al. (2021) in the case of the study of groundwater recharge in northern China, where groundwater is laterally recharged by the waters of the Anyanghe rivers. The piezometric data processed allowed the production of the piezometric maps in Figures 5 and 6 below, respectively for the low and high waters of the study area.

Figure 5

Piezometric map of low water in the study area (November – March 2021).

Figure 5

Piezometric map of low water in the study area (November – March 2021).

Close modal
Figure 6

Piezometric map of high water levels in the study area (March–September 2021).

Figure 6

Piezometric map of high water levels in the study area (March–September 2021).

Close modal

Looking at these two piezometric maps, it can be seen that there is no significant difference in the overall piezometric surfaces and therefore in the average piezometric levels between the dry and rainy seasons. Moreover, the paired Student test on these two averages shows a P-value=0 at a threshold of 5%. This confirms the non-significance of the piezometric averages between the two seasons.

Microbiological data

The analysis of the water using the liquid enumeration method resulted in Tables 6 and 7 of the descriptive statistics of the most probable numbers of coliforms in 100 ml per water sample in the dry and rainy seasons respectively.

Table 6

Most likely number of coliforms in the dry season (December 2021)

StatistiqueNMinMaxMeanDeviation
Mean count 38 160 46.87 50.788 
StatistiqueNMinMaxMeanDeviation
Mean count 38 160 46.87 50.788 
Table 7

Most likely number of coliforms in the rainy season (September 2021)

StatistiquesNMinimumMaximumMeanDeviation
Mean count 38 220 52.71 53.155 
StatistiquesNMinimumMaximumMeanDeviation
Mean count 38 220 52.71 53.155 

The average values for the two seasons show that all the free groundwater in Bafoussam is microbiologically polluted by coliforms. The water quality is of poor physical quality in the dry season compared to the rainy season but microbiological pollution is higher in the rainy season than in the dry season. This poor water physical quality in the dry season may be due to the increased of frequent withdrawing of water from the wells in the absence of rain, which increases the lateral movement of water and consequently the turbidity in traditional wells; colour and average temperature (22.3°C in the dry season and 20.13°C in the rainy season). On the other hand, the poor microbiological quality of the water in the rainy season is related to the immediate infiltration of surface water into the undeveloped wells, which constitute the majority of the wells in the sector. The classification of the pollution categories according to the most likely number of coliforms and the physical quality of the water in the whole sector shows an unacceptable pollution risk of 78.94% for the dry season and 71.9% for the rainy season, thus confirming the poor physical quality of the water in the dry season. Tables 8 and 9 below present the descriptive statistics of the colony units formed per 100 ml of well water samples in the city of Bafoussam in the dry and rainy seasons following the membrane filtration method.

Table 8

Descriptive statistics of dry season colony units (CFU)

BacteriaEnterobacteria sppE. coliStreptococcus sppSalmonella sppShigella sppStaphylococcus sppVibrio spp
Min 
Max 300 150 240 80 15 90 
Mean 95.84 50.10 39.55 23 3.15 20.94 95.84 
BacteriaEnterobacteria sppE. coliStreptococcus sppSalmonella sppShigella sppStaphylococcus sppVibrio spp
Min 
Max 300 150 240 80 15 90 
Mean 95.84 50.10 39.55 23 3.15 20.94 95.84 
Table 9

Descriptive statistics of rainy season colony units (CFU)

BacteriaEnterobacteria sppE. coliStreptococcus sppSalmonella sppShigella sppStaphylococcus sppVibrio spp
Min 10 
Max 350 240 300 120 28 94 12 
Mean 109.74 65.13 46.82 27.95 6.16 22.42 2.53 
BacteriaEnterobacteria sppE. coliStreptococcus sppSalmonella sppShigella sppStaphylococcus sppVibrio spp
Min 10 
Max 350 240 300 120 28 94 12 
Mean 109.74 65.13 46.82 27.95 6.16 22.42 2.53 

This is due to the fact that the samples were taken in the middle of the rainy season when the groundwater is not yet diluted. Similar observations were made by Ngouh et al. (2020) in the city of Yaoundé with a maximum of 280 CFU/100 ml in the rainy season which is not very close to that obtained for this study because his study area does not include areas of intense agricultural practices such as in the city of Bafoussam where farms are observed in the city and the uncontrolled use of manure and droppings in agriculture. On the whole, a CF/SF ratio of >4 was observed for the samples, confirming pollution of human origin (George et al. 2021). If we want to compare the values of these coliforms with the WHO standard for drinking water, which is 0 CFU, we can say that the well water in the town of Bafoussam is of very poor microbiological quality.

