The study conducted in dry and rainy seasons in 2019 assessed the levels of metals in ground water sampled from boreholes and dug wells in Ikwo, south-eastern Nigeria. Nine water samples were analysed for calcium, iron, zinc, copper, manganese, and aluminium using Atomic Absorption Spectrophotometer (AAS). Analysis showed higher metal concentrations in rainy season, except for manganese whose concentration was higher in the dry season. The levels of the metals exceeded permissible limits in both seasons except for calcium. The metal indices were not greater than 2 while all degrees of contamination were less than 1. This collaborative classification of both metal index and degrees of contamination suggest that water samples were safe for human consumption. The health risk evaluated by hazard indices (HI) were categorized into low and medium (≥ 0.1 to < 4) levels of chronic risks. The HI values, evaluated for both children and adults; were predominantly medium for children and low for adults. All indices for water quality evaluation revealed that ground water in the study area is of minimal contamination hence, no immediate threat to health. Therefore, to ensure quality water supply, sustainable monitoring of water quality is recommended in the study area, with continuous environmentally friendly agricultural practices.
Research Article
Assessment of Ground Water as Source of Drinking Water in an Agricultural Tropical Environment
https://doi.org/10.21203/rs.3.rs-1857892/v1
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Ikwo
ground water
water quality
metals
metal index
degree of contamination
health risk assessment
Ground water is the safest source of drinking water for a larger portion of communities around the world. The importance of water to humans, agriculture and industry cannot be over-emphasized. In fact, it is man’s number one need for sustenance of physiological and economic activities (Tyopine et al. 2022; Azhar et al.2021; Aghalari et al.2020; Gao et al.2020). There is greater demand for safe drinking water now than it was in the last decade. This is due to an increase in the industrialization; increased population growth, and wastewater management among others (Hoang et al.2020; Su et al.2019; Mukanyandwi et al. 2019; Banule et al.2018; Karthikeyan et al. 2018; Patel et al. 2018). These factors are crucial in the provision of safe and affordable drinking water and by extension, they impact directly on the wellbeing of communities (Nyantakyi et al. 2019; Abdel-Satar and Gohler 2017; Nagar et al. 2015; Chukwuemeka et al. 2014). However, the increase in population in urban and semi- urban settlements is a major challenge to the authority’s ability to keep a constant and sufficient supply of safe water through good management of water resources (Wedgworth and Brown 2013; Onda et al. 2012). The provision of safe drinking water is challenged greatly by limited finances and poor infrastructure in developing countries of Asia and Africa (Ji et al.2020). This challenge has driven communities to consume water directly from dug wells, streams, bore holes, rain water and other natural waters without adequate prior treatment (Sahoo and Patra 2018; Scott et al. 2017; Bempah and Ewusi 2016). For instance, it is reported that approximately 2/3rd of Chinese population sources water primarily from ground water (Zhang et al. 2020). Prolonged exploitation of ground water affects availability and also affects the quality of ground water (Mohabansi et al. 2011). This suggests that the challenge of accessing safe drinking water is greater with rural dwellers.
Local authorities and non- governmental organisations are giving much attention to ensuring supply of safe drinking water to all citizens including those in the rural settlements but this is still a huge burden to developing countries. For instance, it is reported that there was a decline inaccessibility to safe potable water from 1900–2015 during which there was a reduction from 2.6 billion people to 663 million who could access safe potable water. Most affected areas were countries of sub-saharan Africa (Bidkhori et al.2018; Aboniyo et al. 2017; Allen et al. 2013; Goher et al. 2014). The inability of the local authorities to keep safe water supply with large population growth is a challenge due to high cost of traveling to and the location of piped water in rural areas. The poor sanitation in the rural areas occasioned by poor sewage systems and open defecation further increases water pollution in sub-saharan Africa (Aboniyo et al. 2017). Ground water contamination is a major environmental and health concern stemming from the nature of contaminants found in ground water. The contaminants are introduced anthropogenically through the exploitation of ground water resources or discharge of waste from industries or runoffs from farms or refuse dumps (Ahmed et al.2019; Mgbenu and Egbueri 2019; Ukah et al. 2019; AbouZakhem and Hafez 2014) or through natural processes like weathering of geological formations and soils (Karbassi et al. 2008). Human health is associated with environmental and ground water contamination. This is widely observed in most developing communities (Adimalla et al. 2018). The quality of ground water as a source of potable drinking water is rapidly diminishing due to increased anthropogenic activities and increasing the ingestion of contaminated water (Bulut et al. 2020; Gao et al. 2020; Kaur 2019). To ensure safe water for humans, information of contaminants through monitoring is necessary. This will ascertain the extent of contamination and its associated human risk. Human risk assessment of water matches concentration of contaminants with quality standards and health risk bench marks (Mgbenu and Egbueri 2019; Taiwo et al. 2019; Ukah et al. 2019). Environmental monitoring of ground water employs methodologies like pollution index, carcinogenic and non-carcinogenic risk analysis, contamination degree, metal index (Rahman et al. 2020; Makanyandwi et al. 2019; Ukah et al. 2019; Jahan and Strezov 2017).
