Evaluation of Groundwater Vulnerability Using Soil and Hydrogeological Data in Parts of the Precambrian Oban Massif, Se Nigeria

doi:10.21203/rs.3.rs-1979540/v1

Research on the vulnerability assessment of groundwater in the crystalline basement of the Oban Massif was carried out using soil and hydrogeological data. The parameters employed as input data included static water level (SWL), organic matter content (OMC), cation exchange capacity (CEC), aquifer media character (AMC), recharge (R), and soil type (ST). Results show groundwaters west of the massif (65% areal cover) to be the most vulnerable, with a decrease in vulnerability towards the eastern arm of the massif. The correlation matrix indicates SWL, OMC, and CEC as being significant factors that influence vulnerability ratings. Under current conditions, contamination indicators Cl^- and NO3^- show no contamination in relation to vulnerability ratings. The methodological approach and findings support the assessment model being quick and cheap, and can possibly be replicated in other areas where input data is available for assessment.

Geology

groundwater

vulnerability index

vulnerability map

Oban Massif

Nigeria

Groundwater remains one of the most valuable natural resources, serving mainly drinking water requirements as well as other varied uses. This makes its contamination a major concern as it poses a clear and present danger to the sustainability of the resource and dependent habitats (Ekwere & Edet, 2017). This contamination can be through a variety of human activities because of the poor practices of waste disposal in the area from both domestic and industrial sources. The ever-increasing population, combined with expanding economic activities, puts a strain on aquifer systems (Ekwere & Edet 2015; 2017). These are evident in increasing withdrawals and the risk of contamination from chemical infiltration, dumping of pollutants and agricultural inputs (van Beyena et al., 2012). Also, indications are that interactions between surface and groundwater bodies increase the salinity of groundwater. All these factors may combine to make groundwater unfit for drinking and other domestic uses.

These possible contamination processes thus mandate the need for protection of groundwater resources, either as a preventive or remedial approach, as the case may require. This may be achieved by site-specific studies with major inputs from soil and hydrogeological investigations.

The assessment of groundwater vulnerability is reckoned to depend on certain factors: (1) the travel time of infiltration water and contaminants to the water table; Maxe and Johansson, 1998; (2) the attenuation capacity of the geologic materials through which the water and contaminants infiltrate; Edet, 2013; Kudamnya et al., 2021; and (3) the relative quantity of the contaminants that can reach the groundwater; Daly and Warren, 1998.

The sensitivity of an aquifer or groundwater to becoming contaminated as a result of activities at the land surface defines the concept of groundwater vulnerability (Vrba and Zaporozec, 1994).

According to Schnebelen et al. (2002), there are two types of vulnerabilities: intrinsic and specific. The intrinsic vulnerability represents the physical and hydrogeological characteristics that protect the groundwater contamination (Anane et al., 2013; Mondal et al., 2018). Specific vulnerability, on the other hand, defines the susceptibility of groundwater to a specific pollutant or group of pollutants (Anane et al., 2013).

Groundwater vulnerability assessments thus provide a veritable tool for groundwater management and protection, and some of the applications of this tool are summarized in Table 1.

The groundwater vulnerability assessment methods generally consider geology, hydrogeology, soil, topography, and recharge. This current research takes cognizance of soil characteristics, geology, static water level, and recharge, as these are assumed to be the major factors that control groundwater vulnerability in the study area. This is so because most aquifers are dominantly shallow, taking residence in fractured and weathered subsurface horizons.

