Abstract
The purpose of this work was to show that the change in land use impacts the quality of free groundwater and therefore the health of the population in the city of Bafoussam. Land use was dominated by housing (about 42%) followed by agricultural areas (33%) and green space. The socio-economic diagnosis by the guided survey showed that 62.5% of the population consumes CAMWATER water with malaria as the dominant water-related disease. The collection of water samples analysed by the colorimetric and photometric assay has allowed us to obtain a dominant water facies of the chloride and sulphate–calcium–magnesium type. The mapping of areas potentially vulnerable to pollution by ArcGIS 10.8 software presents the low-lying areas, downstream of agricultural sites, close to industries and neighbourhoods with strong urban disorder as the most vulnerable to pollution and therefore retained as actors of diffuse groundwater pollution in the city of Bafoussam. Thus, the change in land use and the increase in agglomeration degrade the quality of groundwater in this city.
HIGHLIGHTS
The quality of Bafoussam's groundwater is deteriorating as time goes by.
The growing urbanization of the city of Bafoussam is uncontrolled and anarchic.
The industrial and demographic development influences the quality of groundwater in the city of Bafoussam.
Concentrations of chemical elements exceeding those recommended by the WHO are mainly related to the overall hygiene quality of the population.
INTRODUCTION
West Cameroon is vast and made up of highlands (Bamoun Plateau, Bamboutos Mountains, Bamenda, Oku and Bana) and plains (Noun and Ndop), where many populations consume natural (raw) water (Nono et al. 2009). For most of Cameroon's cities, these waters are degraded by the presence of latrines, rubbish dumps and farms, and the emptying of sanitation facilities into the environment, especially into streams (Mpakam et al. 2006; Nguema et al. 2021; Tontsa et al. 2023). Groundwater quality, particularly in wells, is further threatened in Bafoussam by urban disorder due to anarchic land occupation (Yemele et al. 2020), which is accompanied by activities that generate pollutants that can harm groundwater resources and aquatic ecosystems (Zoyem & Talla 2021). Despite this, water resource exploitation works are subject to no management that would allow conservation and/or protection of its quality. The rights to develop and improve peoples’ living environment and the duty to safeguard the natural heritage are nowadays two parameters of a problem difficult to approach. Thus, water, an asset for any development, is at risk of being contaminated by anthropogenic activity (Behanzin et al. 2021). In agricultural areas, fertilizers and phytosanitary products constitute a danger that can reach the water table and contaminate it for many years. On the other hand, in urban areas, industrialization is a remarkable pollution factor associated with human activities that are generally the cause of water-borne diseases, especially malaria, which remain the predominant disease in the study area (Tchazi et al. 2021). According to Donfack et al. (2020), 5% of households in Bafoussam are connected to the CAMWATER network, 8% to the water infrastructure (reservoir, pumping station, and water tower), 12% to boreholes, 30% to wells, and 25% to developed and undeveloped sources. Thus, the majority of the city's inhabitants (67%) are supplied with water from underground sources, 30% of which is supplied as drinking water.
In view of the pressures exerted by human activity directly or indirectly on Bafoussam's groundwater quality, the problem of land use change impact arises from the quality deterioration of the deep open aquifers easily accessible by the majority of the population. The aim of this study is to show that the progress of urban development through the evolution of the quality of land use in Bafoussam contributes to groundwater pollution.
MATERIALS AND METHODS
Location
Biophysical environment
The Western Highlands hydrography is marked by straight structural control and numerous waterfalls and cascades. The two largest rivers 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 Bamileke and Bamoun ethnic groups (Kankeu et al. 2010). Bafoussam incorporates both flat areas and hills. The latter are fairly steep, favouring erosion and leading to landslides in places (PNDP 2013) (National Participatory Development Programme).
The West Cameroon Highlands comprise pan-African granite-gneiss bedrock covered by a volcanic mantle (Kwékam et al. 2013). Bafoussam's soils are derived mainly from volcanic and granite-gneissic rocks, with permeabilities varying between about 10−5 m·s−1 for volcanic (hydromorphic) soils and 10−7 m·s−1 for soils derived from granite-gneissic rocks (Nguedia et al. 2022).
