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Spatial distributions and probabilistic risk assessment of exposure to heavy metals in groundwater in a peri-urban settlement: case study of Atonsu-Kumasi, Ghana
Groundwater for Sustainable Development 10 (2020) 100327
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Research paper
Spatial distributions and probabilistic risk assessment of exposure to heavy metals in groundwater in a peri-urban settlement: case study of Atonsu-Kumasi, Ghana Portia Annabelle Opoku a, b, *, Geophrey K. Anornu a, b, Abass Gibrilla c, Emmanuel de-Graft Johnson Owusu-Ansah d, Samuel Y. Ganyaglo c, Courage D. Egbi a, c a
Regional Water and Environmental Sanitation Centre, Kwame Nkrumah University of Science and Technology Kumasi, KNUST, Kumasi, Ghana Department of Civil Engineering, Kwame Nkrumah University of Science and Technology Kumasi, Ghana c Nuclear Chemistry and Environmental Research Centre, National Nuclear Research Institute, GAEC, Box LG 80, Legon-Accra, Ghana d Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana b
A R T I C L E I N F O
A B S T R A C T
Keywords: Groundwater Heavy metals Risk assessment Sensitivity analysis Ghana
Heavy metal contamination has been of major concern at local, regional, and global scales due to its direct effect on human health. In this study, physico-chemical parameters and heavy metals (Cr, Cd, Fe, Zn, Pb, Cu, Zn and Mn) concentration in drinking water comprising of 12 mechanized boreholes (MBH) and 44 hand dug wells (HDW) in Atonsu, a suburb of Kumasi, Ghana were assessed. Carcinogenic and non-carcinogenic health risk assessments, sensitivity and uncertainty analysis for children and adults were carried out using Monte Carlo simulations technique. The results showed that, the chemical composition of the groundwater varies over a wide range both in salinity and ionic composition but lowly mineralized (TDS« 1000 mg/L). All the physic-chemical parameters were below their respective guideline values. However, about 21% of HDW had NO3 values above the guideline value of 50 mg/L. Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn and Zn showed appreciable levels in some wells but below their respective guideline limits. However, 88.63% and 83.33% of the hand dug wells and boreholes had Pb above the guideline values. The noncarcinogenic hazard index (HI) for adult and children with respect to Fe and Mn were >1 signifying some level of risk. However, the carcinogenic HI with respect to Pb was <1. A sensitivity analysis for the oral and dermal exposure pathways for both adults and children indicated that the most important factors in the risk model are (1) the amount of the heavy metal consumed, (2) exposure duration and (3) ingestion rate. To protect public health, reduction of the metal’s concentrations, proper construction of the wells to prevent runoff seepage and comprehensive water quality monitoring program are recommended to avoid/minimise the potential risk to the population.
1. Introduction Groundwater is and will keep being a significant source of potable water for most rural and urban dwellers globally. In Sub-Saharan Africa, access to potable water supply is still a major challenge and groundwater drawn from boreholes and protected hand dug wells are the main improved water sources in most rural and peri-urban settlements (Foster et al., 2006). In Ghana, 62% of urban and 71% of rural communities depend on groundwater as their essential source of drinking water (Obiri-Danso et al., 2009). Boreholes and hand-dug wells have become a
major source of water for households and modern industrial use, particularly in territories and spots that do not get water from the local water supply company, Ghana Water Company Limited (GWCL). In view of the recognized well-being and economic effects associated with drinking contaminated water, the quality of groundwater meant for domestic and industrial consumption must be constantly monitored for sustainable groundwater security and management strategies (Sathees et al., 2013). Heavy metals are naturally occurring elements, and are present in varying concentrations in all ecosystems. They are found in elemental
* Corresponding author. Regional Water and Environmental Sanitation Centre, Kwame Nkrumah University of Science and Technology Kumasi, KNUST, Kumasi, Ghana. E-mail address: [email protected] (P.A. Opoku). https://doi.org/10.1016/j.gsd.2019.100327 Received 19 November 2018; Received in revised form 16 April 2019; Accepted 23 December 2019 Available online 3 January 2020 2352-801X/© 2020 Elsevier B.V. All rights reserved.
