Characterizing groundwater quality for a safe supply of water using WQI and GIS in Bahir Dar city, northwest Ethiopia

Water is among the most crucial natural resources that guarantee the survival of all life forms. It is advantageous to one's health to consume enough high-quality water in sufficient amounts. Access to clean water is crucial for reducing sickness and enhancing the quality of life. Due to a rise in population and human activity, groundwater use has increased. The only source of drinking water for at least two billion people worldwide is groundwater, making it one of the most important and frequently used renewable resources in the world (Mahmud et al. 2020; Aniteneh 2021).

Anthropogenic activities, such as urbanization, industry, and agricultural intensification, damage groundwater quality worldwide (Kawo & Karuppannan 2018). Due to inadequate water sources, poor sanitation, and poor hygiene, 3.4 million people die each year from diseases associated with water (UNICEF 2008). The physical, chemical, and biological characteristics of water can be deemed to be the components of groundwater purity (Zeabraha et al. 2020). Many commonplace activities, including the use of pesticides and fertilizers as well as the removal of human, animal, and agricultural refuse, can contaminate groundwater. Many academic studies have focused on the state of the world's groundwater and the factors that contribute to contamination from both human activity and natural processes (Al-Sudani 2019).

Groundwater quality has declined and becomes contaminated as a result of population growth, changes in land use and land cover (LULC), human activities, and worldwide climate change (Tefera et al. 2021). One problem in the twenty-first century is the worsening of water quality in emerging nations because of unregulated incidents of industrial, farming, and residential pollution (Oki & Akana 2016). A basic human right and a necessity for health and growth are access to clean drinking water. However, it is inaccessible to millions of individuals in developing nations (UNICEF 2008).

Groundwater is an important supply of domestic water in many African cities. It is an essential water supply for Ethiopia's industrial and drinking needs (Karuppannan & Kawo 2019). Numerous studies stated the problems with the quality of groundwater (Gorelick & Zheng 2015; Vetrimurugan et al. 2017; Wu et al. 2019; Elumalai et al. 2020). Analysis of groundwater quality for safe and beneficial use requires the use of geographic information systems (GIS). It is an efficient tool for understanding and managing all water resources, and it can be used to create geographic decision support systems by combining spatial data with models for assessing groundwater quality (Singha et al. 2015). Groundwater susceptibility to contamination, potential from nonpoint sources of pollution, and other factors can be assessed through the integration of the groundwater quality index (GWQI) and GIS (Machiwal et al. 2018; Gonçalves et al. 2022; Jenifer & Jha 2022). The water quality index (WQI) assigns a single value to each water sample, specifies its overall quality category, and compares the quality of various selections based on the values acquired to assess and analyze groundwater quality for human consumption (Boudibi et al. 2019).

Numerous studies observed that Ethiopia's groundwater has been contaminated due to ineffective refuse management, inadequate sanitation, insufficient management, and fertilizer usage. For instance, groundwater in the rift valley is contaminated by liquid waste flows from the towns (Rango et al. 2010; Karuppannan & Kawo 2019). The Dire Dawa groundwater area was contaminated due to ineffective liquid and solid waste management (Tilahun & Merkel 2010). Research by Tamiru et al. (2004) indicates that untreated waste dumped into waterways has the potential to contaminate the groundwater in Addis Ababa. Dinka (2017) also mentioned the human-caused groundwater pollution in the Matahara area. Urban garbage and industrial and agricultural expansion are to blame for the surface and groundwater quality degradation in the main Ethiopian rift, particularly in the Modjo river basin (Kawo & Karuppannan 2018). Conducting the study in the Upper Blue Nile Basin has revealed that the majority of shallow wells near Lake Tana are unsafe to drink (Tefera et al. 2021). Total dissolved solids (TDS), total hardness (TH), nitrate (⁠⁠), and electrical conductivity (EC) levels that are all higher than the permitted maximum limit are the main contributors to it. The groundwater quality in the Chilanchil Abay watershed is generally in poor condition at the sampling locations (Haile & Gabbiye 2021).

Similar to other cities in Ethiopia and other countries, the quality of the surface and groundwater continues to decline in Bahir Dar city where there is insufficient waste management and environmental protection process (Alemu et al. 2022). In and around Bahir Dar city, the growth of urbanization, population, and human activity could be a factor in the increased waste discharged into open areas, which will ultimately result in a decline in groundwater quality. Due to numerous pollution-related factors, the bacteriological quality of the surface and groundwater in Bahir Dar City is continuously declining. About 60% of city dwellers use pit latrines, which are ill-built, badly maintained, and frequently overflow (Tabor et al. 2021). Liquid refuse from most urban dwellings either drains into the septic tanks and dry pits that are typically located near most shelters or ends up in the open ditches and marshes of the city. The open site in Bahir Dar is one of the unregulated open dumps that are near many residential areas. The communities located upstream and downstream of the disposal site use tainted groundwater and surface water for everyday needs. The shallow groundwater quality in cities can be influenced by the season, related agricultural management techniques, and close-by refuse management sanitation facilities.