The principal component analyses for the two seasons are presented in Figures 7 and 8 below

Figure 7

Principal component analysis of dry season coliform units.

Figure 7

Principal component analysis of dry season coliform units.

Close modal
Figure 8

Principal component analysis of rainy season coliform units.

Figure 8

Principal component analysis of rainy season coliform units.

Close modal

In the case of the dry season, it can be seen after factoring the variables that two large principal components are generated with variables that are correlated with each other. In particular, the coliform component is made up of the variables E.coli and E.enterobacteria, both of which have a strong correlation due to their closeness and their position being restricted to other genera of bacteria. The second component is the bacterial component which associates the genera of bacteria such as Salmonella, Shigella, and Staphylococcus which are more or less correlated with each other. Component 1 consists of bacteria from soil and vegetation, while component 2 consists of coliform bacteria from organic matter, particularly faeces. Figure 8 shows that there is microbiological pollution in the town of Bafoussam and many samples have both high coliform and bacterial counts. The samples with the highest microbiological pollution in faecal and total coliforms are NP198, NP74, NP38 and NP213, all of which are located on permeable to moderately permeable soils. As for the rainy season, almost the same thing is observed as in the dry season with the two by two correlations between the variables enterobacteria and E-coli constituting component 1 characterising the coliforms and the two by two correlations between the variables salmonella, shigella and staphylococcus constituting component 2 characterising the specific bacteria. It can be seen that very few samples (almost 5.5%) represented by NP196, NP74 have a very high microbiological load of specific bacteria, although the number of samples with microbiological loads of coliforms is considerable. These samples (NP213, NP214, NP36, NP75, NP173, NP69, NP111, NP1, NP46, NP55, NP19, NP20, NP27, NP17, NP55) represent almost 45% of the sample points, are mostly located on granitic bedrock soils and some on basaltic and ignimbritic bedrock soils. This distribution of high microbiological pollution points on granitic basement soils with high and moderate permeability would confirm that these are susceptible to pollution, making the free groundwater vulnerable to pollution. This can be seen in the following Figure 9 showing the projection map of the well water sampling points onto the soil parent rock types. Compared to the study by Nayebare et al. (2022) soils on sedimentary rock saprolites dominate their study area at 93% and are more susceptible to pollution, making groundwater vulnerable with a maximum concentration of the E.coli order of 319 CFU.

Figure 9

Well water sampling map of source rock types and soils.

Figure 9

Well water sampling map of source rock types and soils.

Close modal

The objective of this work is to show that hydrogeological characteristics, particularly soil permeability, influence the microbiological quality of groundwater in the town of Bafoussam. The study highlighted the fact that soils on granitic formations are more susceptible to pollution and to the vulnerability of groundwater in the town of Bafoussam than basalts and ignimbrites, through the piezometric monitoring of existing wells, the granulometric analysis of soils and the microbiological analysis of water. It was found that water depths do not vary significantly between the rainy and dry seasons. Water quality is more degraded in the rainy season than in the dry season. This degradation of the bacteriological quality of the water is associated with an uncontrolled urbanisation plan that could deteriorate further in the coming decades. The contribution of this study is to have shown that the susceptibility of soils to pollution is one of the causes of groundwater vulnerability in the city of Bafoussam by establishing a decision-making tool that presents the parcels of the study area and their respective permeabilities. The municipal authorities must establish an urbanisation plan that puts environmental protection first and implement it. It should raise awareness of the poor quality of well water and demand adequate treatment before consumption.

All relevant data are included in the paper or its Supplementary Information.

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