In the study area, natural vegetation has practically disappeared from the Bamileke plateau, where the landscape has been transformed into a bocage – pastureland with small areas of woodland – characteristic of the country and its dispersed habitat, to favour the growth of the town.
Sanitation diagnosis
Land use analysis
The land use maps were made using the supervised classification method on ArcGIS 10.8 based on the ‘Google Earth Pro and Earth Explorer USGS’ satellite images captured in 2006 and 2022. The 2006 and 2022 land use maps were compared with respect to the type of activity carried out on the land between the two periods.
Groundwater analysis
A total of 38 wells, out of 220, were selected for physico-chemical analysis of the groundwater. Three samples were taken per district, one each at top, middle, and foot of the slope on each type of geological formation per district. (Very few wells were found at the top of the slope.)
The pH, temperature (°C), electrical conductivity (EC), and total dissolved solids (TDS) of the water samples were all measured at the time of field collection using the multi-electrode portable probes. A Hach DR/820 Portable Data Storage Colorimeter was used to measure other parameters in laboratory. The sampling technique consisted of rinsing 0.5 l PVC bottles three times with the water sampled and taking three samples per point. One sample was filtered and the filtrate was treated with 1.5 ml of 0.1 N nitric acid (to stabilize the cations) and the second and third were used for anion and microbiological determinations. The filled samples were resealed, stored in coolers with ice cubes, and transferred to the Faculty of Agronomy and Agricultural Sciences (FASA) laboratories of soil science and environmental chemistry and water management for analysis (see Table 1).
Parameter . | Method and equipment . |
---|---|
Colorimetric determination and reading with the ‘PC Multidirect’ photometer according to the procedures of the PCmultidirect Phometer system Manual version 2005 | |
Cl | |
Elements for the determination of carbonate concentration as described by Rodier (2009) | |
K+ | Flame photometry using atomic adsorption as described by Rodier (2009) |
Na+ | |
Ca2+ | |
Mg2+ | |
Fe, Cu, Mn, Zn, Pb, Ni, Cd | Determination with spectrophotometry as described by Rodier (2009) |
Parameter . | Method and equipment . |
---|---|
Colorimetric determination and reading with the ‘PC Multidirect’ photometer according to the procedures of the PCmultidirect Phometer system Manual version 2005 | |
Cl | |
Elements for the determination of carbonate concentration as described by Rodier (2009) | |
K+ | Flame photometry using atomic adsorption as described by Rodier (2009) |
Na+ | |
Ca2+ | |
Mg2+ | |
Fe, Cu, Mn, Zn, Pb, Ni, Cd | Determination with spectrophotometry as described by Rodier (2009) |
Data processing
The data were structured with Excel, processed with SPSS 20, and finalized in Word. SPSS 20 was used to process physico-chemical data, and ArcGIS to draw up and analyse thematic maps of land use, nitrate and ammonium distribution, as well as their superimpositions to draw up and analyse the pollution-level map according to land use, and the nitrate content of the groundwater
RESULTS AND DISCUSSION
Land use
Land use . | Proportion (%) in 2006 . | Proportion (%) in 2022 . |
---|---|---|
Green space | 36 | 25 |
Agricultural areas | 35 | 33 |
Old buildings | 20 | 15 |
Recent buildings | 10 | 27 |
Land use . | Proportion (%) in 2006 . | Proportion (%) in 2022 . |
---|---|---|
Green space | 36 | 25 |
Agricultural areas | 35 | 33 |
Old buildings | 20 | 15 |
Recent buildings | 10 | 27 |
Environmental diagnosis
About 63% of Bafoussam's population uses traditional latrines (with a cesspool) against 22 and 14%, respectively, using modern latrines and mixed latrines (traditional and modern). The drinking water sources used are springs (12.5%), boreholes (3%), wells (15.5%), CAMWATER (Cameroon Water Utilities Corporation) (62.5%), and bottled industrial water (6.5%). Some 66% of the population dump household waste in urban roads, 6.3% in plantations, and 5% in rivers and gullies, with 22.8% having it collected. Malaria is the most common disease, at 65.8%, followed by typhoid at 27.8%. Tchazi et al. (2021) reported that typhoid is the most common disease, in Batoufam, at 50% followed by malaria, which difference arises because, in Batoufam, only 1% of the population has access to water from CAMWATER, 29% consume rainwater, and 29% water from wells of dubious quality.