Table 1

Some vulnerability assessment models and their applications worldwide
Author	Aquifer type	Region (country)	Models used
Lobo-Ferriera & Oliviera (1997)	Sedimentary	Setubal (Portugal)	DRASTIC, SINTACS, GOD, AVI, SI
Rodney (2006)	Sedimentary	Carrol, Chariton (USA)	DRASTIC, Pesticide DRASTIC
Edet (2013)	Sedimentary	Calabar (Nigeria)	DRASTIC
Anane (2013)	Sedimentary	Cap-Bon (Tunisia)	DRASTIC, SI
Andreo et al., (2006)	Karst	Sierra de Libar (Spain)	PI, COP
Germain (2001)	Karst	Montana (Switzerland)	EPIK
van Beyena et al., (2012)	Karst	Central Florida (USA)	KAVI, SI
Jiménez et al., (2005)	Metasediments	Oaxaca (Mexico)	DRASTIC, AVI, GOD
Khadse & Kulkarni (2013)	Igneous (basalts)	Amravati (India)	DRASTIC

Description of study area

Oban Massif has an aerial extent of approximately 8,740 km2 and is located between longitudes 8 00 E–8 55 E and latitudes 5 00 N–5 45 N (Ekwere and Edet, 2012a & b).This Precambrian basement is a rugged geologic terrain on the southeastern fringe of Nigeria, bordering the Cameroon volcanic mountain range. The relief is undulating, straddled with isolated hills of up to 1,200 m above sea level. Deeply incised v-shaped valleys are also common features, and the hills are typically forested at their highest peaks.

The study area is well drained through weathered zones, fractured and jointed areas, coursing in two directions: southwards (seawards) and northwards to join the upper course of the Cross River in the Ikom depression (Ekwere and Edet, 2012a & b). The area experiences a tropical climate with two distinct seasons; wet (May-October) and dry (November-April). Temperatures are generally high with negligible diurnal and annual variations, with monthly averages in the order of 27–34 C (Ekwere and Edet, 2012a). Ekwere (2012) shows that the annual precipitation regime in the area is about 2,300 mm with an annual mean daily relative humidity and evaporation of 86% and 3.85 mm/day, respectively (Ekwere 2012). Regional run-off coefficients of the area are in the order of 0.21–0.61 and are due to topography and evaporation (Petters et al., 1989).

The Precambrian crystalline basement rocks include migmatites, granites, gneisses, and schists, exhibiting varying degrees of weathering across the massif. These are intruded by pegmatites, granodiorites, diorites, tonolites, monzonites, charnokites, and dolerites (Ekwueme and Ekwere, 1989; Ekwere and Ekwueme, 1991). Weathered profiles, fractures, and joints are prominent features within these rock suites, and they control groundwater movement and storage as the massif's main aquifers (Ekwere, 2012; Ekwere and Edet, 2012a).of weathered profiles is controlled by the variations in density and frequency of structural discontinuities across the massif, and this ultimately affects the spatial configuration of the hydrogeological system (Ekwere, 2012). The massif's regional hydrogeological differentiation (Fig. 2) reveals a three-layer hydro-stratigraphic model composed of (a) top unsaturated clayey sand (lateritic), (b) middle gravelly sand and decomposed bedrock, and (c) fresh bedrock (fractured), according to Okereke et al. (1998) and Ekwere (2012).

Groundwater in the area occurs under water table conditions in the weathered and fractured zones with a static water level at artesian conditions to a depth of 10.50 m across the massif (Ekwere, 2012). Groundwater yield variations also depend on the extent of fracturing and jointing (Ekwere et al., 2012).

Twenty-five soil samples were taken from different locations across the study area by the use of a graduated soil auger. The depth of the sampling was 0–30 cm, devoid of surface humus cover, and the samples were stored in well-labeled polythene bags at each location.

Collected samples were air dried in the laboratory, disaggregated and sieved through an automated sieve shaker into the different size fractions (clay, silt, and sand). Depths to subsurface water levels were measured from existing boreholes and wells during the field survey with the use of a dip-meter. According to Edet (2006), the estimated recharge was pegged at 20% of total precipitation.

Data on two indicators of pollution (Cl and NO₃) were obtained from Ekwere (2012) and Ekwere and Edet (2021) (see Table 1). These two indicators represent the influence of seawater and anthropogenic activities, respectively, and their values were compared to ratings of other parameters to establish possible relationships with the vulnerability of groundwater.