There may be substantial groundwater pollution in the study area because of poor sanitation and a lack of efficient waste disposal and management practices. The most obvious pollution effects were seen in shallow wells, which are a reflection of all anthropogenic effects on groundwater sources (Alemu et al. 2022). Given the serious challenge to the quality of the groundwater in the municipality in question (Haile & Gabbiye 2021), it is necessary to investigate the issue's spatial distribution for the long-term management of a safe water supply. The groundwater quality should be periodically evaluated and tracked to safeguard its numerous applications. In areas where groundwater is the main source of potable water, it is essential to evaluate the geographical variability of groundwater quality to ensure the security of the water supply and the welfare of the population (Li et al. 2017). The GIS-based study is the most effective way to track the constantly changing evolution of water quality. For environmental executives and decision-makers, this cutting-edge tactic significantly simplifies environmental monitoring and encourages action. With the aforementioned characteristics of groundwater contamination and its application in groundwater quality evaluation, this work employs GIS to evaluate groundwater quality in Bahir Dar, Ethiopia. There is a need for a case study report that focuses on mapping the groundwater quality of Bahir Dar city particularly and uses geostatistical techniques along with a WQI. Therefore, the study aimed to evaluate how the main LULCs in the city differed in terms of groundwater quality. The study also examined the potential of groundwater and the spatial differences in its quality.

Bahir Dar typically experiences mild to warm weather. The highest relative humidity (84.57%) of any month is in August. The lowest relative humidity (43.81%) of any month is in March. July is the month with the rainiest days (28.97 days). A few days in January experience precipitation with a monthly average of 4 mm. It rarely rains in the winter, but it usually does in the summer. About 102.1 mm of precipitation fall on the city annually. The wettest month is August, with an average rainfall of 528 mm (Merkel 2020). The average annual temperature in Bahir Dar is 20.1 °C.

Land-use activities directly affect water quality, even though water quality has a significant effect on the chance for land-use activities. Inappropriate land use, in particular poor land management, is the cause of groundwater pollution worries. Inappropriate land use frequently causes acute problems with groundwater quality (Omer 2019). The built-up area grew mainly due to horizontal growth from 80 ha in 1957 to 848 ha in 1994 and predict that the current urban region will have doubled by 2024 at the cost of agricultural lands. This will have extensive ecological, socioeconomic, and environmental impacts.

Lake Tana, which drains more than 3,000 km² and is the source of the Blue Nile River, was accessible to Bahir Dar city. The drinking water of the city is supplied from 19 boreholes (not deemed private boreholes) and three springs, accounting for about 86.6 and 13.4%, respectively. Water supply from these holes has not adequately addressed the consumption rate of the rapidly growing population of the city. The rate of consumption of the rapidly expanding population of the city has not been properly met by the water supply from these holes. In response to expansion and its associated commercial, residential, institutional, and industrial activities of the city, the rate of groundwater consumption greatly increased. Every year, Bahir Dar's population has increased significantly. The urban population has grown over the past 10 years, going from 180,174 in 2007 to 313,997 in 2017 (Wubie et al. 2020).

Issues with acute groundwater quality are common and are brought on by improper land use and management, especially point sources of hazardous chemicals (Alemu et al. 2022). From the main land uses and land areas (built-up, plantation, and agricultural) of the city, about 33 groundwater samples were purposefully collected (Figure 1; Table 1).

Five private and eight public boreholes, as well as two private hand-dug holes, were taken from the built-up area. Eight public and one private borehole, as well as two private hand-dug holes, were used from the plantation. Moreover, two government boreholes and five private hands dug were sampled from agricultural land. Using location data obtained with a handheld Global Position System(GPS), a point feature showing the location of the wells were produced (Figure 1). The sampling locations are listed in Table 1.

The bottles are properly cleaned with water before being filled with the sample water. Samples are collected, stored, and then transported to the laboratory for physiochemical and bacteriological examination. The chemical laboratory study is carried out by Amhara Design and Supervision Work Enterprise Soil Chemistry and Water Quality Section, and the physical–biological inspection is done by Choice Water Bottling Company. Information on water quality was prepared to create laboratory results. The laboratory results of each water quality parameter were then linked with the geographic data and saved in excel format before being converted into shape files using Arc Map's joining feature. The formation and combination of the spatial and nonspatial files allowed for the creation of maps showing the distribution of the water quality parameters.