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forms and in a variety of chemical compounds. Human activities have drastically changed the biochemical cycle and balance of some heavy metals. According to Nriagu and Pacyna (1995) and Alaee et al., 2003, between 1850 and 1990, the productions of Pb, Cu and Zn have increased tenfold as a result of human activities. The main anthropo genic sources of heavy metals are mining and industrial activities like foundries, smelters, combustion of fossil fuel and gasoline, and waste incinerators. In recent times, trace or heavy metal contamination in groundwater has been a major source of concern on local, regional, and global scales. Based on their concentration levels and toxicity, they can cause serious health and environmental hazards. This concern is highlighted in several scientific studies on As contamination of groundwater in Bangladesh (Alam et al., 2003) and Cd, Pb, Cu, and Zn contamination of ground water and vegetables in Europe, Asia and Africa (Hadzi et al., 2018; Miller et al., 2004; Zietz et al., 2003; Anawar et al., 2002) with its related human health issues. Anthropogenic activities like improper disposal of waste, have been found culpable for the elevation of heavy metal con centrations in groundwater (Saleem et al., 2015). However, heavy metals are also found naturally in the earth’s crust. Exposure to high concentrations of heavy metals like Fe and Mn in drinking water usually imparts aesthetic mutations to some consumers while metals such as As, Cr, Pb can cause serious health problems including diarrhoea, derma titis, nausea, liver and kidney damage, as well as respiratory problems (Hadzi et al., 2018). To reduce the impact of heavy metals on human health and the environment, there has been a significant development of cost-effective technologies for removing heavy metals in drinking water and waste water such as Zn (Senthil Kumar et al., 2012), Pb (Senthil Kumar et al., 2013) and Cr (Yaashikaa et al., 2019). Femina Carolin et al., 2017 provides an extensive literature review on heavy metal removal technologies in drinking water and waste water. Atonsu is one of the major peri-urban territories in the Kumasi Metropolis in the Ashanti Region of Ghana (Abdul-Razak, 2012). The territory is part of the wetland zone within the Ashanti Region. As with all wetland areas, the water table here is very shallow and groundwater can be assessed few meters below the surface (Abdul-Razak, 2012). The majority of the masses living in this zone depend on hand-dug wells, springs and boreholes. These sources of water are relatively affordable when contrasted with penetrated wells which are sunk to more profound depths. The proliferation of hand dug wells is expected due to the deficient and irregular supply of water from GWCL. The inhabitants of Atonsu do not fetch water directly from the boreholes or hand dug wells but rather pump the water into their houses through a pipe distribution system. There is limited literature on heavy metal contamination in the Atonsu area. However, several studies have been carried out in other parts of Ghana which have indicated increased concentrations of heavy metals and other contaminants in groundwater, surface water and sed iments in peri urban areas (Cobbina et al., 2013; Obiri, 2007; Obiri et al., 2010; Armah et al., 2011). The extent and degree of heavy metal contamination reported in different parts of the country vary depending on geology and the anthropogenic activities; mining, industry, improper waste disposal, and heavy vehicular traffic occurring in the area. Despite the existence of several data on trace metal contamination in Ghana, most of the studies focused on their compliance with WHO (2004) acceptable limits without addressing the potential human health risk to children, teens and adults who are exposed to these metals via drinking water using comprehensive risk assessment models. Health risk assessment models provide a systematic form of quanti tative and semi-quantitative descriptions of the potential human or environmental health effect attributed to exposure to contaminants in drinking water. The conventional approach is usually based on a single or deterministic value of the input parameters; concentrations of the contaminant, ingestion rate, body weight, contact frequency and dura tion parameters, to compute the risk. The approach provides a numeral value for average scenarios of the risk; it may over or under estimate the actual risk because uncertainty factors are not considered (Lonati and