The assessment of the groundwater was based on the following parameters: depth to groundwater levels (SWL), cation exchange capacity (CEC), organic matter content (OMC), aquifer media character (AMC), recharge (R), and soil type (ST; and this was classed by percentages of soil fractions). These parameters were weighted based on their importance, with the highest rated 5 and the least rated 1 and further classified with ratings for each class (see Table 2).

Table 1

Chemical data as obtained from the study.
	Location
Sample No.	Latitude	Longitude	Cl⁻	NO₃⁻
1	N5˚ 25’ 23.0”	E8˚ 10’ 28.5”	5.98	1.62
2	N5˚ 27’ 09.4”	E8˚ 09’ 07.2”	6.00	5.89
3	N5˚ 27’ 51.0”	E8˚ 10’ 26.3”	2.50	0.003
4	N5˚ 21’ 01.0”	E8˚ 21’ 14.0”	3.49	0.89
5	N5˚ 18’ 55.1”	E8˚ 21’ 14.9”	5.00	0.005
6	N5˚ 18’ 53.3”	E8˚ 21’ 05.0”	14.49	8.21
7	N5˚ 15’ 20.8”	E8˚ 21’ 05.2”	33.96	35.18
8	N5˚ 15’ 13.9”	E8˚ 21’ 37.3”	56.89	47.51
9	N5˚ 18’ 35.2”	E8˚ 34’ 35.4”	19.49	10.89
10	N5˚ 18’ 55.0”	E8˚ 34’ 51.3”	10.00	11.25
11	N5˚ 27’ 44.9”	E8˚ 43’ 35.9”	4.00	3.57
12	N5˚ 06’ 19.4”	E8˚ 30’ 58.6”	21.56	21.20
13	N5˚ 25’ 21.0”	E8˚ 10’ 25.3”	10.52	6.26
14	N5˚ 26’ 10.0”	E8˚ 09’ 07.7”	32.99	45.89
15	N5˚ 28’ 41.1”	E8˚ 11’ 42.2”	2.06	0.54
16	N5˚ 23’ 02.0”	E8˚ 15’ 36.8”	9.30	8.04
17	N5˚ 24’ 28.5”	E8˚ 16’ 18.1”	0.80	4.64
18	N5˚ 24’ 23.0”	E8˚ 16’ 17.3”	92.97	23.04
19	N5˚ 24’ 34.6”	E8˚ 16’ 23.5”	22.79	40.01
20	N5˚ 23’ 05.7”	E8˚ 15’ 47.6”	21.93	19.82
21	N5˚ 23’ 01.8”	E8˚ 15’ 46.4”	87.44	3.14
22	N5˚ 21’ 02.0”	E8˚ 16’ 56.5”	23.49	15.71
23	N5˚ 21’02.1”	E8˚ 16’ 56.8”	17.99	19.46
24	N5˚ 21’ 04.2”	E8˚ 16’ 57.9”	17.05	40.89
25	N5˚ 18’ 55.3”	E8˚ 21’ 20.2”	8.52	4.83

Vulnerability parameters description

Depth to the water's surface

The depth to which the water table is at is important as it determines the depth of the travel path through which a contaminant must travel before reaching the aquifer (Aller et al., 1987; Al-Zabiet, 2002). The depth determines the contact time with the surrounding media and indicates the maximum opportunity for pollutant attenuation (Aller et al., 1987). Consequently, a contaminant would take a longer time to reach water levels at greater depths than at shallower levels, and this depth supports pollutant attenuation through physico-chemical retention or reaction. The study area's depth to water level ranged from 0.48m to 10.50m, with a mean of 5.84m.

Depth to subsurface water level was assigned a weight of 5, while the rating classifications were: high (depth 2m), moderate (depth 2-5m) and low (depth > 5m).

