Effects of Waste Sanitation Facilities on Water Quality Index and Human Health Risk Assessment of Groundwater Resources in Abeokuta Markets, Southwestern Nigeria

doi:10.21203/rs.3.rs-1754356/v1

Groundwater is a sustainable resource that provides potable water for millions of people. Contamination of this resource may, therefore, pose serious health threats to consumers. This study aimed at assessing the effects of waste sanitation facilities on water quality index and health risk of groundwater resources in Abeokuta markets, southwestern Nigeria. Fifty hand-dug wells at varying distance of 10–81 m to the waste sanitation facilities from ten markets in the Abeokuta metropolis were seasonally monitored at wet and dry seasons. Five hand-dug wells from a community with no waste sanitation facility were sampled to serve as a control. Groundwater samples were collected and analysed for physical and chemical parameters using the standard methods. Data were subjected to descriptive and inferential statistics. Water quality index and health risk assessment (non-carcinogenic hazard quotient [HQ], and cancer risk [CR]) were estimated in groundwater. Significantly higher levels of total dissolved solids, electrical conductivity, dissolved oxygen, biological oxygen demands, and heavy metals were observed in groundwater from the market area than in the control site. Furthermore, the WQIs of groundwater from the market areas were higher than 300, indicating unfit water for drinking. The health risk assessment showed high HQs greater than the acceptable limit of 1.0 for Mn and Pb, establishing non-carcinogenic adverse health effects. Co also indicated carcinogenic effects in the market area groundwater with a CR value greater than the permissible limit of 1.0 x 10^− 4. This study established the detrimental effects of market wastes on the surrounding groundwater.

waste sanitation

groundwater quality

health risk

regression model

Globally, rapid economic growth, population explosion, climate change, and urbanization have led to an increase in water demand for domestic and drinking purposes (Boretti and Roza 2019; Zacchaeus et al. 2020). However, more than 884 million people worldwide lacked access to potable drinking water (CDC 2021a). In Nigeria, at least 52% of the population are denied access to clean drinkable water (Orebiyi et al. 2010). Potable water is essential for the healthy living and survival of humans. Drinking water could be contaminated by various human activities including industrialization, agriculture, urbanization, vehicular emission, improper waste disposal, and also by natural phenomena such as siltation, saltwater intrusion, weathering of bedrock, and radioisotopes (Taiwo 2012; Okamura et al. 2016).

Groundwater (in form of hand-dug wells and boreholes) is a source of drinking water for millions of people in the world, especially in developing countries, where public water supply is not available or distributed below demand (Taiwo 2012). When protected and well managed, groundwater is believed to be a source of pure and safe drinking water supply (Taiwo et al. 2015). It is purified in the soil column through processes such as filtration and ion exchange (Awonusi et al. 2016). However, improper waste disposal could threaten groundwater quality through contamination by runoff. Urban runoff could carry physical (solids), chemical (heavy metals), and microbiological (total coliform) materials from the disposal facilities and deposit them in hand-dug wells (Taiwo et al. 2011). The major microbiological diseases associated with the consumption of unsafe drinking water are diarrhea, cholera, and dysentery. The United States Center for Diseases Control has documented the yearly morbidity cases due to cholera, dysentery and diarrhea as3, 11, and 1700 million, respectively (CDC 2021). Chemical contaminants could initiate numerous illnesses such as damage to organs (kidney, liver, and lungs), impairment of central and peripheral nervous systems, cancer, DNA mutation, and birth defects (Järup, 2003; Taiwo and Awomeso, 2017). Exposure to unsafe drinking water in developing countries causes more than one million deaths annually (Ritchie and Roser, 2021).

The Ogun State Waste Management Authority (OGWAMA) was established in 2019 by Ogun State Government andis saddled with the responsibilities of the evacuation and clearance of market and public wastes, highway disposal, and street sweeping and cleaning activities (OGWAMA, 2022). In this regard, each market was equipped with a metal container for storing the market wastes, prior to evacuation. Unfortunately, the waste collection facilities are not big enough to cater to the huge amounts of generated market waste (Adelakun et al. 2019). The littering of these wastes around the sanitation facilities before evacuation by the OGWAMA could therefore have negative environmental consequences on the nearby groundwater. This study, therefore, seeks to assess the effects of waste sanitation facilities on water quality index and health risk of groundwater resources (hand-dug wells) in Abeokuta markets, Nigeria.

2.1 Study area

Abeokuta, the capital of Ogun State is an ancient city located on longitude 7° 09ʹ and 7° 39ʹ E,and latitude 3° 20ʹ and 3° 54ʹ N on the eastern bank of Ogun River, and 78 km north of Lagos in southwestern Nigeria (https://www.britannica.com/topic/role-of-Nigerian-women-1360615). The city is established around a group of rocky outcroppings that rose above the surrounding wooded savanna. The metropolis occupies four Local Government Areas viz: Abeokuta North, Abeokuta South, some areas in Odeda, and Obafemi Owode. Abeokuta covers about 2320 km² of land, which has continued to expand in all directions due to increasing urbanization and rapid population growth (Olayinkaet al. 2015).The study area falls within the tropical rainforest belt of Nigeria characterized by tall trees and dense grass undergrowth with two distinctive seasons (wet and dry) (Gbadebo 2021). In the study area, the annual rainfall, average monthly temperature, and relative humidity were ca 2,000 mm, 25.7–30.2°C, and > 50%, respectively (Gbadebo 2021).The geology of Abeokuta is a subset of the basement geology of Southwestern Nigeria comprising varieties of gneisses and older granites, which could be mapped as Early Phase Granodiorite and Quartz-diorite (Jones and Hockey 1964).

2.2 Sample collection and analysis

Fifty hand-dug wells in the vicinity of waste collection facilities at ten major markets (Kuto, Lafenwa, Oke-Ibukun, Iberekodo, Gbangba, Ijeun, Bode, Asero, Osiele, and Saje) in Abeokuta were studied. Hand-dug wells are distinctive examples of shallow wells that varied from 0.9 to 1.8 m in diameter and 4.5 to 10 m in depth, depending on the water-bearing formation (Orebiyi et al. 2010). The distance between the wells and the waste collection facilities ranged from 10 to 81 m. Groundwater samples were collected from the hand-dug wells at wet and dry seasons.

Five hand-dug wells were also assessed at a community far from the market areas to serve as control. A total of 110 groundwater samples were collected between July and December 2021, covering both wet and dry seasons. Groundwater samples for heavy metal analysis were collected in separate containers and fixed in-situ with 2 mL concentrated hydrochloric acid (Taiwo et al. 2013).

Groundwater samples were assayed for parameters including pH, temperature, electrical conductivity (EC), total dissolved solids (TDS), total hardness, calcium hardness, alkalinity, sulphate, nitrate, dissolved oxygen (DO), biological oxygen demand (BOD), total coliform (TC), and Escherichia coli.

Temperature, pH, TDS and EC were analysed electronically using a combined temperature/pH/EC/TDS meter (Hanna 98130 Digit Multi-meter), after the instrument has been calibrated with standard pH buffer 4 and 7 solutions. The temperature/pH/EC/TDS electrode was thoroughly rinsed with distilled water before measuring the temperature, pH, EC, and TDS of the groundwater samples in-situ at the point of collection.