Zanoni, 2012). The probabilistic approach using the Monte Carlo simulation provides a more realistic risk assessment that accounts for different kinds of uncertainties, sensitivity to different exposure path ways and the effects of various intervention scenarios (Fallahzadeh et al., 2018; Augustsson and Berger, 2014; Morisset et al., 2013). The probabilistic approach using the Monte Carlo simulation regards all the input parameters for calculating the risk as distributions or random variables to obtain a wide range of outcomes, a risk or hazard quotient (HQ), after repeated simulations usually 10,000 or more (Zhang et al., 2017; Yousefi et al., 2018). The final outcome of a given risk is obtained in a form of a probability distribution. This method has been used in various studies to assess the risk of pollutants in drinking water (Zhang et al., 2017; Fallahzadeh et al., 2018; Lonati et al., 2007). The main purpose of the study was to assess the spatial distribution of heavy metal contamination in drinking water and their potential human health risk (oral ingestion and dermal exposure) within the Atonsu community through a quantitative health risk assessment model. The sensitivity and uncertainty analysis due to exposure to heavy metals were also evaluated. The results of the water quality analysis were buttressed by collecting data on cases of water-borne diseases (diar rhoeal diseases, suspected cholera and typhoid fever) within the com munity from the Kumasi South Hospital, The Hart Adventist Hospital and Christ the King Hospital. The outcome and findings of this study can serve as a baseline data and provide information on human health risk associated with the consumption of heavy metal contaminated drinking water. 2. Materials and methods 2.1. Study area Atonsu is a peri-urban community in the Kumasi metropolis in the Ashanti Region of Ghana, Africa. The study area is located between 6� 370 4000 N and 6� 400 000 N and 1� 340 2000 W and 1� 370 000 W as shown in Fig. 1. It has a population of about 8790. The area lies in the transitional forest zone; specifically within the moist semi-deciduous south-east ecological zone and is characterised by a wet sub-equatorial climate (Ghana Statistical Service, 2014). The Kumasi metropolis lies within the plateau of the south-west physical region which ranges from 250 to 300 m above sea level. The topography is undulating. The area lies within the Birimian Super-group which is a major part of the Paleo-proterozoic terrain of Ghana underlain, by supra-crustal and intrusive rocks which display a characteristic pattern of NE-trending, parallel, approximately evenly-spaced volcanic belts called Volcano-Plutonic Group and inter vening sedimentary basins called the Sedimentary-Volcano-Sedimen tary Group (Geological Map of Ghana, 2009). The metropolis is tra versed by a major river (Owabi) and streams like Subin, Wiwi, Sisai, Aboabo and Nsuben (Ghana Statistical Service, 2014). 2.2. Water sample collection, preparation and treatment Water samples were collected from fifty-six (56) point sources comprising twelve (12) mechanized boreholes (MBH) and forty-four (44) hand-dug wells (HDW) as shown in Fig. 1. 500 ml of each sample was collected into plastic bottles prewashed with a 1:1 concentrated nitric acid and distilled water solution and eventually rinsed with only distilled water. Sample bottles were labeled with unique codes and date of collection for easy identification. The samples were collected in du plicates after every fifth sample. Sample bottles were acidified to pH < 2 with concentrated HNO3 before being transported to the laboratory. The container was rinsed three times with water from the well of interest before samples were collected from each point source. In-situ mea surement of pH, electrical conductivity (EC), total dissolved solids (TDS), temperature, salinity and the geographical coordinates were done. All water samples were then kept in an ice chest containing ice bricks at a temperature below 5 � C and transported to the Ghana Atomic 2
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Fig. 1. Map of the study area showing sampling sites.
Energy Commission (GAEC) laboratory. At the laboratory, the samples were kept in a refrigerator at 4 � C prior to analysis (Naveedullah et al., 2014).
were acid digested and subsequently analysed for the heavy metals of interest through the same procedure (Brady et al., 2014). The samples were further analysed in triplicates and after every 5 samples, a cali bration standard was used to check the response and efficiency of the instrument alongside the blank which was constantly used to check for contamination. Reproducibility and recovery tests were conducted using a Multi Element Reference Standard (Standard Agilent Technologies Multi Element Reference Standard 2A) as the unknown. The percentage recovery obtained for the standard ranged from 99.2 to 105.8%. This suggests that, the error linked with the analysis is negligible; hence, the results are reliable.