Table 2

Weights and ratings as assigned to input parameters.
		Rating
		1	2	3
Parameter	Weight	Low	Moderate	High
SWL (m)	5	> 5	2–5	< 2
OMC (%)	4	< 1	1–3	> 3
CEC (meq/100g)	4	< 5	5–10	> 10
AMC	3	Clay-Silt	Silt-Fine Sand	M-C Sand-Gravel
Recharge (mm)	2	< 300	300–500	> 500
Soil Type	1	Clay	Silt	Sand

Organic Matter Content (OMC) and Cation Exchange Capacity (CEC)

It is believed that extractions of contaminants from particulate fractions of soils are controlled mainly by organic content and CEC. Bekesi & McConchie (2000) emphasize the importance of sorption of contaminants into soil particles as a major process in vulnerability assessments. They also suggest that an estimation of soil sorption capacity and the partition coefficient of a medium is a function of its organic matter content and cation exchange capacity. Hence, an increase in OMC and/or CEC increases the accumulation potential of contaminants in the soil matrix.

Data from analysis revealed that OMC ranged from 1.60–4.40% with a mean of 3.07, while CEC ranged between 1.53–23.20 meq/100g with a mean of 8.52 meq/100g. Both parameters were assigned a weight of 4 and rated as follows: OMC > 3.00% (high), 1.00–3.00 (moderate) and 1.00 (low), while CEC > 10.00meq/100g (high), 5.00–10.00 meq/100g (moderate) and 5.00 meq/100g (low).

Aquifer Media Characteristics

The nature of the aquifer matrix material determines the extent of subsurface processes such as infiltration, sorption, cation exchange, etc. The matrix and inherent processes ultimately affect the quality and vulnerability of groundwater. This makes the texture of the aquifer very important in groundwater vulnerability assessment. The pathway of a contaminant thus depends on the permeability of the subsurface horizon. Thus, the lower the permeability and the thicker the sequence, the higher the dilution effect and the lower the contamination risk (Edet, 2004). Aquifer media characteristics were gotten from drill logs of boreholes within the study area, and the materials were dominantly gravel and sand, with less silt and clay content. The aquifer media character was assigned a weight of 3, while the ratings were as follows: high (medium-coarse and gravel), moderate (fine sand-silt), and low (clay-silt).

Recharge

Recharge, and primarily precipitation water, is responsible for contaminant conveyance vertically to the aquifer and horizontal movement within the aquifer through percolation and infiltration. The amount of recharge water necessary for dispersion and dilution within the vadose and saturated zones is controlled by the amount of recharge water.

Table 3

Data set for vulnerability index assessment
Sample	SWL (m)	OMC (%)	CEC (meq/100g)	Aquifer media	Recharge (mm)	Sand (%)	Silt (%)	Clay (%)	Soil class
1	6.20	4.40	23.20	Medium grain sand	280	90.30	7.70	2.00	S
2	3.21	3.10	3.20	Silty sand	240	88.40	8.10	3.50	S
3	5.80	2.60	10.20	Silty sand	440	77.80	19.20	3.00	SL
4	6.00	3.80	8.60	Silty sand	340	82.40	12.40	7.60	SL
5	0.48	4.30	9.70	Silty sand	600	84.80	9.60	5.60	SL
6	0.18	3.60	5.70	Silty sand	480	84.60	11.60	3.80	SL
7	7.00	3.90	12.90	Medium grain sand	420	90.20	5.40	4.40	S
8	6.80	3.50	2.60	Silty sand	260	89.20	6.70	4.10	S
9	7.00	2.10	2.90	Silty sand	280	85.60	10.70	3.70	S
10	6.20	1.60	10.60	Silty sand	420	84.60	14.80	4.60	SL
11	5.80	2.60	3.60	Silty sand	620	86.40	8.40	5.20	S
12	5.05	1.80	9.40	Silty sand	540	84.60	11.20	4.20	SL
13	1.36	2.90	5.30	Silty sand	420	78.20	13.40	8.40	SL
14	3.10	2.30	18.40	Silty sand	280	88.40	7.50	4.10	S
15	0.65	3.80	22.60	Medium grain sand	480	90.40	6.80	2.80	S
16	10.51	4.30	13.80	Silty sand	360	89.20	7.20	3.60	S
17	0.62	2.60	2.80	Silty sand	540	80.80	14.00	5.20	SL
18	3.76	4.10	10.40	Silty sand	510	88.40	7.00	4.60	S
19	4.92	3.10	3.80	Medium grain sand	480	90.20	6.00	3.80	S
20	1.87	2.70	5.14	Medium grain sand	280	92.40	4.40	3.20	S
21	0.78	3.60	1.53	Silty sand	520	88.60	8.80	2.60	S
22	5.34	2.40	8.74	Silty sand	460	84.20	12.60	3.20	SL
23	5.36	1.80	7.20	Medium grain sand	380	90.40	6.80	2.80	S
24	4.40	2.20	4.84	Silty sand	280	88.60	6.00	5.40	S
25	5.67	3.60	5.76	Silty sand	340	89.60	6.20	4.20	S