Alkalinity was determined by a titrimetric method. The procedure entails the measurement of 100mL water sample into a 250 mL Erlenmeyer flask,afterward,2 drops of phenolphthalein indicator were introduced, and later a few drops of methyl orange indicator was added. The resulting solution was titrated against 0.05 M H₂SO₄for change of colour to peach. Total hardness was also determined titimetrically, where a 50 mL groundwater sample was titrated against 0.05 M EDTA in the presence of 1 mL ammonia buffer solution and 3 drops of Erichrome Black T (EBT) indicator. Calcium was also determined by EDTA titrimetric method. Biochemical oxygen demand (BOD) was measured by the dilution method described in Taiwo et al. (2013); concentrations of nitrate and phosphate were determined by spectrophotometry using the sodium salicylate method for nitrate and the vanado-molybdo-phosphoric acid method for phosphate.

Samples for metal analysis (100 mL) were digested with 10 mL concentrated HCl on a hotplate in a fume cupboard for 30 minutes. Digested samples were filtered (Whatman Filter, Pore size 1.0 µm), and analysed for heavy metals (Co, Cu, Fe, Mn, Pb, Zn) using atomic absorption spectrophotometry (AAS, Bulk Scientific, 210/211VGp, CT, USA).

Analytical quality control procedures observed during the laboratory analysis were:(1) using reagents of analytical grade (Sigma-Aldrich Chemie, GmbH, Germany),(2) running of blank samples to cancel the extracting reagents background matrix, and to calculate the detection limit of the analytical instrument.

2.3 Data analysis

Groundwater data were subjected to simple descriptive (mean and standard deviation) and inferential (Duncan multiple range test, principal component analysis, regression model) statistics using SPSS for Windows software package (version 22.0).

2.4 Evaluation of health risk of heavy metals

The human health risk assessment was determined for estimated daily intake (EDI), hazard quotient (HQ), hazard index (HI), cancer risk (CR) using the mathematical models presented in equations 1–3 (USEPA2001; 2002; 2007).

EDI = \(\frac{\text{C} \text{x} \text{I}\text{R} \text{x} \text{E}\text{F} \text{x} \text{E}\text{D}}{\text{B}\text{W} \text{x} \text{A}\text{T}}\) (1)

HQ = \(\frac{\text{E}\text{D}\text{I}}{\text{R}\text{f}\text{D}}\), HI = \(\sum _{\text{i}=1}^{\text{n}}\text{H}\text{Q}\) i = 1…n (2)

Cancer Risk (CR) = EDI x CSF (3)

Where EDI is the estimated daily intake(mg/kg/day) of heavy metals (HMs), C is the amounts of HMs (mg/L) observed in groundwater samples, IR is the ingestion rate of groundwater samples (2, 1, 0.5 L/day for adults, children and infants, respectively), ED is the exposure duration (30, 6 and 1 years for adults, children, and infants, respectively), EF is the exposure frequency (350 days/year, Taiwo et al. 2020),AT is the averaging time/life expectancy = 54.5, 6 and 1 years for adults, children and infants, respectively (WHO 2015), AT = ED for non-carcinogenic effects, BW is the body weight (60, 15 and 10kg for adults and children, respectively, RfD is the reference dose of metals (mg/kg/day; USEPA 2001), and n is the number of elements observed, and CSF is the cancer slope factor (1/mg^/kg/day).

HQ > 1 indicates non-carcinogenic adverse health effects, HQ < 1 denotes no adverse effects, CR < 1.0 x 10^− 4 indicates no cancer risk, while the CR > 1.0 x 10^− 4establishes possible development of cancer.

2.5Determination of water quality index (WQI)

The water quality index (WQI) method was adopted to assess the suitability of the groundwater for drinking purposes. It reflects the combined influence of various water quality parameters (Şeneret al. 2017). This method uses parameters such asthe water quality rating scale, relative weight, and sub-index, WQI to calculate the overall WQI (Saana et al. 2016). In this present study, the WQI was estimated from 18groundwater parameters (Table S1 in the supplementary material).

Water quality rating scale, qi was estimated as:

Qi=\(\frac{\text{C}\text{n}}{\text{S}\text{n}}\)×100 (4)

Where C_n = Concentration of each (n^th) parameter, S_n = standard value of each (n^th) parameter.

Relative weight, W_n was calculated as:

W_n=\(\frac{\text{W}\text{n}}{\sum _{\text{n}=1}^{\text{y}}\text{W}\text{n}}\) (5)

Where w_n =weight of each (n^th)parameter and y = number of parameters.

The overall WQI was estimated:

SI_n= W_n x Q_n (6)

Where SI_n = sub-index of n^th parameter, Q_n = Water quality rating scale, and W_n = Relative weight.

WQI = ∑SI_n (7)

WQI less than 50 indicates an excellent water quality; WQI between 50.1 and 100 shows good water quality; WQI ranging from 100.1 to 200 suggests poor water quality; WQI between 200.1 and 300 reveals very poor water quality; while WQI > 300 establishes water unfit for drinking (Ramakrishnaiah et al. 2009).

3.1 Physical, chemical, and microbiological characteristics of groundwater samples

Table 1 presents the results of the physical parameters of the markets’ groundwater samples. The average distance of the waste sanitation facilities to the hand-dug wells varied from 29.6 ± 18.0 to 54.6 ± 14.6 m. No significant variations (p > 0.05) were observed among the various sampling distances to the wells. This indicates a common contamination source (to the hand-dug wells), which might probably be linked to the market waste sanitation facilities. The mean temperature of the groundwater samples ranged from 29.0 ± 0.82 to 30.5 ± 0.85°C. There were few temperature variations in groundwater samples, however, the wells in Lafenwa were statistically (p < 0.05) higher in temperature than in seven other sites. The average temperature observed in groundwater fell within the typical water temperature greater than 27°C in the tropical region (Benz et al. 2017). The temperature observed in the present study is lower than those reported in the study of Saana et al. (2016) in groundwater from the upper and northern regions of Ghana.

The lowest values of EC (255 ± 77 µS/cm) and TDS (128 ± 38 mg/L) were observed at the Olajide community, which serves as a control site, while the statistically (p < 0.05) highest EC (1056 ± 318

µS/cm) and TDS (536 ± 160mg/L) concentrations were observed at the Lafenwa site. The EC and TDS measured in the groundwater from the market sites clearly showed higher values than the control site, indicating the possible contributions of the market wastes. WHO (2017) demonstrated that TDS in drinking water may originate from natural and anthropogenic sources including urban runoff, sewage, and industrial wastewater. The EC levels at all the sampling sites, except the control, Asero, Bode and Saje were higher than the values of 400 µS/cm set by the European Union (Shah and Joshi 2017). TDS levels were generally lower than the permissible standard of 600 mg/L stipulated by the World Health Organization (WHO 2017). High EC in water may result in unpleasant tastes, objectionable odour, and change in pH (Leo and Dekkar 2000). A change in water pH may result in adverse health effects. For example, a pH less than 5.3 may hinder vitamin assimilation by the body, while a pH greater than 11 may aggravate skin disorders, and also cause eye irritation (Shah and Joshi 2017).