Ikwo community is an agrarian community in south eastern part of Nigeria with a fast growing population. Its soil is characterized by strong geoaccumulation of heavy metals and the leaching of metals into the environment may set the stage for ground water contamination (Tyopine et al. 2018). Ground water is the major source of drinking water in Ikwo and environ. Ikwo communities experience acute water scarcity in dry season as rain water is seasonal. This seasonal scarcity is controlled by soil texture, geology, and stream flow and rainfall level (Tyopine et al. 2018). The volume of water at any season renders pollutants like metals of great concern and long-term exposure to contaminated water poses high health risks. Various researchers have agreed that securing drinking water quality safety is important for safeguarding human health (He and Li 2020; He et al. 2019; Li et al. 2019; Li et al. 2018; Tyopine et al. 2018; Li et al. 2016). There is no evaluation to ascertain the extent of contamination by metals and their health risk in the communities. Therefore ground water quality in the communities is important in ascertaining the suitability of drinking water. The research aims to evaluate the metal contaminations and human health risk through the following objectives: to determine the concentration of metals (Ca, Fe, Cu, Zn, Al and Mn); to evaluate the degree of metal contamination of ground water in the area using index approach, to assess health risk by calculating daily dose intake, hazard quotient and hazard index. These analyses will be carried out in dry and rainy season to determine season with higher pollution index with the corresponding health risk (Lenoble et al. 2013; Huang et al. 2012; Rauf et al. 2009). The outcome of this work is expected to establish a preliminary baseline for effective monitoring of ground water in the area from which empirical information and data, can be obtained for informed decision from governmnetal environmental agencies.
Study Area
The study area (Fig. 1) is located within Ikwo and environs. The study area is drained by Ebonyi River with its numerous tributaries forming a dendritic pattern. Prominent towns within the study area include Enyigba, Ameka, Ameri and Amegu. Geologically, the area is composed of Shales of the Asu river group of Albian age. The Shales serve as host rock to the Lead-Zinc ores within the study area. The sediments of AsuRiver are low-yielding aquifers that provide only small pumped water supplies except where they may be fractured, sheared or weathered (Omaka et al. 2017). There are two seasons experienced in this area; the dry season is between November and April while the wet season is between May and October. The prevailing wind in the dry season is the harmattan wind while the northward wind of the inter-tropical zone ushers in the monsoon rainfall. The northward wind cools the temperature to below 280C with an average annual rainfall of 1800 mm (Inyang 1975). The average elevation of the study area is generally less than 250m. The population of Ikwo is sparsely dense and is mostly composed of farmers and petty traders.