On-site water temperature was measured with a mercury thermometer, and a digital pH meter (Model Metrohm, Zofingen, Switzerland) was used to measure pH levels (Alramthi et al. 2022). The turbidity, TDS, total alkalinity, TH, chloride, sulfate, nitrite, nitrate, iron, and manganese measurements were all made by APHA/AWWA/WEF (2017) guidelines. EC was measured using a conducting meter. The atomic absorption spectrophotometric method was used to analyze the amount of phosphate. The most probable number (MPN) method was employed to quantify Escherichia coli to highlight the microbiological quality of the water (APHA/AWWA/WEF 2017).

The histogram and regular quantile–quantile (QQ) plots were used in the ArcGIS version 10.7 geostatistical study to visualize the distribution of data. The distribution of the data is compared to a typical normal distribution using the QQ plot. Normal QQ plots show asymmetric (i.e., far from normal) distribution of water quality metrics and univariate normality. The points are no longer in a straight line. All 12 of the other characteristics had a skewed distribution, except temperature and overall hardness, which were both somewhat regularly distributed.

One of the frequently employed techniques for data normalization is log transformation. It is optimal for kriging approaches if the data are roughly regularly distributed. Any interpolation method that is used to interpolate data spatially presupposes a normal distribution. Before using skewed data in any geostatistical analysis, it must first be transformed into a normal distribution. The normalization results in Table 2 disclose that except temperature and TH, all other parameters, including nitrate (⁠⁠), nitrite (⁠⁠), TDS, chloride (Cl⁻), manganese (Mn), E. coli, alkalinity (CaCO₃), sulfate (⁠⁠), Phosphate (⁠⁠), EC, iron (Fe), and pH, had a skewed distribution as the result all are transformed using log transformation.

Table 2

Summary of the analyzed water quality parameters

Drinking water quality parameters .	Min. .	Max. .	Mean .	Standard deviation .	Skewness .	Kurtosis .
Temperature	26	29.3	27.5	0.7	0.02	0.5
pH	6.2	8.9	6.9	0.6	1.5	3.7
pHa	3.3	3.4	3.3	0.03	−0.1	3.2
EC	49	799	358.2	218.7	−0.1	−1.1
ECa	3.9	6.7	5.6	0.9	−0.8	1.9
TDS	20	319	139.4	84.1	−0.1	−0.9
TDSa	3	5.8	4.6	0.9	−0.8	1.8
CaCO3	100	200	137.8	28.4	0.5	−0.9
CaCO3a	4.6	5.3	4.9	0.2	0.3	1.8
T. Hardness	54	198	98.4	24.5	2	7.9
Turbidity	0.001	4	0.7	1.1	1.5	1.5
Turbiditya	−6.9	1.4	−4.2	3.6	0.6	1.4
E. coli	0.001	272	26.4	49.6	3.9	19.5
E. colia	− 6.9	5.6	0.4	4.4	−0.9	2.2
	0.01	0.6	0.1	0.1	4.7	26.5
a	−4.3	−0.5	−2.7	0.6	0.8	7.3
Mn	0.001	1.4	0.4	0.5	0.9	−0.2
Mna	−6.9	0.4	−3.7	3.4	0.1	1.0
Fe	0.001	0.5	0.04	0.1	4.6	26.1
Fea	−6.9	−0.7	−5.02	2.0	0.3	1.5
	0.001	16.3	2.4	4.8	1.9	2.6
a	−6.9	2.8	−4.4	3.9	0.9	1.9
	0.001	0.6	0.1	0.2	1.7	1.9
a	−6.9	−0.4	−5.2	2.7	0.9	1.9
Cl−	0.001	35.8	5.4	10.9	1.8	2.0
Cl−a	−6.9	3.6	−4.1	4.4	0.9	1.9
	0.001	32.5	1.1	5.5	5.5	32.8
a	−6.9	3.5	−4.9	3.2	1.1	2.6