Table 1

The physical parameters of the markets’ groundwater samples
		Distance				Temperature (°C)				EC (µS/cm)				TDS (mg/L)
	N	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.
Kuto	10	35.8a	12.3	19.0	50.0	29.8bcde	0.63	29.0	31.0	700c	143	480	866	357c	65	256	700
Lafenwa	10	54.6a	14.6	36.0	75.0	30.5e	0.85	29.0	32.0	1056d	318	299	1441	536d	160	153	1056
Oke-Ibukun	10	36.2a	23.5	15.0	74.0	30.2de	0.92	29.0	32.0	496b	111	246	621	248b	52	124	496
Iberekodo	10	47.4a	10.3	34.0	60.0	29.6abcd	0.97	28.0	31.0	981d	98	777	1094	499d	40	421	981
Gbangba	10	29.6a	18.0	10.0	55.0	30.0cde	0.47	29.0	31.0	989d	307	441	1537	521d	121	380	989
Ijeun	10	41.6a	24.3	12.0	76.0	29.5abcd	0.97	28.0	31.0	425ab	133	217	535	213ab	68	107	425
Bode	10	41.2a	17.5	18.0	62.0	29.6abcd	0.52	29.0	30.0	331ab	159	266	752	166ab	75	134	331
Asero	10	36.0a	20.4	10.0	64.0	29.2ab	0.63	28.0	30.0	376ab	63	239	455	189ab	31	121	376
Osiele	10	46.2a	26.0	15.0	81.0	29.0a	0.82	28.0	30.0	926d	365	310	1351	466d	186	150	926
Saje	10	51.8a	15.6	31.0	71.0	29.7abcd	0.82	28.0	31.0	388ab	26	335	413	195ab	13	170	388
Olajide (control)	10					29.4abc	0.70	28.0	30.0	255a	77	150	354	128a	38	75	255
										400				600

Temp-Temperature, TDS-Total dissolved, EC- Electrical conductivity, Means with similar alphabets along the column are not significantly different at p > 0.05 according to Duncan Multiple Range Test, SD-standard deviation, Min-minimum, Max. maximum

Table 2 shows the data of the chemical composition of the markets’ groundwater samples. Despite recording low pH values less than the permissible level of 6.5 in some groundwater samples from Iberekodo and Saje market sites; the average pH levels observed at all the monitoring sites indicated a neutral to slightly alkaline medium (7.00 ± 0.50–7.53 ± 0.69). The mean pH values of groundwater showed no statistical variance, and thus fell within the acceptable range of 6.5–8.5 in drinking water (WHO 2017).The pH of groundwater in the vicinity of the markets and the control sampling location (Olajide) indicated fitness for possible consumption purposes. Since human fluid had apH level in the range of 7.0 to 7.2, drinking water should fall within this pH to protect human health (Shah and Joshi 2017).

The hand-dug wells had the highest alkalinity concentration (6.64 ± 4.34 mg/L) at the Lafenwa market. The alkalinity values of groundwater samples were generally lower than the WHO standard of 120 mg/L. Ninety percent of the groundwater samples had a calcium value higher than the permissible limit of 75 mg/L (WHO 2017). The control site, however, showed a level of calcium lower than the permissible limit. Similar trends were observed for total hardness, except that only 50% of the markets’ well water samples fell within the acceptable limit of 200 mg/L (WHO 2017).

The groundwater samples having a total hardness value greater than the permissible standard indicated significantly higher levels in the market area than in the control site, establishing the possible introduction of dissolved substances from the market wastes (Taiwo et al. 2011). Water hardness has a significant effect on the consumer’s acceptability of drinking water as well as economic and operational consideration of water treatment (WHO 2017). Limited scientific evidence has established a relationship between hardness and cardiovascular mortality (WHO 2017).

Sulphate and nitrate levels in groundwater at both market and control sites were generally lower than the permissible threshold of 250 and 50 mg/L, respectively (WHO 2017). The control site indicated a lower significance of these important water parameters. The levels of nitrate and sulphate in the market area groundwater samples revealed the contributory effects of the market wastes disposal activities showing higher values than the control site.

Dissolved oxygen (DO) in groundwater samples showed the lowest value (3.36 ± 1.23 mg/L) at one of the market sites, while the highest level (7.05 ± 4.55 mg/L) was observed at the control location. DO is a measure of biological change by aerobic and anaerobic microbes in water, an indication of organic load and water pollution (Shah and Joshi, 2017).

Table 2

Chemical parameters of markets’ groundwater samples
					pH						Alkalinity								Calcium								Total Hardness
											mg/L
	N		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.
Kuto	10		7.53a		0.69		6.82		8.45		6.22d		4.69		2.60		16.20		314b		182		146		806		327b		92.1		216		492
Lafenwa	10		7.29a		0.37		6.69		7.84		6.64d		4.34		2.20		12.80		317b		97.4		112		434		407b		119		152		542
Oke-Ibukun	10		7.27a		0.76		6.45		8.16		1.60a		0.43		1.00		2.20		125ab		31.5		52.0		166		190a		47.0		86.0		250
Iberekodo	10		7.30a		0.77		6.35		8.21		4.08bc		1.42		2.60		6.40		305b		36.0		238		346		427b		32.8		364		470
Gbangba	10		7.48a		0.67		6.52		8.39		5.48cd		1.78		3.60		8.60		267b		62.3		184		364		320b		186.4		92.0		730
Ijeun	10		7.45a		0.67		6.64		8.19		2.00a		0.83		1.40		4.20		131ab		56.6		58.0		216		199a		64.3		102		292
Bode	10		7.10a		0.19		6.71		7.30		1.48a		0.70		0.80		3.20		90a		30.6		62.0		172		151a		80.6		110		376
Asero	10		7.05a		0.15		6.81		7.25		1.26a		0.53		0.80		2.20		71a		17.4		48.0		100		123a		26.0		66.0		156
Osiele	10		7.51a		0.32		6.99		8.07		2.98ab		1.13		1.20		4.60		190b		61.9		106		294		377b		246		0		796
Saje	10		7.00a		0.50		6.38		8.09		1.14a		0.48		0.60		2.00		104a		13.9		84.0		132		153a		15.6		120		172
Olajide(control)	10		7.11a		0.20		6.93		7.39		1.45a		0.59		0.80		2.40		59.3a		23.0		20.0		90.0		117a		38.3		56		168
WHO (2017)			6.5–8.5								120								75								200
				Sulphate								Nitrate								DO								BOD
		N		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.
Kuto		10		38.7bcd		20.8		1.45		87.1		7.09bc		6.74		1.88		16.2		5.42abc		3.81		0.75		10.4		4.46a		3.86		0.22		10.4
Lafenwa		10		46.1cde		38.0		0.32		140		2.78ab		1.46		1.18		4.58		6.31abc		3.33		2.60		12.2		3.62a		3.30		0.48		10.2
Oke-Ibukun		10		63.8e		35.6		12.4		124		9.09c		4.50		3.86		14.8		5.76abc		3.11		1.60		10.5		3.95a		2.60		0.50		8.30
Iberekodo		10		61.0de		42.6		8.55		156		5.36abc		3.81		1.02		10.1		6.15abc		3.42		2.60		14.2		2.82a		2.18		0.10		8.15
Gbangba		10		29.7abc		6.4		20.3		36.8		6.09abc		2.12		4.31		8.81		6.75bc		3.80		2.60		12.7		4.28a		4.10		0.25		9.65
Ijeun		10		11.2ab		12.5		0.16		40.5		2.68ab		1.13		1.48		4.11		4.37abc		1.84		1.50		7.50		2.79a		1.77		0.05		5.80
Bode		10		14.0abc		13.6		0.97		36.8		4.14abc		4.47		0.92		10.4		4.28abc		2.01		1.35		6.75		2.42a		2.59		0.05		6.75
Asero		10		20.8ab		19.8		1.77		52.9		3.61ab		2.78		1.04		6.98		3.36a		1.23		1.75		5.50		2.08a		1.39		0.40		5.50
Osiele		10		52.9ab		28.7		7.58		103		2.29ab		1.34		1.06		3.91		3.81ab		0.92		2.50		5.20		2.51a		1.39		0.05		4.80
Saje		10		12.2a		10.2		2.26		29.8		1.38a		0.74		0.86		2.46		4.53abc		1.71		1.90		6.55		1.88a		1.54		0.00		4.55
Olajide (control)		10		6.88a		3.83		1.77		12.6		0.87a		0.15		0.72		1.00		7.05a		4.55		1.70		12.3		1.81a		1.31		0.45		4.10
WHO (2017)				250								50								*5.0								*3.0
DO-Dissolved oxygen, BOD-Biological oxygen demand, Means with similar alphabets along the column are not significantly different at p > 0.05 according to Duncan Multiple Range Test, SD-standard deviation, Min-minimum, Max. maximum, * BIS (1991)

DO level less than 5.0 mg/L indicates water pollution, therefore 50% of the groundwater samples influenced by the market wastes were higher than the acceptable limit, while the control groundwater samples revealed an unpolluted level.