Sampling and Laboratory Analysis
The predominant sources of drinking water in Ikwo community are boreholes and dug wells and these extract water from aquifers. The topography of Ikwo is fairly uniform with uniform geological parent material spreading beneath the surface (Tyopine et al. 2018) therefore nine drinking water samples were used to achieve the aim of this study. The sampling was carried out in two seasons: in march 2019 during the dry season corresponding to low water period and in October 2019 during the rainy season corresponding to high water period. A total of nine water samples per season were collected using 1L sterilized polyethylene bottles. These samples represented nine sampling sites (north (N), south (S), east (E), west (W) and central area (F)) as shown in Table 1 and were intended for metal analyses. The sample bottles were washed and soaked in 1:1 dilute HCl for 24 hrs, rinsed with distilled water. Prior to collecting the water samples, the sterilised bottles were rinsed with source water before collecting the samples. Water samples were acidified with 1% HNO3 and preserved for onward analysis. The samples were digested, concentrated and metal analysis were carried out using Atomic Absorption Spectrophotometry (model 200S). The metals measured were: iron (Fe), manganese (Mn), copper (Cu), calcium (Ca), aluminium (Al), and zinc (Zn). All analyses in the laboratory were carried out within 48 hrs of sample collection. The data from the analysis of water samples in dry season were compared with those in rainy season.
Quality Control of Results
Procedural blanks were used to correct for background absorption and values of measured heavy metals were expressed in mg/L (APHA 1995). The heavy metals were evaluated by comparing mean levels obtained in dry and rainy season.
Metal Contamination Assessment
The metal contamination of the water samples was evaluated using metal index (MI), contamination factor (Cf) and degree of contamination (Cd). These describe the limits and extent of pollution of water by metals (Rahman et al. 2020; De Anda et al. 2019; Mukanyandwi et al. 2019; Pandey et al. 2019; Shen et al. 2019).
Metal Index (MI)
The metal index has been used in describing drinking water quality (Goher et al. 2014). This index accounts for the possible additive effect of metals on human health. Its expression was first proposed by Caeiro et al. (2005) and it is calculated thus,
Where m = permissible concentration. The m values used in this study were adopted from Mukanyandwi et al. (2019). Ci is metal concentration. When a metal index value exceeds unity, it signifies a threshold of warning (Bakan et al. 2010) and this was applied on the metals analysed in this study to know which metals had more contamination impact on drinking water.
| Sampling spot | Latitude | longitude | Elevation( ft) |
|---|---|---|---|
| N1 | 6009’40.07’’N | 8007’48.67’’E | 142 |
| N2 | 6010’25.43’’N | 8006’55.63’’E | 168 |
| S1 | 6005’26.61’’N | 8009’06.03’’E | 131 |
| S2 | 6005’36.95’’N | 8006’56.37’’E | 172 |
| E1 | 6008’18.78’’N | 8010’30.88’’E | 95 |
| E2 | 6007’53.85’’N | 8011’23.95’’E | 102 |
| W1 | 6007’42.88’’N | 8004’57.10’’E | 182 |
| W2 | 6007’31.17’’N | 8003’17.88’’E | 242 |
| F | 6008’27.56’’N | 8007’58.25’’E | 126 |
Degree of Contamination (Cd) and Contamination Factor (Cf)
The degree of contamination sums all parameters whose effect is regarded as harmful (Rahman et al. 2020). The parameters whose effects renders drinking water polluted or safe are referred to as Cf. Håkanson (1980) classified Cf into four categories which includes : Class 1 (Cf < 1); Class 2 (1 ≤ Cf < 3); Class 3 (3 ≤ Cf < 6); and Class 4 (Cf ≥ 6). Degree of contamination is the sum of Cfs in the water samples. In this study Cd was calculated as follows;
\(Cd={\sum }_{i=1}^{n}\left(Cf\right)\) -------------2
Where Cf = (CAi / m) – 1; CAi = measured concentration of the metal, m = permissible value of the metal whose values are just as m used for MI
The classification of Cd and MI in drinking water as proposed by Håkanson (1980) is shown in Table 2
| Cd | Contamination level | Class | MI | Contamination level | class |
|---|---|---|---|---|---|
| < 1 | Low | 1 | < 0.3 | Very pure | 1 |
| 1 < Cd | moderate | 2 | 0.3-1.0 | Pure | 2 |
| Cd > 3 | High | 3 | 1.0–2.0 | Slightly | 3 |
| 2.0–4.0 | Moderately | 4 | |||
| 4.0–6.0 | Strongly | 5 | |||
| > 6.0 | Seriously | 6 |
Health Risk Assessment
The health risk evaluation was done by calculating the daily exposure dose (mg kg−day−) using guidelines published by US-EPA (1989). The daily exposure dose was determined for both adults and children. The daily exposure dose evaluates the risks posed by ingesting a single metal (Ukah et al. 2019). The daily exposure was used in evaluating the hazard quotients (HQ) and hazard indices (HI) of individual water samples.