Consequently, the potential for pollution increases with recharge (Aller et al., 1987). Net recharge has been described as (1) the amount of water per unit area of land which penetrates the ground surface and reaches the water table (Anane et al., 2013) and (2) the total quantity of water which infiltrates from the surface to the aquifer (Wen et al., 2009). For this study, net recharge was pegged at 20% of precipitation after Edet (2006), and it ranged from 240–620mm, with an average of 410mm. The recharge was assigned a weight of 2, and the ratings were as follows: high (> 500mm), moderate (300–500mm), and low (300mm).

Soil Type

Soil is defined as the uppermost portion of the weathered zone of the earth with an average thickness of 1.00 m or less (Aller et al., 1987) and is characterized by significant biological activity (Anane et al., 2013). Soil has control on the amount of recharge water that can infiltrate the ground and possibly carry contaminants into the vadose zone. It has the potential to adsorb and attenuate surface and near-surface derived pollutants (Edet, 2013). The soil is also described as the most chemically active part of the unsaturated zone and has the highest content of organic matter that can attenuate the movement of certain heavy metals, in addition to having a high cation exchange capacity (Palmer et al., 1995). According to Aller et al. (1987), attenuation processes such as filtration, biodegradation, sorption, and volatization may be quite significant in the soil zone.

Generally, soil pollution potential is largely affected by the amount of sand, silt, and clay in the soil, and the potential decreases with a decrease in grain size and vice versa. In the study area, sands ranged from 77.8–92.4% with an average of 86.73%, silt from 4.4–19.2% with a mean of 9.30%, and clay from 2.0–7.6% with a mean of 4.22%. These values indicate that the soils are dominantly sands (S) and sandy loam (SL). This parameter was assigned a weight of 1, rated as low (clay), moderate (silt) and high (sand).

Vulnerability Index Computations

The vulnerable index was computed from the above-mentioned parameters using the formula below:

VI = SWLW SWLR + CECW CECR + OMCW OMCR + RW RR + STW STR

Where, W and R represent the weight and ratings of parameters.

Computed vulnerability indices are then classified subjectively as presented in Table 4.

Table 4

Description for vulnerability levels
Class	Vulnerability Index	Vulnerability Level
I	< 35	Low
II	35–40	Moderate
III	> 40	High

Table 5 presents results from the computation of ratings for each of the vulnerability assessment parameters as well as their corresponding total ratings. The indications from computed ratings show that 65% of the area of investigation is of high vulnerability (> 40). Thirty-five (35%) of the area is of moderate vulnerability (35–40), while 20% is of low vulnerability (35).

The groundwater vulnerability map (Fig. 3) shows the variations across the massif. From the assessment of the map, high vulnerability sections of the study area are on the western arm of the massif. This section of the massif hosts major rock and aggregate quarrying districts that have been operational for about fifty (50) years (Ekwere and Edet, 2021). This area is characterized by deeply weathered basements with well developed regolith profiles propagated by rock blasting and mining, as well as other anthropogenic influences. Pockets of areas within the western arm also indicate moderate vulnerability. The central portion of the massif indicates moderate vulnerability, while the north-western arm, which is thickly forested with low human population density, is dominantly of low vulnerability index. The designated vulnerability zones show apparent coincidence with vulnerability zones as determined by DRASTIC, GOD, and AVI vulnerability assessment models (Ekwere and Edet, 2015; Ekwere and Edet, 2017).