BOD is defined as the oxygen consumed by microorganisms in water during the breakdown of organic materials. When BOD is greater than 3.0 mg/L, it is also an indication of a high organic load inthe water. The control site had the lowest BOD level (1.81 ± 1.31 mg/L) lower than the permissible standard of 3.0 mg/L, thereby signifying low organic load, and hence clean water. A high BOD value of 10.01 mg/L had been established in a well near the dumpsite in Indonesia (Sholichin 2012) due to contamination from organic wastes.

Table 3 shows the heavy metal concentrations of groundwater samples in the study area. No significant (p > 0.05) variations were observed for Co and Pb at both the market and control sites groundwater samples. However, higher levels of these HMs were observed in the market groundwater samples establishing the probableinfluence of the market wastes. Although there was no permissible standard for Co in drinking water, the ATSDR (2004) showed that Co is normally determined at the concentration of 0.002 mg/L in drinking water. The Co levels in all the groundwater exceeded this normal concentration of 0.002 mg/L indicating probable contamination. The Pb concentrations in the market-waste-influenced groundwater samples were generally 4–6 times higher than the permissible standard of 0.01 mg/L (WHO 2017). This further established possible contamination of the groundwater by the pollutants escaping from the waste sanitation facilities. Pb is a pediatric poison that affects children’s intelligence quotient (IQ) and even results in death at high concentrations (Rees and Fuller 2020).

Cu and Zn levels in all the groundwater samples were generally less than the permissible limits of 2 and 3 mg/L, respectively. However, Cu showed a significantly higher level in 70% of the market groundwater than in the control (Olajide) site, while Zn only exhibited a higher significant level in 10% of the market sampling sites than in the control sampling location. Fe had the highest mean concentration (3.58 ± 3.36 mg/L) in the Ijeun market groundwater samples, a level that was over 10 times higher than the permissible level of 0.3 mg/L.All the groundwater samples including the control site at Olajide community exhibited Fe concentrations generally higher than the permissible limit.

High content of Fe in drinking water is not a major issue of health concern, however, it affects the acceptability of drinking water in terms of taste, odour, and colour (WHO 2017). Like Fe, Mn is not a health-based heavy metal when concentration is less than 0.1 mg/L but incline towards aesthetic acceptability (WHO 2017).

Table 3

Concentrations of heavy metals in groundwater samples
				Co								Cu								Fe
	N	Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.
Kuto	8	0.03a		0.03		0.01		0.09		0.11cd		0.03		0.08		0.16		3.30c		1.58		1.58		5.27
Lafenwa	8	0.02a		0.03		0.01		0.08		0.13d		0.02		0.10		0.16		2.38abc		1.22		1.17		4.48
Oke-Ibukun	8	0.02a		0.02		0.01		0.05		0.13d		0.12		0.05		0.43		1.57ab		0.50		0.84		2.33
Iberekodo	8	0.03a		0.02		0.01		0.06		0.12cd		0.03		0.08		0.15		2.51bc		1.28		0.83		4.04
Gbangba	8	0.02a		0.02		0.01		0.06		0.09bcd		0.03		0.04		0.14		2.36abc		0.86		1.00		3.33
Ijeun	8	0.02a		0.02		0.01		0.06		0.09bcd		0.03		0.05		0.16		3.58c		3.36		0.47		7.76
Bode	8	0.02a		0.02		0.01		0.04		0.07abc		0.02		0.04		0.11		0.73a		0.27		0.32		1.08
Asero	8	0.02a		0.03		0.01		0.07		0.07bc		0.02		0.04		0.11		1.59ab		0.74		0.88		2.73
Osiele	8	0.02a		0.02		0.01		0.05		0.07abc		0.02		0.05		0.09		0.87ab		0.54		0.35		1.66
Saje	8	0.02a		0.02		0.01		0.07		0.06ab		0.02		0.03		0.10		0.98ab		0.15		0.78		1.18
Olajide (control)	8	0.01a		0.00		0.01		0.01		0.02a		0.01		0.02		0.03		1.07ab		0.57		0.71		2.44
WHO (2017)										2.0										0.3
					Mn								Pb								Zn
			Mean		SD		Min.		Max.		Mean		SD		Min.		Max.		Mean		SD		Min.		Max.
Kuto	8		0.80c		0.32		0.40		1.28		0.06a		0.06		0.01		0.14		0.08a		0.01		0.07		0.09
Lafenwa	8		0.18ab		0.09		0.05		0.29		0.05a		0.05		0.01		0.12		0.07a		0.01		0.05		0.09
Oke-Ibukun	8		1.33d		0.83		0.38		2.47		0.05a		0.05		0.01		0.13		0.08a		0.01		0.07		0.10
Iberekodo	8		0.15ab		0.11		0.01		0.30		0.05a		0.05		0.01		0.11		0.06a		0.02		0.03		0.08
Gbangba	8		0.61bc		0.71		0.07		1.48		0.06a		0.07		0.01		0.16		0.07a		0.02		0.05		0.10
Ijeun	8		0.40abc		0.47		0.01		1.08		0.06a		0.06		0.01		0.14		0.07a		0.01		0.05		0.09
Bode	8		0.05a		0.03		0.01		0.12		0.04a		0.06		0.00		0.16		0.06a		0.01		0.04		0.07
Asero	8		1.45d		0.90		0.58		2.93		0.04a		0.04		0.01		0.10		0.15b		0.13		0.05		0.38
Osiele	8		0.24ab		0.19		0.01		0.55		0.06a		0.07		0.01		0.20		0.07a		0.02		0.05		0.11
Saje	8		0.08ab		0.04		0.02		0.16		0.05a		0.05		0.01		0.12		0.09a		0.06		0.05		0.22
Olajide (control)	8		0.17ab		0.05		0.10		0.23		0.01a		0.00		0.01		0.01		0.10ab		0.07		0.05		0.27
WHO (2017)			0.4								0.01								3.0
Means with similar alphabets along the column are not significantly different at p > 0.05 according to Duncan Multiple Range Test, SD-standard deviation, Min-minimum, Max. maximum

However, a concentration greater than 0.4 mg/L Mn has health implications for the consumers (WHO 2017). Mn in 40% of the market area hand-dug wells had Mn concentrations greater than the permissible limit. These sites indicated concentrations significantly higher than the control site. The major health issue associated with the consumption of high concentrations of Mn is brain damage in children and young adults (HealthLink BC 2019).