Drinking water quality parameters .	Min. .	Max. .	Mean .	Standard deviation .	Skewness .	Kurtosis .
Temperature	26	29.3	27.5	0.7	0.02	0.5
pH	6.2	8.9	6.9	0.6	1.5	3.7
pHa	3.3	3.4	3.3	0.03	−0.1	3.2
EC	49	799	358.2	218.7	−0.1	−1.1
ECa	3.9	6.7	5.6	0.9	−0.8	1.9
TDS	20	319	139.4	84.1	−0.1	−0.9
TDSa	3	5.8	4.6	0.9	−0.8	1.8
CaCO3	100	200	137.8	28.4	0.5	−0.9
CaCO3a	4.6	5.3	4.9	0.2	0.3	1.8
T. Hardness	54	198	98.4	24.5	2	7.9
Turbidity	0.001	4	0.7	1.1	1.5	1.5
Turbiditya	−6.9	1.4	−4.2	3.6	0.6	1.4
E. coli	0.001	272	26.4	49.6	3.9	19.5
E. colia	− 6.9	5.6	0.4	4.4	−0.9	2.2
	0.01	0.6	0.1	0.1	4.7	26.5
a	−4.3	−0.5	−2.7	0.6	0.8	7.3
Mn	0.001	1.4	0.4	0.5	0.9	−0.2
Mna	−6.9	0.4	−3.7	3.4	0.1	1.0
Fe	0.001	0.5	0.04	0.1	4.6	26.1
Fea	−6.9	−0.7	−5.02	2.0	0.3	1.5
	0.001	16.3	2.4	4.8	1.9	2.6
a	−6.9	2.8	−4.4	3.9	0.9	1.9
	0.001	0.6	0.1	0.2	1.7	1.9
a	−6.9	−0.4	−5.2	2.7	0.9	1.9
Cl−	0.001	35.8	5.4	10.9	1.8	2.0
Cl−a	−6.9	3.6	−4.1	4.4	0.9	1.9
	0.001	32.5	1.1	5.5	5.5	32.8
a	−6.9	3.5	−4.9	3.2	1.1	2.6

^aTransformed using log transformation.

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The last result of GWQI off the study area ranged between 15.6 and 66.9. The GWQI values between 0 and 24 indicate excellent performance, 25 to 50 indicate good performance, and 50 to 70 indicate poor performance (Elubid et al. 2019).

Note: Groundwater quality values ranging from 0 to 24 are excellent, GWQI values 25–50 represent good, and GWQI values ranging from 50 to 70 denote poor (Elubid et al. 2019).

The groundwater potential zone map was prepared using seven different types of thematic maps: rainfall, lineament, slope, land use/cover, drainage density, soil, and geology. The data were acquired from satellites and meteorological stations in the study area. ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) Digital Elevation Model (DEM) at 30-m spatial resolution was used to create lineament density, slope, and drainage density maps, which were downloaded from the United States Geological Survey Earth Explorer (earthexplorer.usgs.gov). The geology and soil data were acquired from the FAO Digital Soil Map of the World. A land-use/land-cover map was generated from Sentinel 2 satellite imagery. To develop the groundwater potential maps in the study area, all parameters are categorized into five classes, such as very low, low, medium, high, and very high. The results of the consistency ratio and the pair-wise comparison matrix categorization of factors influencing groundwater potential zones (GWPZ) were prepared using free web-based analytical hierarchy process (AHP) software available at http://bpmsg.com.

Literature was used to determine the importance and ranking of each conditioning factor (Arulbalaji et al. 2019; Berhanu & Hatiye 2020; Popoola et al. 2020; Yihunie & Halefom 2020; Melese & Belay 2022). Each theme layer was given a ranking and weight depending on its ability to contain water (Arulbalaji et al. 2019). After ranking each layer, the weights of all the layers were summed, and the resulting total was divided into potential groundwater zones. A groundwater potential map has been produced in GIS by combining thematic layers, such as slope, drainage density, rainfall, soil, geology, land use/land cover, and lineament density that support the occurrence of groundwater. All of the layers were rasterized and then combined.

Table 4 demonstrates the best-fitted models for the analyzed parameters. The cross-validation results are evaluated and shown in Table 4 based on the semivariogram model assumptions provided (Equations (3)–(6)). In terms of pH, alkalinity, turbidity, nitrite, and iron, the model stable fits the data the best. The spherical model fits temperature, EC, E. coli, and sulfates the best. The most accurate forecast for TDS, manganese, and nitrate came from a circular model. For phosphate and overall hardness, exponential was optimum. As a result, each model proved reliable and could be used to generate surfaces and predict values.