Pearson’s bivariate correlation analysis (at p 0.01) was carried out as a means of assessing the interrelationships between vulnerability input variables and the calculated vulnerability ratings. Table 6 shows the results of the correlation analysis. Variables showing r > 0.5 are considered to be strongly correlated, r > 0.3–0.5 shows moderate correlation, and r 0.3 shows weak correlation at a significance level (p) of 0.01.

A significant, strong positive correlation exists between groundwater vulnerability and the pair of SWL (0.680) and OMC (0.504). CEC (0.463), AMC (0.351), and R (0.313) all have a moderately positive correlation with groundwater vulnerability, whereas Cl- has a weakly positive correlation (0.164).ST and AMC show a moderate positive correlation (0.459), as an expected common relationship between the development of regolith and subsurface aquiferous horizons in most crystalline basements.

Table 5

Computed ratings and vulnerable indices.
	Rating
Location	SWL	OMC	CEC	AMC	R	ST	Total Rating
1	5	12	12	9	2	3	43
2	10	12	4	6	2	3	37
3	5	8	12	6	4	2.5	37.5
4	5	12	8	6	4	2.5	37.5
5	15	12	8	6	6	2.5	49.5
6	15	12	8	6	4	2.5	49.5
7	5	12	12	9	4	3	45
8	5	12	4	6	2	3	33
9	5	8	4	6	2	3	28
10	5	8	12	6	4	2.5	37.5
11	5	8	4	6	6	3	32
12	5	8	8	6	6	2.5	35.5
13	15	8	8	6	4	2.5	43.5
14	10	8	12	6	4	2.5	42.5
15	15	12	12	9	4	3	55
16	5	12	12	6	4	3	42
17	15	8	4	6	6	2.5	41.5
18	10	12	12	6	6	3	49
19	10	12	4	9	4	3	42
20	15	8	8	9	2	3	45
21	15	12	4	6	6	3	46
22	5	8	8	6	4	2.5	33.5
23	5	8	8	9	4	3	37
24	10	8	4	6	2	3	33
25	5	12	8	6	4	3	38

Table 6

Correlation matrix for input parameters, chemical data and vulnerability rating values
	SWL	OMC	CEC	AMC	R	ST	Cl	NO₃	Rating
SWL	1
OMC	0.104	1
CEC	-0.172	0.107	1
AMC	0.051	0.163	0.234	1
R	0.202	0.121	0.072	-0.270	1
ST	-0.127	0.362	-0.024	0.459	-0.354	1
Cl	0.103	0.240	-0.052	-0.090	0.204	0.300	1
NO₃	-0.144	-0.105	-0.103	0.160	-0.274	0.203	0.412	1
Rating	0.684	0.504	0.463	0.351	0.313	-0.036	0.164	-0.191	1

A groundwater vulnerability assessment approach based on inputs from hydrogeological parameters was carried out in the crystalline basement complex of the Oban Massif. The parameters were static water level (SWL), cation exchange capacity (CEC), organic matter content (OMC), aquifer media characteristics (AMC), recharge (R), and soil type (ST). Computation and plot of the vulnerability indices show the western sector of the massif to be the most vulnerable (covering about 65% of the massif). The vulnerability decreases as one move towards the massive and largely unweathered eastern sector. Correlation analysis shows SWL, OMC, AMC, and R to be the major parameters that control groundwater vulnerability. Pollution indicators (Cl^- and NO₃^-) have no bearing on groundwater vulnerability in the current physical environment. The groundwater vulnerability assessment method applied in this research offers a cheap and quick model that can be effectively used in other areas where the dataset inputs are available.

Declaration Statement

The authors declare that there no conflicts associated with the conception of this research all through to the production of the manuscript for publication.