Dimension . | Cronbach's alpha . | Explained variance . | ||
---|---|---|---|---|
Total (eigenvalue) . | Inertia . | Percentage of variance explained . | ||
1 | 0.702 | 2.659 | 0.295 | 29.543 |
2 | 0.592 | 2.11 | 0.234 | 23.44 |
Total | 4.769 | 0.53 | ||
Average | 0.653a | 2.384 | 0.265 | 26.492 |
Dimension . | Cronbach's alpha . | Explained variance . | ||
---|---|---|---|---|
Total (eigenvalue) . | Inertia . | Percentage of variance explained . | ||
1 | 0.702 | 2.659 | 0.295 | 29.543 |
2 | 0.592 | 2.11 | 0.234 | 23.44 |
Total | 4.769 | 0.53 | ||
Average | 0.653a | 2.384 | 0.265 | 26.492 |
ais the average Cronbach's Alpha value and based on the average eigenvalue.
Figure 5 shows two dimensions, dimension 1 representing the factors responsible for water contamination and dimension 2 representing water-related diseases. The modalities representing the factors responsible for contamination include the predominance of traditional latrines (63%), poor housing conditions, the lack of wells, the preference for depositing household waste in the roads (66%), and the supply of drinking water from CAMWATER. All of these modalities contribute to malaria being the commonest water-related disease in Bafoussam.
Physico-chemical parameters
Tables 4 and 5 present the descriptive statistics for major ions and heavy metals in well water in the dry season.
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Ca (mg/L) | 38 | 45.6 | 3.2 | 48.8 | 17.5 | 1.9 | 12.0 | 144.9 |
Mg (mg/L) | 38 | 25.9 | 0.3 | 26.2 | 7.4 | 1.3 | 8.1 | 66.1 |
SO4 (mg/L) | 38 | 134.9 | 4.0 | 138.9 | 65.1 | 6.0 | 37.3 | 1,390.6 |
Cl (mg/L) | 38 | 682.0 | 25.0 | 707.0 | 212.2 | 22.1 | 136.5 | 18,644.1 |
P (mg/L) | 38 | 0.9 | 0.0 | 0.9 | 0.2 | 0.0 | 0.3 | 0.9 |
K (mg/L) | 38 | 31.7 | 1.1 | 32.8 | 8.0 | 1.1 | 7.0 | 50.4 |
Na (mg/L) | 38 | 46.0 | 7.8 | 53.8 | 21.5 | 1.9 | 12.0 | 145.1 |
HCO (mg/L) | 38 | 453.5 | 7.1 | 460.6 | 125.3 | 12.1 | 74.8 | 5,601.4 |
NH4 (mg/L) | 38 | 28.5 | <1 | 28.5 | 9.0 | 1.1 | 7.1 | 50.8 |
NO3 (mg/L) | 38 | 63.2 | <1 | 63.2 | 25.2 | 3.3 | 20.7 | 427.1 |
EC (μS/cm) | 38 | 720.0 | 30.0 | 750.0 | 246.3 | 28.3 | 174.6 | 30,480.3 |
T.(°C) | 38 | 3.3 | 20.1 | 23.4 | 21.9 | 0.1 | 1 | 0.8 |
TDS (mg/L) | 38 | 358.0 | 17.0 | 375.0 | 133.2 | 13.9 | 85.8 | 7,355.5 |
pH | 38 | 2.3 | 4.9 | 7.3 | 6.4 | 0.0 | 0.5 | 0.2 |
N valid (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Ca (mg/L) | 38 | 45.6 | 3.2 | 48.8 | 17.5 | 1.9 | 12.0 | 144.9 |
Mg (mg/L) | 38 | 25.9 | 0.3 | 26.2 | 7.4 | 1.3 | 8.1 | 66.1 |
SO4 (mg/L) | 38 | 134.9 | 4.0 | 138.9 | 65.1 | 6.0 | 37.3 | 1,390.6 |
Cl (mg/L) | 38 | 682.0 | 25.0 | 707.0 | 212.2 | 22.1 | 136.5 | 18,644.1 |
P (mg/L) | 38 | 0.9 | 0.0 | 0.9 | 0.2 | 0.0 | 0.3 | 0.9 |
K (mg/L) | 38 | 31.7 | 1.1 | 32.8 | 8.0 | 1.1 | 7.0 | 50.4 |
Na (mg/L) | 38 | 46.0 | 7.8 | 53.8 | 21.5 | 1.9 | 12.0 | 145.1 |
HCO (mg/L) | 38 | 453.5 | 7.1 | 460.6 | 125.3 | 12.1 | 74.8 | 5,601.4 |
NH4 (mg/L) | 38 | 28.5 | <1 | 28.5 | 9.0 | 1.1 | 7.1 | 50.8 |