Table 4

Best-fitted models for each groundwater quality parameter

Drinking water quality parameters .	Best-fit model .	Prediction errors .
		Mean .	Root-mean square .	Average standard error .	Mean standardized .	Root-mean-square standardized .
pH	Stable	−0.06	0.45	0.45	−0.06	0.99
Temperature	Spherical	−0.04	0.76	0.59	−0.07	1.18
EC	Spherical	−0.83	139.06	371.19	−0.03	1.01
TDS	Circular	3.88	53.09	152.63	0.001	0.94
CaCO3	Stable	0.14	26.82	25.76	−0.003	1.04
T. Hardness	Exponential	−1.41	24.29	25.08	−0.06	0.97
Turbidity	Stable	−6.29	64.19	48.67	−0.06	1.42
E. coli	Spherical	1.11	49.63	55.65	0.02	0.92
	Stable	0.01	0.11	0.18	−0.06	0.94
Mn	Circular	−0.06	0.39	0.35	−0.08	1.09
Fe	Stable	−0.0009	0.08	1.17	0.04	0.86
	Circular	0.04	4.13	4.78	0.01	0.86
	Exponential	−0.01	0.18	0.11	−0.02	1.32
Cl−	Circular	0.59	8.15	10.23	0.04	0.86
	Spherical	−0.09	6.14	6.00	−0.006	0.84

Drinking water quality parameters .	Best-fit model .	Prediction errors .
		Mean .	Root-mean square .	Average standard error .	Mean standardized .	Root-mean-square standardized .
pH	Stable	−0.06	0.45	0.45	−0.06	0.99
Temperature	Spherical	−0.04	0.76	0.59	−0.07	1.18
EC	Spherical	−0.83	139.06	371.19	−0.03	1.01
TDS	Circular	3.88	53.09	152.63	0.001	0.94
CaCO3	Stable	0.14	26.82	25.76	−0.003	1.04
T. Hardness	Exponential	−1.41	24.29	25.08	−0.06	0.97
Turbidity	Stable	−6.29	64.19	48.67	−0.06	1.42
E. coli	Spherical	1.11	49.63	55.65	0.02	0.92
	Stable	0.01	0.11	0.18	−0.06	0.94
Mn	Circular	−0.06	0.39	0.35	−0.08	1.09
Fe	Stable	−0.0009	0.08	1.17	0.04	0.86
	Circular	0.04	4.13	4.78	0.01	0.86
	Exponential	−0.01	0.18	0.11	−0.02	1.32
Cl−	Circular	0.59	8.15	10.23	0.04	0.86
	Spherical	−0.09	6.14	6.00	−0.006	0.84

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EC is a measurement of dissolved materials in water and is a function of ionic concentrations. Salinity, which has a substantial impact on taste and, in turn, the user's approval of water as potable, is the key component of EC. The value ranges from 49 to 799 μS/cm in the research region, with a mean and standard deviation of 358.3 and ±218.7, respectively (Figure 4(b)). In the city's built-up areas, concentrations are reported at both their maximum (799 μS/cm) and their lowest (49 μS/cm) (Table 5). The findings show that the groundwater was not significantly ionized, but greater values are noted in built-up portions of the city. The result agrees with the results of Meride & Ayenew's (2016) study on the drinking water of the Wondo genet campus in Ethiopia. The concentration of ions affects how well water conducts electricity. This is because inorganic dissolved solids like nitrate and phosphate, as well as the geology of the region through which the water travels, have an impact on EC in the sampled built-up areas (Alemu et al. 2022).

With a mean and standard deviation of 27.48 and 0.71, respectively, the temperature values in all of the stations that were sampled ranged from 26 to 29.3 °C (Figure 4(c) and Table 5). The plantation region recorded the highest temperature (29.3 °C), while built-up areas recorded the lowest temperature (26 °C). To stop the growth of the organism, the water temperature should be kept below the range of 25–50 °C (WHO & UNICEF 2013). Although there are differences in land-use and -cover classes, the groundwater temperature that was sampled is ambient, which is ideal for customers who prefer cool to warm water and for the specific needs of the water's quality for various uses (Meride & Ayenew 2016).

Natural water typically has less than 500 mg/L of dissolved solids, and water with more than 500 mg/L is not suitable for drinking or many industrial purposes (Jain et al. 2010). Brackish water is subsurface water with a TDS value of higher than 1,000 mg/L (Pande & Moharir 2018). TDS levels range from 20 to 319 mg/L in the study area, with a mean of 358.3 and a standard deviation of ±84.1, respectively (Figure 4(d)). Built-up locations have TDS concentrations that are both the highest and lowest. Built-up areas have high TDS values compared to other land-use groups (Table 5). Akhtar & Tang (2013) reported the higher TDS level in the groundwater was alarming for the consumers of the second biggest city in Pakistan. High TDS concentrations in groundwater could affect kidney and cardiac health (Gobalarajah et al. 2020). High-solid water may be laxative or cause diarrhea (Yin et al. 2020).