2.3. Digestion, analysis of water samples and quality control Acid digestion of the samples was performed using a commercial high-pressure laboratory microwave oven (Milestone Ethos 1600 Mi crowave Lab station, Sorisole, Italy). This equipment has ten 100 mL tetrafluoro methoxy (TFM) tubes operating at a frequency of 50 Hz, with an energy input of 2200 W. Analytical grade chemicals and reagents were used for digesting the samples; HCl (35% m/v), HNO3 (69% m/v) and HClO4 (71–73% m/v). High purity deionised water (resistivity 18.2 MΩ cm) was used as blank, dilution and preparation of solutions where required. The trace metals were analysed using a Fast-sequential Atomic Ab sorption Spectrometer (Varian AA240FS). The equipment was first calibrated with standards, and the lamps optimized then the water samples were aspirated through the capillary tube of the Atomic Ab sorption Spectrometer (AAS) and the concentrations and absorbance of heavy metals were displayed on the monitor. To underscore the reli ability of the analytical method, quality control (QC) and blank samples
2.4. Health risk assessment and risk characterization The major routes for heavy metal entry into the human body are (1) direct ingestion of substrate particles (Ding), (2) inhalation of suspended particles through the mouth and nose (Dinh), and (3) dermal absorption of elements in particles stuck to exposed skin (Dder) (De Miguel et al., 2007). For this research, health risk assessment of heavy metals in drinking water were estimated using the oral ingestion and dermal absorption scenarios for receivers (adults and children) based on the USEPA, (2002) 3
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risk assessment methodology. Inhalation as an entry way was excluded from the assessment because of unavailable toxicological data such as inhalation reference dose, and transfer efficiency from water to air in either database or published literature (Zhang et al., 2017).
2.7. Monte Carlo simulation and sensitivity analysis Health risk assessments are usually accompanied with high un certainties especially when single point values are used to estimate risk for a given population. To cater for uncertainty and variability analysis, input values were described with probabilistic distributions and subject it through a Monte-Carlo Simulation with hypercube sampling method in 100,000 iterations. This process was applied in order to separate uncertainty from variability to minimise their influences on the risk assessment estimates. Priorities were given to the values obtained from the study area obtained from the questionnaires administered. The sensitivity analysis was also performed to identify the input parameters which have significant impact on the model output. In this study, the Monte-Carlo Simulation and sensitivity analysis were done in Microsoft excel with in-built @ Risk software (Palisade corporation, version 7.4). All input parameters are shown in Table 3.
2.5. Exposure models The questionnaires administered helped to highlight the parameters needed for the risk assessment of the heavy metals in the water. It also gathered information regarding the treatment, usage and general quality of the water. The exposure doses in water through ingestion and dermal contact was computed using equations (1) and (2) (Naveedullah et al., 2014). CDIing
w
¼
Cw � IRw � EF � ED BW � AT
(1)
CDIder
w
¼
Cw � SA � Kp � ETs � EF � ED � CF BW � AT
(2)
2.8. Data and statistical analysis
CDIing w and CDIder w are the chronic daily intake through ingestion and dermal contact of water respectively in mg/kg/day. Cw is the concen tration of the heavy metal in the water sample in mg/L, IRw is the ingestion rate of the water in L/day, EF is the exposure frequency in days/year, ED is the exposure duration in years, BW is the body weight in kg, AT is the average time of non-carcinogenic and carcinogenic risk in days, CF is the unit conversion factor for water in 1L/1000 cm3, Kp is the dermal permeability coefficient in cm/hr, SA is exposed skin area in cm3 and ETs is the exposure time in hr/day.