NO3 (mg/L) | 38 | 63.2 | <1 | 63.2 | 25.2 | 3.3 | 20.7 | 427.1 |
EC (μS/cm) | 38 | 720.0 | 30.0 | 750.0 | 246.3 | 28.3 | 174.6 | 30,480.3 |
T.(°C) | 38 | 3.3 | 20.1 | 23.4 | 21.9 | 0.1 | 1 | 0.8 |
TDS (mg/L) | 38 | 358.0 | 17.0 | 375.0 | 133.2 | 13.9 | 85.8 | 7,355.5 |
pH | 38 | 2.3 | 4.9 | 7.3 | 6.4 | 0.0 | 0.5 | 0.2 |
N valid (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Fe (mg/L) | 38 | 0.4 | 0.2 | 0.6 | 0.3 | 0.0 | 0.0 | 0.0 |
Cu (mg/L) | 38 | 0.3 | 0.1 | 0.4 | 0.2 | 0.0 | 0.0 | 0.0 |
Cd (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Pb (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Zn (mg/L) | 38 | 16.2 | 0.0 | 16.2 | 2.2 | 0.6 | 4.0 | 16.2 |
Ni (mg/L) | 38 | 0.1 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 |
Mn (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Valid N (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Fe (mg/L) | 38 | 0.4 | 0.2 | 0.6 | 0.3 | 0.0 | 0.0 | 0.0 |
Cu (mg/L) | 38 | 0.3 | 0.1 | 0.4 | 0.2 | 0.0 | 0.0 | 0.0 |
Cd (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Pb (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Zn (mg/L) | 38 | 16.2 | 0.0 | 16.2 | 2.2 | 0.6 | 4.0 | 16.2 |
Ni (mg/L) | 38 | 0.1 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 |
Mn (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Valid N (listwise) | 38 |
Tables 6 and 7 present the descriptive statistics for the same parameters in well water during the rainy season.
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Ca (mg/L) | 38 | 47.5 | 4.4 | 51.8 | 18.5 | 1.9 | 12.1 | 147.8 |
Mg (mg/L) | 38 | 45.2 | 0.7 | 45.9 | 11.2 | 1.8 | 11.1 | 123.6 |
SO4 (mg/L) | 38 | 184.0 | 5.3 | 189.3 | 78.7 | 7.3 | 45.4 | 2,058.6 |
Cl (mg/L) | 38 | 580.6 | 50.0 | 630.6 | 305.8 | 25.1 | 154.8 | 23,956.8 |
P (mg/L) | 38 | 14.6 | 0.0 | 14.6 | 2.2 | 0.6 | 3.5 | 12.2 |
K (mg/L) | 38 | 43.1 | 1.9 | 45.1 | 14.3 | 1.6 | 10.2 | 104.9 |
Na (mg/L) | 38 | 46.9 | 10.0 | 56.9 | 26.8 | 2.1 | 13.1 | 173.0 |
HCO3 (mg/L) | 38 | 431.8 | 11.3 | 443.2 | 133.0 | 16.5 | 101.6 | 10,326.0 |
NH4 (mg/L) | 38 | 79.5 | <1 | 80.0 | 27.3 | 2.9 | 18.2 | 331.6 |
NO3 (mg/L) | 38 | 95.1 | 1.2 | 96.0 | 33.6 | 3.9 | 24.0 | 576.9 |
EC (μS/cm) | 38 | 558.0 | 26.0 | 584.0 | 197.6 | 20.9 | 129.1 | 16,663.2 |
T (°C) | 38 | 1.7 | 20.1 | 21.8 | 21.1 | 0.1 | 0.3 | 0.1 |
TDS (mg/L) | 38 | 4,027.0 | 25.0 | 4,052.0 | 268.0 | 103.3 | 637.0 | 405,787.3 |
pH | 38 | 6.0 | 1.5 | 7.540 | 6.7 | 0.1 | 0.9 | 0.9 |
N valid (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Ca (mg/L) | 38 | 47.5 | 4.4 | 51.8 | 18.5 | 1.9 | 12.1 | 147.8 |
Mg (mg/L) | 38 | 45.2 | 0.7 | 45.9 | 11.2 | 1.8 | 11.1 | 123.6 |
SO4 (mg/L) | 38 | 184.0 | 5.3 | 189.3 | 78.7 | 7.3 | 45.4 | 2,058.6 |
Cl (mg/L) | 38 | 580.6 | 50.0 | 630.6 | 305.8 | 25.1 | 154.8 | 23,956.8 |
P (mg/L) | 38 | 14.6 | 0.0 | 14.6 | 2.2 | 0.6 | 3.5 | 12.2 |
K (mg/L) | 38 | 43.1 | 1.9 | 45.1 | 14.3 | 1.6 | 10.2 | 104.9 |
Na (mg/L) | 38 | 46.9 | 10.0 | 56.9 | 26.8 | 2.1 | 13.1 | 173.0 |