The mean alkalinity of 137.8 and a standard deviation of ±28.4, with alkalinity levels ranging from 100 to 200 mg/L (Figure 5(b)). Alkalinity levels are highest and lowest in populated regions and plantations, respectively (Table 6). The concentration levels of 26 out of 33 groundwater samples were found to be under the WHO's desired standard (150 mg/L), while the levels in the remaining 7 samples were found to be beyond the desirable limit (Table 6). The majority of values found in populated regions are above the ideal alkalinity range. Salman et al. (2018) found a high level of alkalinity in the populated area of Bangladesh. This is because the water contains compounds like bicarbonates, carbonates, and hydroxides, which eventually diminish the water body's capacity to neutralize acids and bases and so maintain a comparatively steady pH level (Arslan & Demir 2013).

With a mean and standard deviation of 98.4 and ±24.5, respectively, the concentration of TH ranged from 54 to 198 mg/L. In the study area, the value of overall hardness is noted below the WHO-recommended level. In the plantation and agricultural sectors, respectively, the minimum and maximum values are noted (Table 6). Balakrishnan et al. (2011) argue that water with a hardness of more than 150 mg/L is dangerous. The abundance of dissolved calcium and magnesium salts has a significant impact on the hardness of groundwater (Rapant et al. 2017). Heat induces the deposition of calcium and magnesium carbonates as a hard scale in kettles, cooking utensils, heating coils, and boiler tubes, resulting in a loss of fuel. Temporary hardness is removed by heat (Sunitha et al. 2014).

The manganese concentration is well within the 0.4 mg/L limits. Manganese levels range from 0.001 to 1.4 mg/L in the study city, with a mean and standard deviation of 0.4 and ±0.5, respectively. The concentration levels of 21 out of 33 groundwater samples were found to be under the acceptable range (0.4 mg/L), while the levels in the remaining 12 samples were found to be over the desirable limit. Maximum values are seen in built-up regions, while plantation areas have the lowest concentration (Table 6). In built-up areas, a high concentration of manganese has mostly decreased (Table 6). Similar findings are made by Hasan & Ali (2010), who also find that the most frequent sources of iron and manganese in groundwater are naturally occurring. However, other sources of iron and manganese in local groundwater include industrial effluent, acid-mine drainage, sewage, and landfill leachate. It may result in stains on clothing, scaling on pipes, and bad-tasting, odorous, or smelling water (Sunitha et al. 2014). This diagram displays the spatial distribution of manganese (Figure 5(e)).

With a mean and standard deviation of 0.03 and ±0.5, respectively, the iron content in the research area ranges from 0.001 to 0.5 mg/L. Of the 33 groundwater samples that were studied, 31 samples' concentration levels were found to be under the ideal limit (0.3 mg/L), whereas the desirable limit was surpassed at 2 sampling sites in the built-up region (Table 6). Within the plantation areas, there is the lowest concentration (Table 6). Sampling locations within populated areas went beyond the desired cap. The aquifer contains naturally occurring iron, but the dissolution of the ferrous borehole and hand pump components can raise groundwater levels (Devatha et al. 2016). Iron's spatial distribution is depicted in Figure 5(f).

In the study area, the concentration of nitrite in sampled groundwater stations varied from 0.01 to 0.6 mg/L with a mean and standard deviation of 0.08 and ±0.1, respectively (Table 6 and Figure 5(d)). Out of 33 groundwater samples analyzed, the concentration levels of 32 samples were found to be within the desirable limit (0.2 mg/L), whereas 1 sample site within a built-up area exceeds the desirable limit (Table 6). The minimum concentrations are recorded in plantation areas. There is a sampling station that exceeds the standard limits in built-up areas (Table 6). This is because nitrites come from fertilizers through runoff water, sewage, and mineral deposits. Nitrite can stimulate the growth of bacteria when introduced at high levels into a body of water (Parvizishad et al. 2017).

The concentration of chloride in the research area ranges from 0.001 to 35.8 mg/L, with a mean and standard deviation of 5.4 and ±10.9, respectively (Figure 6(b)). This concentration is within the WHO's allowed limits (250 mg/L). Built-up areas have the highest concentration, whereas plantation and agricultural areas have the lowest (Table 6). Built-up regions have higher chloride concentrations than other types of land use. Fertilizers, septic systems, and animal manure are typical sources of contamination. Alemu et al. (2022) reported that chloride concentrations at all measured sites vary from 16.33 to 108.67 mg/L, with an average of 56.12 mg/L, which could harm kidneys. Additionally, groundwater that is degrading as a result of intense agricultural use or excessive effluent application will nearly always have a rising chloride concentration (Singh et al. 2010).