Daily exposure dose is the chronic daily intake of the metals in the water samples and it is the ratio of the products of : the analytical concentration of the metal under investigation ,C (mg /L); the water ingestion rate W (1 litre for children and 2 litres for adults); exposure frequency (EF) and duration of exposure (ED) to the product of: body weight of consumer (B) and average exposure time (T) for both children (2190 days) and adults (25,550 days) (Mgbenu and Egbueri 2019).
Daily exposure dose (mg kg− day−) = \(\frac{C X W X EF X ED}{B X T}\) --------- 3
HQ evaluated the health risk of ingesting a single metal for a given duration. HQ was calculated using reference doses (Rm) of specific metals (Fe, Zn, Cu and Mn)
HQ = \(\frac{Daily exposure dose}{Rm}\) ----------- 4
Where Rm values were: (0.7) Fe, (0.3) Zn, (0.04) Cu and (0.046) Mn (Tyopine et al. 2022). HI was used to classify the carcinogenic risk associated with the water samples. Based on US-EPA (1989) classification, HI > 1 implies that there is health risk in ingesting the elements at the prevailing analytical concentrations, while the reverse (HI < 1) shows little or no health risk involved in drinking the water sample. Mgbenu and Egbueri (2019) and Bortey- Sam et al (2015) classified the health risk based on HI as negligible (< 0.1), low risk (≥ 0.1 < 1), medium risk (≥ 1 < 4) and high risk (≥ 4). HI is therefore the summation of HQ values of metals in a water sample
The mean metal concentrations of water samples in dry and rainy season are summarised in Table 3.
| Site | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| element | season | N1 | N2 | S1 | S2 | E1 | E2 | W1 | W2 | F | metal X | m |
| Ca | Ds | 38.4 | 45.00 | 66.7 | 32.1 | 86.54 | 37.2 | 32.1 | 60.3 | 26.3 | 47.18 | 80 |
| Rs | 68.7 | 75.2 | 80.9 | 63.8 | 102.2 | 54.7 | 32.2 | 37.8 | 77.7 | 65.91 | ||
| Fe | Ds | 0.31 | 0.31 | 0.77 | 0.86 | 0.09 | 0.2 | 1.01 | 0.41 | 0.49 | 0.49 | 0.3 |
| Rs | 0.66 | 0.61 | 1.24 | 0.11 | 0.41 | 0.08 | 0.65 | 0.18 | 0.71 | 0.52 | ||
| Mn | Ds | 0.78 | 0.54 | 0.08 | 0.31 | 0.49 | 0.48 | 0.09 | 0.40 | 0.19 | 0.37 | 0.1 |
| Rs | 0.24 | 0.32 | 0.01 | 0.46 | 0.26 | 0.24 | 0.16 | 0.16 | 0.38 | 0.25 | ||
| Cu | Ds | 0.58 | 0.71 | 1.38 | 0.88 | 0.64 | 1.31 | 1.08 | 0.09 | 0.003 | 0.74 | 1 |
| Rs | 0.80 | 1.18 | 1.44 | 1.02 | 0.86 | 1.76 | 2.02 | 0.65 | 0.92 | 1.18 | ||
| Al | Ds | 0.19 | 0.11 | 0.66 | 1.06 | 0.71 | 0.66 | 0.008 | 0.04 | 0.006 | 0.38 | 0.2 |
| Rs | 0.04 | 0.15 | 0.26 | 1.00 | 1.08 | 1.10 | 0.06 | 0.19 | 0.32 | 0.47 | ||
| Zn | Ds | 0.93 | 0.78 | 1.00 | 0.63 | 0.55 | 0.33 | 0.14 | 1.00 | 0.46 | 0.65 | 0.3 |
| Rs | 1.13 | 1.08 | 0.70 | 0.78 | 0.96 | 0.73 | 0.04 | 1.01 | 0.88 | 0.81 | ||
| Site X | Ds | 6.87 | 7.91 | 11.77 | 5.97 | 14.84 | 6.70 | 5.74 | 10.37 | 4.57 | ||
| Rs | 11.93 | 13.09 | 14.09 | 11.20 | 17.63 | 9.77 | 5.86 | 6.67 | 13.49 | |||
| X= mean, Ds = Dry season, Rs= Rainy season, m= permissible limits | ||||||||||||
The results showed gradual increase in metal concentration in the rainy season as compared to dry season. The abundance of the metals did not follow any clear pattern. However, calcium showed highest abundance in all sample sites and in both dry and rainy seasons. For convenience, the average metal concentration (Metal X) showed clearly higher metal effect in rainy season compared to dry season. Furthermore, the metal concentrations were greater than m values except for Ca and Cu during Ds, i.e., metal X < 80 and X < 1 (Table 3). The mean values of the metals were used to compute the Cf of individual metals and the metal index in the water samples for dry and rainy season as shown in Table 4.