HCO3 (mg/L) | 38 | 431.8 | 11.3 | 443.2 | 133.0 | 16.5 | 101.6 | 10,326.0 |
NH4 (mg/L) | 38 | 79.5 | <1 | 80.0 | 27.3 | 2.9 | 18.2 | 331.6 |
NO3 (mg/L) | 38 | 95.1 | 1.2 | 96.0 | 33.6 | 3.9 | 24.0 | 576.9 |
EC (μS/cm) | 38 | 558.0 | 26.0 | 584.0 | 197.6 | 20.9 | 129.1 | 16,663.2 |
T (°C) | 38 | 1.7 | 20.1 | 21.8 | 21.1 | 0.1 | 0.3 | 0.1 |
TDS (mg/L) | 38 | 4,027.0 | 25.0 | 4,052.0 | 268.0 | 103.3 | 637.0 | 405,787.3 |
pH | 38 | 6.0 | 1.5 | 7.540 | 6.7 | 0.1 | 0.9 | 0.9 |
N valid (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Fe (mg/L) | 38 | 7.9 | 0.3 | 8.2 | 1.6 | 0.3 | 2.0 | 4.1 |
Cu (mg/L) | 38 | 7.2 | 0.0 | 7.2 | 1.1 | 0.2 | 1.6 | 2.5 |
Cd (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Pb (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Zn (mg/L) | 38 | 16.2 | 0.0 | 16.2 | 2.3 | 0.6 | 4.0 | 16.1 |
Ni (mg/L) | 38 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.00 |
Mn (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.000 | 0.000 | 0.0 | 0.00 |
Valid N (listwise) | 38 |
Parameter . | N . | Interval . | Min . | Max . | Mean . | Std error . | Standard deviation . | Variance . |
---|---|---|---|---|---|---|---|---|
Fe (mg/L) | 38 | 7.9 | 0.3 | 8.2 | 1.6 | 0.3 | 2.0 | 4.1 |
Cu (mg/L) | 38 | 7.2 | 0.0 | 7.2 | 1.1 | 0.2 | 1.6 | 2.5 |
Cd (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Pb (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Zn (mg/L) | 38 | 16.2 | 0.0 | 16.2 | 2.3 | 0.6 | 4.0 | 16.1 |
Ni (mg/L) | 38 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.00 |
Mn (mg/L) | 38 | 0.0 | 0.0 | 0.0 | 0.000 | 0.000 | 0.0 | 0.00 |
Valid N (listwise) | 38 |
Aqueous concentrations of ammonium, nitrate, and some heavy metals such as zinc, lead, and copper in the area would be expected to increase significantly between the dry and wet seasons as the Student's t-test for matched sample pairs at the 5% threshold, the p-values are below 0.05 for concentration comparisons (Table 8). This implies a significant difference between these concentrations in the wet and dry seasons. This would be due to rainfall, which facilitates the infiltration of the pollutant load from the surface to the water table. The quality of maintenance of wells, their topographic position, and the distance between them and latrines are all elements that influence water quality. While ammonium and nitrate pollution arise mainly from agricultural activities, it is also due to the proximity of latrines to wells. Heavy metal pollution is linked to the discharge of untreated household and industrial wastewater from sewers and pipes on the city outskirts. Similar studies were carried out in Kobo, northern Ethiopia, by Sitotaw & Nigus (2021) and Afzaal et al. (2022) in Pakistan, but the waters’ physico-chemical concentrations were found to be within WHO recommended levels. This is because the well depth in Kobo exceeds 30 m, with a high rate of well development, whereas the average well depth in Bafoussam is 7 m and the rate of well development is low.