With a mean and standard deviation of 1.1 and ±5.5, respectively, the concentration of sulfate in the study area ranges from 0.001 to 31.5 mg/L (Figure 6(c)). Built-up areas have the highest concentration, whereas plantation and agricultural areas have the lowest (Table 6). Sulfate's average value is within the accepted range. High sulfate values are seen in built-up regions compared to the plantation and rural areas. Aniteneh (2021) looked into findings similar to those on the evaluation of water quality in Addis Ababa city. Mineral dissolution, atmospheric deposition, and other anthropogenic sources are all sources of sulfate in groundwater. Sewage treatment facilities and industrial outputs from tanneries, pulp mills, and textile mills are examples of point sources. Additionally, fertilized agricultural fields' runoff feeds water bodies with sulfates (Sharma & Kumar 2020; Han et al. 2021).

With a mean and standard deviation of 2.4 and ±4.8, respectively, the nitrate concentration ranged from 0.001 to 17 mg/L (Table 6 and Figure 5(d)). One sample site within a built-up area surpasses the desirable limit out of 33 groundwater samples that were studied, while the concentration levels of 32 samples were found to be under the desirable limit (0.2 mg/L). Cropland areas followed by built-up regions have the highest concentrations, whereas plantation areas have the lowest concentration. The research area's overall mean value for nitrate is within the desired range. In populated regions, there is a sampling station that exceeds the permitted limits. This is because runoff water, sewage, and mineral deposits all transport nitrates from fertilizers to the environment. Nitrate, which is formed from pit latrines and is the most well-studied chemical contaminant, is the primary indicator of fecal pollution in groundwater sources since it is present in high concentrations in human excreta (Graham & Polizzotto 2013). When nitrate is added to a body of water in large quantities, it can encourage the growth of bacteria (Parvizishad et al. 2017). Inadequate good construction, well placement, excessive use of chemical fertilizers, and inappropriate handling of human and animal waste are all common causes of high amounts of nitrate in well water (Yu et al. 2020).

Eight of the 33 groundwater samples that were evaluated for E. coli concentration levels were found to be within the desired limit (zero counts per 100 mL), but the levels in the remaining 25 samples were found to be beyond the desirable limit (Table 6). The highest amounts were found in the built-up areas. According to the findings, fecal coliforms were identified practically everywhere that was sampled. Alemu et al. (2022) also discovered comparable findings. Therefore, it is evident that disease-causing organisms are present in the investigated groundwater stations due to leakage creation from wastewater produced by on-site sanitation systems, primary pit latrines, and septic tanks. Despite this, Azizullah et al. (2011) discovered that poorly designed on-site sanitation facilities, such as pit latrines and septic tanks, are the leading causes of groundwater contamination and the primary source of fecal coliform.

Results in Table 7 reveal the GWQI values range from 15.64 (excellent) to 66.88 (Poor). According to Raheja et al. (2022), 31.57 and 68.43% of samples fell into the excellent and good drinking water quality categories, respectively. In agriculture areas, 3.1% of WQI agricultural areas were rated excellent, and 18.2% were rated good. Agricultural areas showed the highest average WQI value (36.5), with ranges from 16.2 to 47.3. The overall mean highest value was found in agricultural areas (Jalali & Kolahchi 2008; He et al. 2021). Animal manure, fertilizers, and pesticides used in agricultural areas all contribute to pollution. In addition, runoff including bacteria from manure, dissolved nutrients from commercial fertilizers, and manure or pesticides that go through the soil to the groundwater can pollute groundwater (Malki et al. 2017).

The GWI in built-up areas was rated as excellent in 21.2% of cases, good in 9.1%, and poor in 15.1% of cases (Table 7). The Groundwater index(GWI) in built-up areas had a mean value of 34.6 and a maximum and minimum value of 66.9 and 15.6 respectively. These sorts of low water quality fall under this category. The poor GWQI value covered about 2,430 ha (Figure 6). Most measurements that exceed the WHO standard for pH, alkalinity, E. coli, nitrate, manganese, and iron are taken in the built-up areas. This is because the majority of the hotels and recreational facilities in this area act as point sources and produce pollutants. Urban locations have distinct groundwater recharge methods from rural ones. In the case of urban groundwater recharge, various new factors must be taken into account in addition to the natural recharge from precipitation (Wakode et al. 2018). Built-up environments provide both nonpoint and point sources of pollutants, according to a study conducted similarly in the USA by Brett et al. (2005). Leaking subterranean storage facilities, as well as different unintentional discharges of organic or inorganic contaminants, are examples of point sources that have an impact on groundwater quality.

Very high and high GWPZ cover 737 ha of the research area and are primarily found in the city's northwest and northern areas (Table 8). The southern and central areas of the city contain medium GWPZ that covers an area of 14,249 ha. Low GWPZs are found in the 407 ha research area's southern, south-eastern, and northern regions. Zones with very high and high groundwater potential account for 0.78 and 4% of the total land area, respectively. Figure 9 discloses that the northern and northwestern regions of the research area have extremely high and high water storage, respectively, owing to higher rainfall and a lower degree of slope (Melese & Belay 2022).