A total of 150 questionnaires were administered and 145 re spondents completed their questionnaires, giving a response success rate of 96.67%. Some additional data, needed for some of the input param eters were obtained from nearby hospitals after obtaining ethical clearance from the Committee on Human Research, Publications and Ethics (CHRPE), School of Medical Sciences, KNUST, Kumasi. The @Risk 7.5 software with add-on to Excel was constructed in Microsoft Excel and used to estimate the probability of health risk and sensitivity anal ysis. The software subjected the input data to the Monte-Carlo simula tion of 100,000 iterations with hypercube sampling for probability of health risk and sensitivity analysis in order to account for the variability and uncertainty in the model parameters.
2.6. Hazard quotient (HQ) and hazard index (HI) The health risk was characterised using the hazard quotient (HQ) and hazard index (HI) due to exposure to toxicants (WHO, 1996; USEPA, 2004). These were calculated using the equations (3)–(6). For non-carcinogens: HQ ¼
CDI Ref D
3.1. Groundwater chemistry Table 4 presents a statistical overview of the hydro-chemical data of HDW and MBH in the study area. The HDW had pH being slightly acidic to slightly basic and ranging from 5.31 to 8.10 while the MBH pH ranged from 5.58 to 7.4. The chemical composition of the HDW and MBH varies over a wide range both in salinity and ionic composition but lowly mineralized with TDS< 600 mg/L and <100 mg/L respectively (Table 4). The EC and TDS of the HDW ranged from 92 μS/cm to 1065 μS/cm with an average of 371 μS/cm and 51 mg/l to 586 mg/l with an average of 204 mg/l respectively. All water samples are relatively fresh with TDS « 1000 mg/L. Salinity of the HDW range from 0.00 ppt to 0.50 ppt with an average of 0.10 ppt while all the MBH recorded salinity of 0 ppt. Fig. 2 shows spatial distribution of EC and TDS in both MBH and HDW. TDS is a measure of the total ionic concentrations of dissolved minerals in water. Natural groundwater contains mineral ions which dissolve slowly in soil particles, sediments, and rocks as the water moves along mineral surfaces in the pores or fractured zones within the aquifer. Other dissolved solids are from precipitation water or river water that recharges the aquifer. Others are also influenced by human activities or other forms of pollution which can affect the concentrations of TDS in
(3)
n X
HI ¼
3. Results and discussion
(4)
HQi i¼1
The variable RefD represent the reference dose in a specific pathway in mg/kg/d. The reference doses for non-carcinogenic heavy metals are listed in Table 1. For carcinogens: The risk posed by individual carcinogenic metals was computed as the product of the slope factor (SF) and chronic daily intake (CDI). The total cancer risk was calculated by adding the risk associated with in dividual carcinogenic heavy metals. (5)
Risk ¼ SFi � CDIi n X
(6)
SFi � CDIi
Rtotal ¼ i¼1
The classification based on Man et al. (2010) and Fryer et al. (2006) were used to assess the level of risk on the health of humans (Table 2).
Table 2 Risk assessment classification.