. | Paired differences . | . | . | . | |||
---|---|---|---|---|---|---|---|
Paired sample test . | Mean . | Standard deviation . | Mean standard error . | t . | ddl . | Sig. (two-tailed) . | |
Nitrate | NO3 dry – NO3 (wet) | −8.405 | 7.915 | 1.284 | −6.546 | 37 | 0 |
Ammonium | NH4 (dry) – NH4 (wet) | −18.211 | 14.878 | 2.414 | −7.545 | 37 | 0 |
Copper | Cu (dry) – Cu (wet) | −1.041 | 1.592 | 0.258 | −4.032 | 37 | 0 |
Lead | Pb (dry) – Pb (wet) | −0.003 | 0.008 | 0.001 | −2.562 | 37 | 0.015 |
Zinc | Zn (dry) – Zn (wet) | −0.093 | 0.244 | 0.040 | −2.358 | 37 | 0.024 |
. | Paired differences . | . | . | . | |||
---|---|---|---|---|---|---|---|
Paired sample test . | Mean . | Standard deviation . | Mean standard error . | t . | ddl . | Sig. (two-tailed) . | |
Nitrate | NO3 dry – NO3 (wet) | −8.405 | 7.915 | 1.284 | −6.546 | 37 | 0 |
Ammonium | NH4 (dry) – NH4 (wet) | −18.211 | 14.878 | 2.414 | −7.545 | 37 | 0 |
Copper | Cu (dry) – Cu (wet) | −1.041 | 1.592 | 0.258 | −4.032 | 37 | 0 |
Lead | Pb (dry) – Pb (wet) | −0.003 | 0.008 | 0.001 | −2.562 | 37 | 0.015 |
Zinc | Zn (dry) – Zn (wet) | −0.093 | 0.244 | 0.040 | −2.358 | 37 | 0.024 |
The 2022 land use map that the parts of the study area with nitrate concentrations above 50 mg/L (the WHO recommended maximum nitrate concentration in drinking water) are susceptible to diffuse pollution.
Areas of high nitrate and ammonium pollution are always in the vicinity of industry and areas of intensive agricultural practice. This indicates poor soil management, through misuse of chemical inputs and industrial effluent discharge into the environment without adequate treatment. Here, agricultural activity is not neglected, either. The land use map shows an overlap of areas of high ammonium pollution with the agricultural plots. the areas of high ammonium pollution are also close to the industry in some places, which shows the involvement of industry in groundwater pollution.
Considering the maximum concentrations of parameters such as nitrate and ammonium reported by Mpakam et al. (2009), pollution in Bafoussam appears to increase over time with urban development. The simple Student's t-test shows for nitrate and ammonium concentrations between 2006 and 2021 that there is a significant difference at the 5% level.