| Rainy season | Dry Season | |||||
|---|---|---|---|---|---|---|
| element | Cf | MI | Cf | MI | ||
| Ca | -0.18 | 0.82 | -0.41 | 0.59 | ||
| Fe | 0.73 | 1.73 | threshold of warning | 0.63 | 1.63 | threshold of warning |
| Mn | 1.5 | 2.5 | threshold of warning | 2.7 | 3.7 | threshold of warning |
| Cu | 0.18 | 1.18 | threshold of warning | -0.26 | 0.74 | |
| Al | 1.35 | 2.35 | threshold of warning | 0.9 | 1.9 | threshold of warning |
| Zn | 1.7 | 2.7 | threshold of warning | 1.17 | 2.16 | threshold of warning |
| Cf = Contamination Factor, MI = Metal index, MI > 1 = threshold of warning (Bakan et al. 2010) | ||||||
The Cf of all the metals reveals that they were of low effect in contaminating the water and therefore fell within category 1 and 2. Table 5 shows the Cd of water from the nine sample sites both in dry and rainy season. The Cd classified drinking water quality into 3 classes as illustrated in Table 2. Based on the classification shown in Table 2, the Cd values of all water sampled in both seasons are classified in class 1 and characterised as low-contaminated water. The health risk assessment evaluated by hazard indices is summarised in Table 6. The chronic risk levels of adults and children ranged from low to medium level in both seasons. Although all chronic risk levels are acceptable, however children have higher health risk from dinking waters from the study area.
| Sample site | MI Rs | classfication | Cd Rs | classification | MI Ds | classification | Cd Ds | classification |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.85 | Pure (class 2) | -0.15 | Low (class 1) | 0.49 | Pure (class2) | -0.51 | Low(class 1) |
| 2 | 0.93 | Pure (class 2) | -0.07 | Low (class 1) | 0.56 | Pure (class 2) | -0.44 | Low(class 1) |
| 3 | 0.99 | Pure (class 2) | -0.01 | Low (class 1) | 0.83 | Pure (class 2) | -0.17 | Low(class 1) |
| 4 | 0.79 | Pure (class 2) | -0.21 | Low(class 1) | 0.42 | Pure (class 2) | -0.58 | Low(class 1) |
| 5 | 1.25 | Slightly affected (class 3) | 0.25 | Low(class 1) | 1.05 | Slightly affected (class 3) | 0.05 | Low(class 1) |
| 6 | 0.69 | Pure (class 2) | -0.31 | Low(class 1) | 0.48 | Pure (class 2) | -0.53 | Low(class 1) |
| 7 | 0.42 | Pure (class 2) | -1.42 | Low(class 1) | 0.41 | Pure (class 2) | -0.59 | Low(class 1) |
| 8 | 0.47 | Pure (class 2) | -0.53 | Low(class 1) | 0.78 | Pure (class 2) | -0.26 | Low(class 1) |
| 9 | 0.96 | Pure (class 2) | -0.04 | Low(class 1) | 0.32 | Pure (class 2) | -0.68 | Low(class 1) |
| Ds = dry season, Rs= Rainy season, Cd = degree of contamination, Cd <1 = low, 1< Cd = moderate, Cd >3 = High contamination | ||||||||
| Sample area | season | HI (Adult) | Chronic risk level | HI (children) | Chronic risk level |
|---|---|---|---|---|---|
| N1 | DS | 1.000 | Low | 2.3333 | Medium |
| RS | 0.8550 | Low | 1.9951 | Medium | |
| N2 | DS | 0.9295 | Low | 2.1690 | Medium |
| RS | 1.1694 | Medium | 2.7290 | medium | |
| S1 | DS | 1.1621 | Medium | 2.6360 | medium |
| RS | 1.1521 | Medium | 2.5900 | medium | |
| S2 | DS | 0.9162 | Low | 2.1380 | medium |
| RS | 1.0931 | Medium | 2.5500 | medium | |
| E1 | DS | 0.8173 | Low | 1.9080 | medium |
| RS | 0.8839 | Low | 2.0630 | medium | |
| E2 | DS | 1.2734 | Medium | 2.9710 | medium |
| RS | 1.4790 | Medium | 3.4510 | medium | |
| W1 | DS | 0.8819 | Low | 2.0580 | medium |
| RS | 1.5726 | Medium | 3.6690 | medium | |
| W2 | DS | 0.4247 | Low | 0.9910 | low |
| RS | 0.6672 | Low | 1.5570 | medium | |
| F | DS | 0.1840 | Low | 0.4290 | low |