Aller L, Bennet T, Lehr J. H, Petty R. J and Hackett G., (1987) DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. EPA/600/2-87-036, US Environmental Protection Agency, Washington DC.
Al-Zabiet T., (2002). Evaluation of aquifer vulnerability to contamination potential using DRASTIC method. Environ Geol. 43: 203–208.
Anane M, Abidi B, Lachaal F, Limam A and Jellali S., (2013). GIS-based DRASTIC, Pesticide DRASTIC and Susceptibility Index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia. Hydrogeol J 21: 715–731.
Andreo B, Goldscheider N, Vadillo I, Vias J. M, Neukum C, Sinreich M, Jiménez P, Brechenmacher P, Carraso F, Hötzl H, Perles M. J and Zwahlan F., (2006). Karst groundwater protection: first application of Pan-European approach to vulnerability, hazard and risk mapping in Sierra de Lĺbar (southern Spain). Sci Total Environ 357:54–73.
Bekesi, G and McConchie, J. (2000). Mapping soil sorption capacity as measure of regional groundwater vulnerability. Journal of Hydrology (NZ), 39 (1), 1–18.
Daly, D and Warren W, (1998). Mapping groundwater vulnerability: the Irish perspective. In: Robins NS (ed) Groundwater pollution, aquifer recharge and vulnerability. Geological Society London, Special Publications, vol 130, pp 179–190.
Edet, A. E., (2013). An aquifer vulnerability assessment of the Benin Formation aquifer, Calabar, south-eastern Nigeria, using DRASTIC and GIS approach. Environ Earth Sci DOI 10.1007/s12665-013-2581-y.
Edet, A., (2006). Groundwater vulnerability assessment using soil and hydrogeological data in petroliferous Niger Delta, southeastern Nigeria. Journal of Environmental Systems, vol. 32 (2).
Edet, A. E, (2004). Vulnerability evaluation of coastal plain sand aquifer with a case example from Calabar, south-eastern Nigeria. Environ Geol 45: 1062–1070.
Ekwere, A. S., (2012). Hydrogeochemical framework of the Oban Massif, south-eastern Nigeria: A baseline for hydrogeochemical assessment and monitoring. Published PhD thesis. Lambert Academic Publishing (LAP) GmbH, Germany pp.173.
Ekwere, A. S., and Edet, B. B., (2021). Temporal variations of heavy metals in sediments, soils and dust particulates across the rock quarrying districts of the Oban Massif, southeastern Nigeria. Environment Nanotechnology, Monitoring & Management 15 (100431).
Ekwere, A. S., & Edet, A. S. (2017). A comparative assessment of vulnerability of the Oban Massif aquifer system, SE Nigeria using DRASTIC, GOD and AVI models. International Journal of Science and Engineering Investigations, 6(68), 39–45.
Ekwere, A. S and Edet, A. E., (2015). Vulnerability Assessment of Aquifers within the Oban Massif, South-Eastern Nigeria, using DRASTIC method. International Journal of Scientific & Engineering Research 6(10) pp. 1123–1136.
Ekwere, A. S and Edet, A (2012a).Trace metals in ground and surface waters of the Oban Massif area, SE Nigeria. Advances in Applied Sciences Research (AASR). Vol.3, No. 1 pp 312–318.
Ekwere, A. S. & Edet, A.E. (2012b). Distribution and chemical speciation of some elements in the groundwater of Oban Area, South Eastern Nigeria. Research Journal of Environmental and Earth Science, 4(3), 207–214.
Ekwere, A. S., Edet, A. E. & Ekwere, S. J. (2012). Groundwater chemistry of the Oban Massif, South-Eastern Nigeria. Revista Ambi-Agua, Taubaté - An Interdisciplinary Journal of Applied Science, 7(1), 51–66.
Ekwere, S. J., & Ekwueme, B. N., (1991). Geochemistry of Precambrian gneisses in the eastern part of the Oban massif, south-eastern Nigeria. Geologie en Mijnbouw 70: 105–114. Kluwer Academic Publishers.