Principal component analysis (PCA) of the samples’ physico-chemical parameters in the rainy season presents two dimensions of which the first is represented by the concentrations of Ca, Mg, and Na. The second is represented by the concentrations of NO3, NH4, and SO4, as well as Cu, Fe, and Pb. From this it can be deduced that the enrichment of Bafoussam's water arises from the natural contribution of mineral elements from rocks (Component 2) and from agricultural and industrial pollution (Component 1).
This study has enabled the mapping of groundwater pollution levels by superimposing the land use maps and nitrate and ammonium distribution maps in the study area (Figure 14). As can be seen, the highly polluted areas coincide with areas of intense agriculture and low altitude. The areas with medium pollution levels represent 2/3 of the sector and are mainly urban. There are still areas with low pollution.
Previous studies by Donfack et al. (2020) and Zoyem & Talla (2021) assessed the quality of drinking water. This study is based solely on updated groundwater physico-chemical quality and land use, to determine pollution levels for the whole area, which could be a decision support tool for the Bafoussam municipalities. A similar study was carried out in Harare, Zimbabwe, by Ndoziya et al. (2019), where the overlay of microbiological and land use maps by GIS shows areas with high pit latrine density as areas of high groundwater pollution. There is a difference from the study because Harare is not bordered by plots of intense agricultural practice like Bafoussam. Taking the maximum concentrations of parameters like nitrate and ammonium, as obtained by Mpakam et al. (2006), it seems that pollution in Bafoussam increases over time with the evolution of land use, because the simple Student's t-test shows that there is a significant difference in nitrate and ammonium concentrations between 2006 and 2022, at the 5% threshold. These values are presented in Table 9.
Chemical elements . | Maximum concentration 2006 (mg/L) . | Maximum concentration 2021 (mg/L) . | WHO recommended maximum (mg/L) . |
---|---|---|---|
NO3 | 36.5 | 96 | 50 |
NH4 | 0.7 | 80 | 0.5 |
Zn | 16 | 3 | |
Cu | 7 | 2 | |
Pb | 0 | 0.01 |
Chemical elements . | Maximum concentration 2006 (mg/L) . | Maximum concentration 2021 (mg/L) . | WHO recommended maximum (mg/L) . |
---|---|---|---|
NO3 | 36.5 | 96 | 50 |
NH4 | 0.7 | 80 | 0.5 |
Zn | 16 | 3 | |
Cu | 7 | 2 | |
Pb | 0 | 0.01 |
In general, analysis of Figure 14 indicates that peri-urban agriculture in Bafoussam has more impact than the industry on groundwater quality. Comparison with the land use map (Figure 4) shows clearly that the industrial areas have average pollution, while those with intense agricultural practices are areas of high pollution. This agricultural relationship with strong pollution can be supported by comparing the pollution and topographic maps, where the areas with serious pollution are at the study area's lowest and moderate altitudes. This would mean that agricultural practices near the foot of the slopes (low altitudes) would be responsible for the rapid pollution of the surrounding wells. However, wells at low altitudes are generally susceptible to pollution (Nguedia et al. 2022). This would also justify relatively high nitrate and ammonium concentrations in the wells, coming from the drainage of chemical fertilizer residues from the plantations to the low points surrounded by wells at the bottoms of the slopes.
CONCLUSION
The study highlighted the human activities on the ground that degrade the quality of Bafoussam's unconfined groundwater resources by studying land use, sanitary diagnosis, and water quality analysis. Poor housing conditions, the existence of industries that discharge untreated effluents into the environment, and poor agricultural practices on the city's outskirts are the causes of groundwater pollution. This water quality degradation is associated with an uncontrolled urbanisation plan, which could lead to further deterioration in the future, with high levels of pollution across the area.
The dominant facies of Bafoussam's groundwater are chloride, sulphate, calcium, and magnesium. The water's enrichment arises from the natural contribution of mineral elements from local rocks and from agricultural and industrial pollution.
Pollution in Bafoussam increases over time with urban development. The simple Student's t-test shows that there is a significant difference at the 5% level, between 2006 and 2021, for groundwater nitrate and ammonium concentrations.
The model used to make the pollution map in this study took into account parameters such as land use and groundwater nitrate and ammonium concentrations. The model could be used as part of a watershed study for future work.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.