The microbiological characteristics of the groundwater samples are presented in Table 4. There was a clear indication that the hand-dug wells around the market areas had higher loads of total coliform (TC) and E. coli, compared to the control (Olajide) site. The highest significant level of TC (2260±2058 MPN/100 mL) was observed in groundwater samples from the Osiele market, while the lowest value (30±48MPN/100 mL) was observed in Olajide (control) wells. The highest significant level of E. coli was observed in the Oke-Ibukun market groundwater sample, while the lowest E. coli value was established at the control site. The permissible limit of TC in drinking water according to the Bureau of Indian Standard is <10MPN/100 mL (BIS 2004). This acceptable limit for this microbial pollutant was exceeded by more than 100 and 3 times in market and control sites groundwater samples, respectively. However, for bathing purposes, the TC standard is stipulated as 30 MPN/100 mL (BIS 2004), indicating that only the well water from the control site could be used for bathing without any suspected health effects.

The E. coli in all the groundwater showed values higher than the permissible limit of 0 MPN/100 mL. The market groundwater samples were, however, extremely contaminated with E.coli, thereby establishing probable contribution by market wastes. A similar study by Kassenga and Mbuligwe, (2009) reported high values of faecal coliform that varied from 7000 to 37,000 MPN/100 mL in groundwater close to a dumpsite in Dar es Salaam, Tanzania.

3.2 Seasonal variation of groundwater parameters

The seasonal variations of groundwater parameters are shown in Table S2 (in the supplementary material). Generally, the average concentrations of the physical, chemical and microbial groundwater parameters were higher in the market area than in the control site. Most of the water parameters were statistically (p < 0.05) observed at higher levels in the wet than the dry season. At the market area, the groundwater samples showed greater statistically significant (p < 0.05) values for temperature, pH, EC, BOD, TC, and Pb during the wet season than in the dry season.

Table 4

Microbiological characteristics of groundwater
		TC (MPN/100 mL)				E. coli(MPN/100 mL)
	N	Mean	Std. Deviation	Minimum	Maximum	Mean	Std. Deviation	Minimum	Maximum
Kuto	10	2110b	694	1300	3300	440ab	693	0	2000
Lafenwa	10	880ab	614	0	2000	100a	316	0	1000
Oke-Ibukun	10	1510ab	983	300	2900	1090b	1314	0	3800
Iberekodo	10	1540ab	2450	0	8000	510ab	1274	0	4000
Gbangba	10	1860b	1655	200	5000	430ab	978	0	3000
Ijeun	10	1254ab	1775	40	6000	430ab	650	0	1400
Bode	10	1430ab	1701	300	6000	0a	0	0	0
Asero	10	1660ab	2161	0	7000	290ab	615	0	1600
Osiele	10	2260b	2058	200	5800	750ab	1583	0	5000
Saje	10	1700ab	2220	0	6000	400ab	845	0	2100
Olajide (control)	10	30a	48	0	100	10a	32	0	100
BIS (2004)		< 10				0

Means with similar alphabets along the column are not significantly different at p > 0.05 according to Duncan Multiple Range Test, SD-standard deviation, Min-minimum, Max. maximum

The high levels of these parameters observed during the wet season suggest contributory effects of runoff materials from urban surfaces including the markets’ sanitation facilities. Similar to the present study, BOD, TC, and Pb had been linked closely to the infiltration of dumpsite leachates into the groundwater (Karim et al. 2010). During the dry season, only alkalinity and Co had significantly (p < 0.05) higher concentrations than in the wet season. At the control site in the Olajide community, nitrate was the only parameter that was measured at a significantly (p < 0.05) higher level in the wet season than in the dry season, while the levels of DO and Cu were statistically (p < 0.05) higher during the dry season than in the wet season. This showed that there was no clear factor dictating the quality of groundwater in the control site.

3.3 Water quality index (WQI) of groundwater

The WQI values of groundwater samples collected from the market areas and the control (Olajide) site are presented in Fig. 2. The mean WQIs of the market groundwater were generally higher than the threshold level of 300, indicating unfit water for consumption (Ramakrishnaiah et al. 2009). However, the average WQI of the control site groundwater sample was less than 100, thereby suggesting good water quality. Similarly, a WQI value of 880 was documented by Amano et al. (2021) in groundwater (borehole) close to a landfill site in Asokwa sub-metro, Ghana. The WQI data of market groundwater was dominated by TC, which contributed 50–76% to the overall water quality index. Pb was the second largest contributor to the WQI, representing 9–15%. TC and Pb are suitable fingerprints for anthropogenic activities, probably from the disposal of organic and inorganic wastes (Masoner et al. 2019). In a developing country like Nigeria, where solid wastes are unsorted before disposal, different types of wastes ended up in waste facilities (Taiwo 2011).This may result in severe environmental and health problems.

4.4 Principal component analysis

The rotated varimax principal component analysis (PCA) data of the market area groundwater samples are presented in Table S3 (in the supplementary material). Six factors were identified by the rotated PCA with 68% of the total variance explained. Factor 1 (explaining 22% of the total dataset) has significant loadings for pH, DO, BOD, E. coli, and moderate loading for TC. The factor was anti-correlated with Pb and Co. This factor is suspected to arise from organic/microbial pollution that could be linked to the market waste sanitation activities (Karim et al. 2010). Factor 2(20% variance) has high loadings for electrical conductivity, TDS, alkalinity, total hardness, and Ca. The source of these parameters may be attributed to the weathering of bedrock. Factor 3 (10% variance) is characterized by the abundance of sulphate, nitrate, Cu, and moderate occurrence of E. coli and Mn. This factor is similar to Factor 1, and may likely emanate from municipal solid wastes.

The high significance of alkalinity, BOD, and Fe, with the moderate occurrence of Ca, characterized Factor 4 (7% variance). The source indicates possible emissions from other anthropogenic activities apart from the waste sanitation facilities. The urban runoff could carry different materials into the hand-dug wells from diverse anthropogenic pathways such as agriculture, dumpsites, textile industries, and mechanic workshops (Yaseen and Scholz 2019). Factor 5 (7%) has positive significant loadings for distance, temperature, and negative correlation with Mn. The occurrence of Mn in Factor 3 (positive) and Factor 5 (negative) shows likely dissimilar sources from the market waste sanitation facilities and textile industry. Adire textile production in the city of Abeokuta (Soaga and Opeolu 2009) may also be responsible for high levels of Mn in the groundwater (Yaseen and Scholz 2019).

4.5 Regression model of distance, sites, and season versus groundwater parameters

Table S4 (in the supplementary material) shows the regression analysis of the distance of the hand-dug wells to the waste sanitation facilities, the monitoring sites, and the season of sampling against the groundwater parameters. The model performed well (good fit)with R² ranging from 0.554 to 0.956.A negative association was observed between the sampling distance and pH, TDS, total hardness, nitrate, BOD, total coliform, Cu, Fe, Mn, and Pb, indicating an adverse influence of market waste sanitation activities on the groundwater. The negative relationship implies that the closer the hand-dug wells were to the sanitation facilities, the higher the levels of the water quality parameters, and vice versa.

The regression model established a negative relationship between the sites, and temperature, pH, EC, alkalinity, total hardness, Ca, sulphate, nitrate, BOD, Cu, Fe, Mn, and Zn. This shows that the hand-dug wells were equally affected by a common factor (probably from the market waste sanitation facility). The highlighted groundwater parameters are notable fingerprints for dumpsite leachates (Kassenga and Mbuligwe 2009; Karim et al. 2010; Taiwo et al. 2019). As the season is concerned, the model revealed a positive relationship with temperature, pH, EC, total hardness, BOD, total coliform, E.coli, Fe, and Zn. This reveals that as these water quality parameters increase in any of the seasons (wet or dry), they decrease in the other season (dry or wet).