Ekwueme, B. N and Ekwere, S. J., (1989). The geology of Eastern Oban massif, SE Nigeria. Jour Min Geol 25: 317–329.
Germain, C., (2001). Karst vulnerability of Montana region (Valais, Suisse): application of EPIK model. Bull Hydrogéo 19:145–146.
Jiménez, S. I. B, Campos-Enríquez, J. O and Alatorre-Zamora, M. A., (2005). Vulnerability to contamination of the Zaachila aquifer, Oaxaca Mexico. Geofísica Internacional 44(3): 283–300.
Khadse, S. P and Kulkarni, S. V., (2013). Groundwater vulnerability assessment through DRASTIC model for Amravati taluka, Amravati district, Maharashtra. Intern J of Sci amd Engr Res. 4(6): 267–274.
Kudamnya, E. A, Edet, A. E and Ekwere, A. S., (2021). Analyzing principal components of physiographic factors affecting groundwater occurrences within Keffi, north-central Nigeria. Egyptian Journal of Remote Sensing and Space Sciences. http://doi.org/10.1016/j.ejrs.2021.08.005
Lobo-Ferreira, J. P and Oliveira, M. M., (1997). DRASTIC groundwater vulnerability mapping of Portugal. In: groundwater: an endangered resource. Proceedings of Theme C of the 27th Congress of the International Association for Hydraulic Research. San Francisco, August 1997.
Maxe, L and Johansson, P. O., (1998). Assessing groundwater vulnerability using travel time and specific surface area as indicators. Hydrogeol J 6: 441–449.
Mondal, N. C., Adike, S and Ahmed, S., (2018). Development of entropy-based model for pollution risk assessment of hydrogeological system. Arabian Journal of Geosciences (14).
Okereke, C. S., Esu, E. O., & Edet, A. E., (1998). Determination of potential groundwater sites using geological and geophysical techniques in the Cross River State, southeastern Nigeria. Jour. of African Earth Sci. Vol. 27, No 1, pp. 149–163.
Palmer, R. C, Holman, I. P and Lewis, M. A., (1995). Guide to groundwater vulnerability mapping in England and Wales. HMSO, London.
Petters, S. W., Adighije, C. I., Essang, E. B., & Ekpo, I. E., (1989). A Regional Hydrogeological Study of rural water supply options for planning and implementation of phase II rural water programme in Cross River State, Nigeria. Rept. for Direct. Of Rural Devt. CRSG, Nigeria.
Rodney, C. S., (2006). Groundwater vulnerability to agrochemicals: a GIS-based DRASTIC model analysis of Caroll, Chariton, and Saline counties. University of Missouri, Colombia, Missouri, USA.
Schnebelen, N, Platel, J. P, Nindre, Y and Baudry, D., (2002). Water management in Aquitaine 5. Sectorial operation. Oligocene aquifer protection in the Bordeau region. BRGM, Orléans, France.
Van Beynena, P. E, Niezielski, M. A, Bialkowska-Jelinskaa, E, Alsharif, K and Matusick, J., (2012). Comparative study of specific groundwater vulnerability of a karst aquifer in central Florida. Appl Geogr 32: 868–877.
Vrba, J and Zaporozec, A., (1994). Guidebook on mapping groundwater vulnerability. IAH International Contribution for Hydrogeology. Heise, Hannover, Germany.
Wen, X, Wu, J and Si, J., (2009). A GIS-based DRASTIC model for assessing shallow groundwater vulnerability in the Zhange Basin, North China. Environ Geol 57: 1435–1442.

Evaluation of Groundwater Vulnerability Using Soil and Hydrogeological Data in Parts of the Precambrian Oban Massif, Se Nigeria

Status:

Version 1

Abstract

Figures

Introduction

Methodology

Depth to the water's surface

Organic Matter Content (OMC) and Cation Exchange Capacity (CEC)

Aquifer Media Characteristics

Recharge

Soil Type

Results And Discussions

Vulnerability Index Computations

Conclusion

Declarations

References

Status:

Version 1