| RS | 1.0060 | Low | 2.3470 | medium |
Assessment of Metal Concentrations in Water Samples
Table 3 shows higher concentrations of metals in rainy season compared to dry season. The results showed varying concentrations of the metals, with calcium recording the highest level (102.2 mg/L, and 86.54 mg/L) in both rainy and dry seasons, respectively. Calcium showed the highest values in both seasons in E1. E1 was the only site whose calcium values were higher than permissible value i.e m. The highest concentration of Fe (1.24 mg/L) was recorded in rainy season in S1. The highest value of Fe in the dry season was recorded in W1 (1.01 mg/L). The abundance of Fe is probably from soil geochemistry and abundance of metal in the soil as occasioned by high geoaccumulation (Tyopine et al. 2018). Manganese (Mn) showed highest concentration (0.78mg/L) in dry season in N1. Generally, Mn showed decrease in concentration in the rainy season across sample sites except in S2, W1 and F. The concentrations of Mn in this study (Table 3) exceeded permissible limits (m). The concentrations of Mn are probably from rocks and acquifers since Mn occur in rocks and soils and are wide spread in the environment (Ngele et al. 2014). The concentrations of Fe and Mn obtained in this study were higher than those analysed in Amike-Aba in Ebonyi state (unpublished). Copper (Cu) showed consistent elevated levels in rainy season when compared to Cu values in dry season. The highest Cu values were recorded in S1 (1.44 mg/L, 1.38 mg/L) for both rainy and dry season. The highest Al value (1.10 mg/L) was recorded in E2 in rainy season while its highest value in dry season (1.06 mg/L) was in S2. The variation in concentrations was not wide. Thus, either of meteorologic or geologic factors was responsible for the concentration of Al at the selected sites. Complexes of aluminium with organic acids reduce aluminium toxicity to organisms. Iron and manganese exhibits similar chemistry and this makes them bioavailable in aquatic biota. Their presence in water increases water acidity (De Anda et al. 2019). Zinc had its highest (1.13 mg/L) values recorded in N1 in rainy season and 1.00 mg/L in dry season in S1 and W2. Generally, elevated levels of Zn were observed in rainy season compared to dry season. Table 3 showed average of metal concentrations across sample sites (metal X) to be generally higher in rainy season, except for Mn whose average was greater in dry season. This may be influenced by a geologic factor and not a meteorological factor. Table 3 also showed that all metals regardless of season where far greater than m, except Ca. The relative abundance of tested metals was in the order: Ca > Cu > Zn > Fe > Al > Mn.Ikwo is located in an area mineralized with galena, sphalerite, arsenopyrite, marcasite and other associated minerals and weathering of these minerals can yield these metals in aquifers (Omaka et al. 2017; Aghamelu et al. 2011). The high concentration of these metals (Ca, Fe, Zn, Cu, Mn and Al) in ground water samples in Ikwo is most likely from the aquifer rock chemistry and little or no input from the impact of mining or agriculture. Fertilizers contain metals and despite a heavy application to farms in the area, the release of observed concentration of the metals into the ground water did not cause outrageous metal contamination as shown in Table 4. The metal concentration may be attributed to weathering of underlying parent rocks.