4.6 Evaluation of health risk of metals in groundwater samples

The estimated daily intake (EDI) data of metals determined in groundwater samples are highlighted in Table S5 (in the supplementary material). Fe was the most dosed metal in groundwater showing the mean EDI values that ranged from 0.021 ± 0.012 mg/kg/day for adults to 0.17 ± 0.21 mg/kg/day for children. However, the EDI level was less than the acceptable daily intake of 0.9 and 3.6 mg/kg/day for adults and children, respectively (WHO 2017).

Table 5 presents the non-carcinogenic hazard quotient (HQ) and hazard index (HI) of metals in groundwater samples consumed by adults, children, and infants. The HQs of Co, Cu, Fe, Zn, and Pb (adults only) were lower than the acceptable limit of 1.0, indicating no non-carcinogenic adverse health effects through oral exposure to these metals in drinking water. However, the HQs of Mn (adults, children and infants) and Pb (children and infants) were greater than the permissible limit of 1.0, suggesting non-carcinogenic deleterious health effects. Mn is an essential trace metal required by the body for regulations of blood sugar, reduction of inflammation and risk of diseases, bone development and maintenance, metabolism of nutrients, collagen production for wound healing, and lower incidences of epileptic seizures (Healthline 2022). Nevertheless, the associated non-carcinogenic health effects of exposure to high level of Mn by adults include behavioural changes, severe nervous system, loss of sex drive, and sperm damage (ATSDR 2012).

In children, Mn could initiate arrays of ill-health effects including difficulty in speech and walking, behavioural changes, decrease learning ability, and undesirable effects on brain development (ATSDR 2012). The high HQ values of Pb in drinking water consumed by children and infants are an issue of public health concern. Pb has no known metabolic function in the human system. It is a teratogenic metal that possesses the ability to cross the placenta barriers and causes structural and functional deformations in the unborn baby (CDC/NIOSH 2021). Further teratogenic effects of Pb include infertility in males and females, stillbirth, and miscarriage (Taiwo et al. 2010). In children, Pb initiates gross adverse effects such as hypertension, low IQ and poor academic performance, development problem, decreased cognitive performance, and post-natal growth (ATSDR 2021). In adults, health problems associated with exposure to a high level of Pb are endocrine, gastrointestinal, cardiovascular, neurological, reproductive, hematological and development changes (ATSDR 2021). In 2010, more than 400 children’sdeaths were reported in Zamfara, Nigeria due to exposure to high Pb concentrations in mine-contaminated water (Getso et al. 2014).

The HI values of metals estimated for adults, children, and infants were greater than 1.0, thus further establishing the cumulative effects of the metals in groundwater from both the market and control sites. Mn is the largest contributor to the HI in the range of 84–98%, while Pb was the second contributing metal in the variation of 1–11%.