Gentle slope, low drainage density, high lineament density, and a good hydro-geomorphological form are all characteristics of favorable GWPZ. When compared to high GWPZ, ‘poor’ GWPZ has a lower recharge capacity and a steeper slope (Allafta et al. 2021). The study area is dominated by a medium groundwater potential zone, which accounts for 92% of the total area. The poor groundwater potential zone contains 3% of the research area. The study's results show how the groundwater potential varies spatially throughout the study area as a result of variations in the area's rainfall, geology, drainage density, soil, land use, land cover, slope, and lineament. Yihunie & Halefom (2020) stated that groundwater potential varied spatially from very low to high levels. The present study's groundwater potential zone map offers guidance to decision-makers for effective groundwater management and planning for urban and agricultural uses.

Table 9 depicts the southwest and northeastern regions of the study area have the best groundwater quality, while the northwest and north have the most productive aquifer.

The entire area of the very high groundwater potential zone is classified as poor groundwater quality (GWQ). This implies that very high GWPZs require immediate action to improve water quality to ensure a safe water supply. It is observed that the north and northwestern regions of the research area, which have high groundwater potential, are primarily distinguished by low-quality water. This could be because the area is a sedimentation site for nutrient-rich eroded soil from the upper and middle parts of the Gilgel Abay watershed, which is the main tributary of the Lake Tana Sub-basin. The movement of contaminated groundwater from higher hydraulic heads to lower hydraulic heads is accompanied by the sideways and downward leaching of farming nutrients and fertilizers into the groundwater (Tinonetsana et al. 2022). In addition, poorly treated industrial effluents (typically from hostels) and city sewage could contaminate areas with high to very high groundwater potential. Areas with high groundwater potential are degrading into poor quality and unfit for human consumption as a result of contamination from improperly treated industrial effluents and urban sewage (Rao & Jugran 2003; Popoola et al. 2020). Water quality in highly productive aquifers can be degraded by salinity, nonpoint-source pollutants such as nitrate from agriculture, and geogenic pollutants such as arsenic (Al-Abadi et al. 2021).

The quality of high groundwater potential consists of 1,116 ha of excellent, 4,699 ha of good, and 1,335 ha of low. This shows that 65.7 and 15.6% of the high groundwater potential zone are classified as good and excellent quality for a safe water supply, respectively. The medium groundwater potential zone covers an area of 14,249 ha, with 22% having excellent quality and 71% having good quality. The quality of the groundwater should be a significant factor in determining groundwater potential (Al-Abadi et al. 2021). Plans for sustainable groundwater management are required, especially for groundwater zones with high potential but poor quality (Jasrotia et al. 2013; Al-Abadi et al. 2021).

The study aimed to evaluate and map the quality and potential of groundwater under land use/covers in Bahir Dar City, northwest Ethiopia, using the WQI and geospatial technology. The findings revealed that 48.5% of the groundwater has the excellent quality, 36.4% has good quality, and 15.1% has poor quality. The land use/covers had an impact on groundwater quality. The pH, alkalinity, E. coli, nitrate, manganese, and iron measurements in the built-up area exceed the WHO standard, implying a low groundwater quality in the built-up area because it emits both nonpoint and point pollutants. The excellent groundwater quality is noticeable in the plantation area. The majority of the study area falls into the medium groundwater potential category, with high groundwater potential in the northern and northeastern areas and low groundwater potential in the eastern and northeastern areas. The medium to very high groundwater potential in the northern and northwest areas is characterized by low quality for a safe supply due to contamination from poorly treated industrial effluents and city sewage.

Based on the findings, it is suggested that the city's water and sewerage authority should prioritize managing and controlling point sources of pollution, especially in areas with very high to high groundwater potential. The appropriate remedial actions are taken, such as imposing limits on hotels and municipalities for the proper treatment of effluents and wastes. The Bahir Dar water and sewerage authority should monitor and evaluate private borehole owners' claims that the designated location where water is extracted is safe or not. Water supply boreholes should be located at a sufficient horizontal distance from dirty areas and inadequately designed sanitary facilities to reduce the risk of infection. Hand-dug and boreholes should be covered by the plantation to improve groundwater quality and ensure a safe supply of water. The groundwater potential zone with quality and the temporal variations of groundwater quality and potential could be determined through an additional quantitative study.

The authors gratefully acknowledge Choice Water Bottling Factory's financial assistance.

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

The authors declare there is no conflict.