Contamination Indices of Water Samples from Sites
This study is pioneer in assessing seasonal variation in heavy metal of drinking water in Ikwo. The extent of contamination of water by the metals is further described by metal index and degree of contamination in Table 5. The highest metal index of rainy season was 1.25 and 1.05 in dry season. Based on classification of MI of quality of drinking water, all sites, except W1 fell in class 2 (Table 2 &5). Water obtained from E1 is categorised as slightly affected (Table 5). All water samples were pure and safe to drink. There was a consistent increase in MI across all sites in rainy season, with the highest value observed in E1 (1.25) and water from E1 is classified as 2 (Table 5). The MI results for Zn and Cu in this study compared with those in neighbouring Enyigba community bordering Ikwo. The metal index of the metals was also classified in class 1 (Aloke et al. 2019) but no mention was made of the season in which the study was conducted. Higher MI values indicate the greater impact of the metals to contaminating the water sample under investigation. The highest class of drinking water by MI is 6 and this is when water is seriously contaminated (Bakan et al. 2010).
The range of degree of contamination (Cd) was (-0.53 to 0.25). During the rainy season, the highest value of Cd (0.25) was observed in E2 while the lowest value (-0.53) was in W2. In the dry season, the highest value of Cd recorded (0.05) was in E1 and lowest, (-0.68) was in F. Based on classification of degree of contamination, all water sampled in both seasons are classified as low contaminated waters (Table 2). The results in this study indicated the degree of contamination of the environment to be from natural processes rather than anthropogenic activities.The findings from the different sources of drinking water irrespective of season shows that the metals in them were higher than permissible limits (m) but they have low effect on contamination of the water accessed for drinking in Ikwo.
Health Risk Assessment
The health risk posed by ingesting metals in the ground water samples is shown by HI of the elements analysed in the samples that represents the nine areas of Ikwo and environs (Table 6). The HI values ranged from ≥ 0.1 to < 4 and therefore are categorized into low and medium levels of chronic health risk. Both categories are fit for drinking. However, the majority of water samples were of medium health risk in rainy season. Although suitable for drinking, it still suggests greater threat to health. There was higher risk for children. The medium risk levels were as a result of abundance of Cu. Consuming water with elevated levels of metals poses health risks like brain damage, kidney failures, nervous system destruction etc (Nyantakyi et al. 2019). Therefore, simple treatment of water like boiling or using resins is required before drinking.
This pioneer study of seasonal monitoring of drinking water quality used metal index and degree of contamination of water sourced from boreholes and dug wells in Ikwo rural communities to monitor water quality in dry and rainy season. The results indicated that all metals analysed in this study (Ca, Fe, Zn, Mn, Al and Cu) exceeded WHO (m) for drinking water, which is a consequence of high geoaccumulation of the metals in the soil; however, their indices suggest that their contribution to water contamination is low. Thus, based on collaborative information given by metal index and degree of contamination, the water samples are safe drinking water samples. The results revealed higher metal concentration in rainy reason but pose no immediate risk to human health. The levels of metals in Ikwo water is influenced mainly by either geological or meteorological factors and not anthropogenically. This study recommends sustainable monitoring of water quality to ensure good water supply to Ikwo and environs.
Acknowledgement
The authors are thankful to management of pollution research centre, Rivers State University, Port-Harcourt, Nigeria for availing their laboratory for this study.
Competing interest
The author declare they have no known competing interest concerning this work
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