Table 5

Hazard quotient and hazard index values of metals in groundwater
		Adults				Children				Infants
		Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.
Co	Kuto	0.02	0.02	0.01	0.05	0.03	0.03	0.01	0.10	0.03	0.02	0.01	0.07
	Lafenwa	0.01	0.01	0.01	0.04	0.03	0.03	0.01	0.09	0.02	0.02	0.01	0.06
	Oke-Ibukun	0.01	0.01	0.01	0.03	0.02	0.02	0.01	0.05	0.02	0.01	0.01	0.04
	Iberekodo	0.02	0.01	0.01	0.03	0.03	0.02	0.01	0.06	0.02	0.02	0.01	0.05
	Gbanga	0.01	0.01	0.01	0.03	0.03	0.02	0.01	0.06	0.02	0.02	0.01	0.05
	Ijeun	0.01	0.01	0.01	0.03	0.03	0.02	0.01	0.06	0.02	0.01	0.01	0.05
	Bode	0.01	0.01	0.01	0.02	0.03	0.02	0.01	0.04	0.02	0.01	0.01	0.03
	Asero	0.01	0.01	0.01	0.04	0.03	0.03	0.01	0.08	0.02	0.02	0.01	0.06
	Osiele	0.01	0.01	0.01	0.03	0.03	0.02	0.01	0.05	0.02	0.01	0.01	0.04
	Saje	0.01	0.01	0.01	0.04	0.03	0.02	0.01	0.08	0.02	0.02	0.01	0.06
	Olajide (control)	0.01	0.00	0.01	0.01	0.01	0.00	0.01	0.01	0.01	0.00	0.01	0.01
Cu	Kuto	0.18	0.04	0.13	0.26	0.37	0.09	0.26	0.51	0.27	0.06	0.19	0.38
	Lafenwa	0.20	0.03	0.16	0.26	0.40	0.07	0.32	0.51	0.30	0.05	0.24	0.38
	Oke-Ibukun	0.20	0.20	0.08	0.69	0.41	0.41	0.16	1.40	0.30	0.29	0.12	1.00
	Iberekodo	0.19	0.04	0.13	0.24	0.39	0.08	0.26	0.48	0.29	0.06	0.19	0.36
	Gbanga	0.15	0.05	0.06	0.22	0.30	0.10	0.13	0.45	0.22	0.08	0.10	0.34
	Ijeun	0.14	0.06	0.08	0.26	0.28	0.11	0.16	0.51	0.21	0.08	0.12	0.38
	Bode	0.11	0.03	0.06	0.18	0.22	0.07	0.13	0.35	0.17	0.05	0.10	0.26
	Asero	0.12	0.04	0.06	0.18	0.24	0.07	0.13	0.35	0.18	0.06	0.10	0.26
	Osiele	0.11	0.02	0.08	0.14	0.22	0.05	0.16	0.29	0.17	0.04	0.12	0.22
	Saje	0.09	0.04	0.05	0.16	0.18	0.08	0.10	0.32	0.14	0.06	0.07	0.24
	Olajide (control)	0.04	0.01	0.03	0.05	0.08	0.02	0.06	0.10	0.06	0.01	0.05	0.07
Fe	Kuto	0.11	0.09	0.00	0.24	0.23	0.18	0.00	0.48	0.17	0.14	0.00	0.36
	Lafenwa	0.08	0.07	0.00	0.20	0.16	0.14	0.00	0.41	0.12	0.10	0.00	0.31
	Oke-Ibukun	0.05	0.04	0.00	0.11	0.11	0.08	0.00	0.21	0.08	0.06	0.00	0.16
	Iberekodo	0.07	0.07	0.00	0.18	0.14	0.15	0.00	0.37	0.11	0.11	0.00	0.28
	Gbanga	0.08	0.06	0.00	0.15	0.16	0.12	0.00	0.30	0.12	0.09	0.00	0.23
	Ijeun	0.12	0.15	0.00	0.35	0.25	0.30	0.00	0.71	0.18	0.23	0.00	0.53
	Bode	0.03	0.02	0.00	0.05	0.06	0.03	0.00	0.10	0.04	0.02	0.00	0.07
	Asero	0.04	0.04	0.00	0.12	0.09	0.09	0.00	0.25	0.07	0.07	0.00	0.19
	Osiele	0.04	0.02	0.02	0.08	0.08	0.05	0.03	0.15	0.06	0.04	0.02	0.11
	Saje	0.03	0.02	0.00	0.05	0.07	0.04	0.00	0.11	0.05	0.03	0.00	0.08
	Olajide (control)	0.05	0.03	0.03	0.11	0.10	0.05	0.07	0.22	0.07	0.04	0.05	0.17
Mn	Kuto	38.38	29.37	0.00	82.00	76.50	58.51	0.00	160.00	56.75	43.15	0.00	120.00
	Lafenwa	10.26	6.59	0.00	19.00	20.55	13.04	0.00	37.00	15.23	9.84	0.00	28.00
	Oke-Ibukun	64.13	60.36	0.00	160.00	128.63	120.86	0.00	320.00	96.38	91.27	0.00	240.00
	Iberekodo	9.52	7.05	0.64	19.00	19.36	14.32	1.30	38.00	14.38	10.81	0.96	29.00
	Gbanga	24.45	39.80	0.00	95.00	48.50	78.74	0.00	190.00	35.91	58.49	0.00	140.00
	Ijeun	19.09	27.98	0.00	69.00	38.31	56.11	0.00	140.00	28.21	40.90	0.00	100.00
	Bode	3.13	2.38	0.00	7.70	6.20	4.67	0.00	15.00	4.73	3.71	0.00	12.00
	Asero	69.50	65.21	0.00	190.00	137.50	127.74	0.00	370.00	103.75	97.12	0.00	280.00
	Osiele	15.07	12.12	0.64	35.00	30.26	24.26	1.30	70.00	22.73	18.26	0.96	53.00
	Saje	5.24	2.88	1.30	10.00	10.51	5.76	2.60	20.00	7.99	4.40	1.90	15.00
	Olajide (control)	9.55	5.00	0.00	15.00	18.75	9.62	0.00	29.00	14.08	7.23	0.00	22.00
Pb	Kuto	0.56	0.54	0.09	1.30	1.13	1.08	0.18	2.60	0.84	0.80	0.14	1.90
	Lafenwa	0.51	0.46	0.09	1.10	1.02	0.91	0.18	2.20	0.77	0.68	0.14	1.60
	Oke-Ibukun	0.46	0.43	0.09	1.20	0.93	0.86	0.18	2.40	0.69	0.64	0.14	1.80
	Iberekodo	0.46	0.40	0.09	1.00	0.92	0.80	0.18	2.00	0.68	0.59	0.14	1.50
	Gbanga	0.59	0.61	0.09	1.50	1.17	1.20	0.18	2.90	0.87	0.89	0.14	2.20
	Ijeun	0.55	0.54	0.09	1.30	1.12	1.08	0.18	2.60	0.82	0.79	0.14	1.90
	Bode	0.42	0.53	0.00	1.50	0.83	1.04	0.00	2.90	0.62	0.78	0.00	2.20
	Asero	0.40	0.35	0.09	0.91	0.80	0.71	0.18	1.80	0.61	0.53	0.14	1.40
	Osiele	0.59	0.65	0.09	1.80	1.19	1.33	0.18	3.70	0.89	0.97	0.14	2.70
	Saje	0.49	0.47	0.09	1.10	0.98	0.94	0.18	2.20	0.73	0.69	0.14	1.60
	Olajide (control)	0.09	0.00	0.09	0.09	0.18	0.00	0.18	0.18	0.14	0.00	0.14	0.14
Zn	Kuto	0.01	0.00	0.01	0.01	0.02	0.00	0.02	0.02	0.01	0.00	0.01	0.01
	Lafenwa	0.01	0.00	0.01	0.01	0.02	0.00	0.01	0.02	0.01	0.00	0.01	0.01
	Oke-Ibukun	0.01	0.00	0.01	0.01	0.02	0.00	0.02	0.02	0.01	0.00	0.01	0.02
	Iberekodo	0.01	0.00	0.00	0.01	0.01	0.00	0.01	0.02	0.01	0.00	0.00	0.01
	Gbanga	0.01	0.00	0.01	0.01	0.02	0.00	0.01	0.02	0.01	0.00	0.01	0.02
	Ijeun	0.01	0.00	0.01	0.01	0.02	0.00	0.01	0.02	0.01	0.00	0.01	0.01
	Bode	0.01	0.00	0.00	0.01	0.01	0.00	0.01	0.02	0.01	0.00	0.01	0.01
	Asero	0.02	0.01	0.01	0.04	0.03	0.03	0.01	0.08	0.02	0.02	0.01	0.06
	Osiele	0.01	0.00	0.01	0.01	0.02	0.00	0.01	0.02	0.01	0.00	0.01	0.02
	Saje	0.01	0.01	0.01	0.02	0.02	0.01	0.01	0.05	0.01	0.01	0.01	0.04
	Olajide (control)	0.01	0.01	0.01	0.03	0.02	0.01	0.01	0.06	0.02	0.01	0.01	0.04
	Kuto	39.26				78.27				58.07
HI	Lafenwa	11.08				22.17				16.45
	Oke-Ibukun	64.86				130.11				97.47
	Iberekodo	10.26				20.85				15.49
	Gbanga	25.30				50.17				37.16
	Ijeun	19.93				39.99				29.45
	Bode	3.71				7.35				5.59
	Asero	70.09				138.69				104.65
	Osiele	15.83				31.79				23.88
	Saje	5.88				11.79				8.94
	Olajide (control)	9.74				19.14				14.37
SD-standard deviation, Min-minimum, Max. maximum

The past study by Ayantobo et al. (2014) in groundwater from the gold mining area of Igun Ijesha reported Pb as the major contributor to the total HQ. The groundwater samples analysed by Laniyan and Adewunmi (2019) at the vicinity of a dumpsite in Ibadan also demonstrated similar high HQ > 1.0 for Pb.

The cancer risk (CR) values of Co and Pb are presented in Table 6. Co had CR values greater than the permissible threshold of 1.0 x 10^− 4(USEPA, 2007) in the market groundwater samples, indicating probable development of cancer. However, the CR of groundwater samples at the control (Olajide community) site was less than the acceptable limit. The wide discrepancy between the CRs of groundwater from the market area and control site suggests the contributions of the disposal activities of market wastes. The CRs of Pb were generally less than the acceptable limit of 1.0 x 10^− 4in groundwater samples from all the sites. The total sum of CR in the market area groundwater indicated values higher than the acceptable limit of 1.0 x 10^− 4, also suggesting possible lifetime development of cancer by all the categories of consumers. In terms of carcinogenic effects, the groundwater from the control (Olajide community) site is safe for adults’ consumption due to the total CR level less than the carcinogenic threshold limit of 1.0 x 10^− 4.However, the total cancer risk of groundwater at the control site was greater than the cancer baseline limit, therefore establishing the possible growth of cancer in children and infants through consumption of the water. Figure S2 shows the contribution of Co and Pb to the total cancer risk. Co formed between 99.7–99.9% of the cancer burden at all the sampling sites, including the control.

Table 6

Cancer risk data of metals in groundwater
		Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.
Co	Kuto	4.6E-03	5.6E-03	8.6E-05	1.6E-02	1.7E-02	2.0E-02	3.1E-04	5.6E-02	1.2E-02	1.5E-02	2.3E-04	4.2E-02
	Lafenwa	3.1E-03	5.0E-03	8.6E-05	1.4E-02	1.1E-02	1.8E-02	3.1E-04	5.0E-02	8.4E-03	1.4E-02	2.3E-04	3.8E-02
	Oke-Ibukun	3.1E-03	3.5E-03	8.6E-05	8.6E-03	1.1E-02	1.3E-02	3.1E-04	3.1E-02	8.3E-03	9.5E-03	2.3E-04	2.3E-02
	Iberekodo	4.4E-03	4.7E-03	8.6E-05	1.0E-02	1.6E-02	1.7E-02	3.1E-04	3.8E-02	1.2E-02	1.3E-02	2.3E-04	2.8E-02
	Gbanga	3.9E-03	4.4E-03	8.6E-05	1.0E-02	1.4E-02	1.6E-02	3.1E-04	3.8E-02	1.1E-02	1.2E-02	2.3E-04	2.8E-02
	Ijeun	3.3E-03	3.8E-03	8.6E-05	1.0E-02	1.2E-02	1.4E-02	3.1E-04	3.8E-02	8.9E-03	1.0E-02	2.3E-04	2.8E-02
	Bode	3.3E-03	3.5E-03	8.6E-05	6.9E-03	1.2E-02	1.3E-02	3.1E-04	2.5E-02	8.9E-03	9.4E-03	2.3E-04	1.9E-02
	Asero	3.5E-03	5.1E-03	8.6E-05	1.2E-02	1.3E-02	1.8E-02	3.1E-04	4.4E-02	9.5E-03	1.4E-02	2.3E-04	3.3E-02
	Osiele	3.3E-03	3.6E-03	8.6E-05	8.6E-03	1.2E-02	1.3E-02	3.1E-04	3.1E-02	8.9E-03	9.7E-03	2.3E-04	2.3E-02
	Saje	3.3E-03	4.7E-03	8.6E-05	1.2E-02	1.2E-02	1.7E-02	3.1E-04	4.4E-02	9.0E-03	1.3E-02	2.3E-04	3.3E-02
	Olajide	8.6E-05	2.7E-21	8.6E-05	8.6E-05	3.1E-04	1.1E-20	3.1E-04	3.1E-04	2.3E-04	7.2E-21	2.3E-04	2.3E-04
Pb	Kuto	8.5E-06	9.5E-06	7.5E-08	2.1E-05	3.1E-05	3.4E-05	2.7E-07	7.6E-05	2.3E-05	2.6E-05	2.0E-07	5.7E-05
	Lafenwa	7.7E-06	8.2E-06	7.5E-08	1.8E-05	2.8E-05	3.0E-05	2.7E-07	6.5E-05	2.1E-05	2.2E-05	2.0E-07	4.9E-05
	Oke-Ibukun	6.8E-06	7.6E-06	7.5E-08	1.9E-05	2.5E-05	2.8E-05	2.7E-07	7.1E-05	1.8E-05	2.1E-05	2.0E-07	5.3E-05
	Iberekodo	6.8E-06	7.3E-06	7.5E-08	1.6E-05	2.5E-05	2.6E-05	2.7E-07	6.0E-05	1.8E-05	2.0E-05	2.0E-07	4.5E-05
	Gbanga	8.8E-06	1.0E-05	7.5E-08	2.4E-05	3.2E-05	3.8E-05	2.7E-07	8.7E-05	2.4E-05	2.9E-05	2.0E-07	6.5E-05
	Ijeun	8.3E-06	9.4E-06	7.5E-08	2.1E-05	3.0E-05	3.4E-05	2.7E-07	7.6E-05	2.3E-05	2.6E-05	2.0E-07	5.7E-05
	Bode	6.0E-06	9.0E-06	0.0E + 00	2.4E-05	2.2E-05	3.3E-05	0.0E + 00	8.7E-05	1.6E-05	2.5E-05	0.0E + 00	6.5E-05
	Asero	5.8E-06	6.5E-06	7.5E-08	1.5E-05	2.1E-05	2.4E-05	2.7E-07	5.4E-05	1.6E-05	1.8E-05	2.0E-07	4.1E-05
	Osiele	9.0E-06	1.1E-05	7.5E-08	3.0E-05	3.3E-05	4.1E-05	2.7E-07	1.1E-04	2.5E-05	3.1E-05	2.0E-07	8.2E-05
	Saje	7.3E-06	8.4E-06	7.5E-08	1.8E-05	2.7E-05	3.0E-05	2.7E-07	6.5E-05	2.0E-05	2.3E-05	2.0E-07	4.9E-05
	Olajide	7.5E-08	3.1E-24	7.5E-08	7.5E-08	2.7E-07	2.2E-23	2.7E-07	2.7E-07	2.0E-07	1.2E-23	2.0E-07	2.0E-07
∑CR	Kuto	4.6E-03				1.7E-02				1.2E-02
	Lafenwa	3.1E-03				1.1E-02				8.4E-03
	Oke-Ibukun	3.1E-03				1.1E-02				8.4E-03
	Iberekodo	4.4E-03				1.6E-02				1.2E-02
	Gbanga	3.9E-03				1.4E-02				1.1E-02
	Ijeun	3.3E-03				1.2E-02				9.0E-03
	Bode	3.3E-03				1.2E-02				8.9E-03
	Asero	3.5E-03				1.3E-02				9.6E-03
	Osiele	3.3E-03				1.2E-02				9.0E-03
	Saje	3.3E-03				1.2E-02				9.0E-03
	Olajide	8.6E-05				3.1E-04				2.4E-04
SD-standard deviation, Min-minimum, Max. maximum

The present study assessed the water quality index and health risk of groundwater around the vicinity of waste sanitation containers at different markets in Abeokuta metropolis, southwestern Nigeria. Most of the groundwater parameters showed higher significant values at the market area than in the control site in the Olajide community. The water quality index revealed values greater than 300 for groundwater in the market area, indicating unfit water for drinking. However, the control groundwater had a WQI level of less than 100, suggesting good water quality. The regression model established anti-correlation between the distance of the hand-dug wells to the waste sanitation facilities, and water parameters such as pH, TDS, total hardness, nitrate, BOD, total coliform, Cu, Fe, Mn, and Pb. This also established the negative effects of the waste sanitation facilities on the groundwater quality. The rotated varimax principal component analysis indicated the market waste sanitation facilities as the major source of pollution in the studied groundwater. The health risk assessment model established deleterious effects of Mn and Pb, having the HQs greater than the permissible limit of 1.0. The cancer risk data of Co in groundwater from the market area revealed values greater than the acceptable limit of 1.0 x 10^-4, establishing the possible development of cancer in adults, children, and infants upon exposure through ingestion. This study, therefore, recommends total overhaul of the existing waste management system around the markets in Abeokuta, in order to protect public health.

Acknowledgment

The authors are grateful to Miss. T. Aroyeun, of the Department of Environmental Management and Toxicology, and Mr.S.A. Omosuli of the Department of Agronomy, University of Ibadan, for their support during laboratory analysis.

Ethics approval and consent to participate (include appropriate approvals or waivers)

Not applicable.

Consent for publication (include appropriate statements)

Not applicable.

Funding (information that explains whether and by whom the research was supported)

No funds, grants or other support was received.

Conflicts of interest/Competing interests (include appropriate disclosures)

The authors declare that they have no conflict of interest.

Availability of data and material (data transparency)

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Code availability (software application or custom code)

Not applicable.

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somade2supplementry.docx

Effects of Waste Sanitation Facilities on Water Quality Index and Human Health Risk Assessment of Groundwater Resources in Abeokuta Markets, Southwestern Nigeria

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Study area

2.2 Sample collection and analysis

2.3 Data analysis

2.4 Evaluation of health risk of heavy metals

3. Results And Discussion

3.1 Physical, chemical, and microbiological characteristics of groundwater samples

3.2 Seasonal variation of groundwater parameters

3.3 Water quality index (WQI) of groundwater

4.4 Principal component analysis

4.5 Regression model of distance, sites, and season versus groundwater parameters

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1