Table 1 Metals and their reference doses used for risk assessment. Metal
RefDing (mg/kg/d)
RefDderm (mg/kg/d)
Reference
Pb Zn Fe Mn
0.0014 0.3000 0.7000 0.0240
0.00042 0.06000 0.14000 0.00096
Huang et al. (2015) Huang et al. (2015) Naveedullah (2014) Naveedullah (2014)
4
Index
Classification
Reference
HI < 1
No obvious health risk
Man et al. (2010)
^ Rt > 1:0*104
Unacceptable, poses health risk
Fryer et al. (2006)
^ Rt < 1:0*106
No significant health risks
WHO (2011)
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Table 3 Input parameters used in risk assessment model. Input Parameters
Symbol
Distribution type for the simulation
Values Children
Adults
Concentration of heavy metal in water Chronic daily intake through ingestion
Cw CDIdermal
– –
Measured calculated
Measured calculated
Chronic daily intake through dermal absorption
CDIingestion
–
calculated
calculated
Ingestion Rate Exposure frequency Exposure Duration Exposure Time Body Weight Averaging Time Skin Surface Area Dermal permeability factor
IngR EF ED ETs BW AT SA CF
Normal Triangular Fixed value lognormal Lognormal Fixed value Lognormal Fixed value
1.7 � 0.4a (180; 345; 365) 18 0.13 � 0.0085a 50.12 � 1.45a 2190 7422 � 1.25a 1 � 10 3
3.3 � 1.00a (180; 345; 365) 50 0.13 � 0.0085a 63.97 � 1.65a 18250 18182 � 1.10a 1 � 10 3
Unit
References
mg/L mg/kg/ day mg/kg/ day L/day days/year years h/day kg days cm2 L/cm3
This Study This Study US EPA (2004) This Study This Study Zhang et al. (2017) US EPA (2004) US EPA (2004)
Values in the bracket means (Min; Max; Mean). a Represents mean � SD. Table 4 Comparison of borehole and hand-dug well water quality results with WHO guideline value. Parameter
WL Depth pH EC TDS Sal Temp Naþ Kþ Ca2þ Mg2þ TH HCO3 F Cl NO3 PO34 SO24 2þ Pb Co Cr Fe Mn Zn Cu Cd
Hand dug wells, HDW (N ¼ 44)
Mechanized Boreholes, MBH (N ¼ 12)
WHO Guide Value
% Outside WHO Guide Value (HDW)
% Outside WHO Guide Value (MBH)
unit
Mean
Min.
Max.
Mean
Min
Max
m m
1.57 2.75 6.54 371.39
0.02 0.27 5.31 92.00
6.55 8.07 8.10 1065.00
– – 6.77 116.73
– – 5.58 47.80
– – 7.47 250.00
– – 6.5–8.5 –
– – 47.73 –
45.46 0
204.45 0.10 28.58 38.97 2.97 13.84 0.20 110.55 65.70 0.02 28.72 26.53 0.40 6.50 0.022 – – 0.047 – – – –
51.00 0.00 26.90 6.80 0.50 3.20 0.01 36.00 10.00 0.00 3.11 0.00 0.00 0.00 <0.001 <0.005 <0.006 <0.006 <0.002 <0.001 <0.003 <0.002
586.00 0.50 29.90 70.30 8.71 65.40 0.58 328.00 170.00 0.18 85.39 87.23 7.96 26.43 0.04 <0.005 <0.006 0.20 0.11 0.01 <0.003 <0.002
62.31 0.00 30.63 12.74 1.90 5.40 0.06 65.67 37.33 0.01 5.49 5.14 0.22 1.17 0.01 – – – – – – –
14.71 0.00 28.1 3.60 0.21 1.60 0.01 48.00 10.00 0.00 0.77 0.00 0.00 0.00 <0.001 <0.005 <0.006 <0.006 <0.002 <0.001 <0.003 <0.002
137.00 0.00 34.50 21.00 4.57 12.80 0.34 180.00 70.00 0.01 11.55 22.00 0.85 6.25 0.02 <0.005 <0.006 0.0095 0.006 0.003 <0.003 <0.002
– – – 200 30 200 150 500 – 1.5 250 50 – 500 0.01 – 0.05 0.30 0.40 3.0 2 0.003
– – – 0 0 0 0 0 – 0 0 20.50 – 0 88.63 – 0 0 0 0 0 0
– – – 0 0 0 – 0 – 0 0 0 – 0 83.33 – 0 0 0 0 0 –
μS/
cm mg/L Ppt (� C) mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
WL ¼ water level; EC ¼ Electrical conductivity; TDS ¼ Total Dissolved Solids; Sal ¼ salinity; Tempe ¼ temperature; TH ¼ total hardness.
water samples (DANR and NRSC, 2003). The observed low EC, TDS and salinity of the MBH was due to the fact that, all the MBH were fitted with desalinization systems for private home use. The dominant cation for both HDW and MBH is Naþ with mean concentration of 38.97 mg/L and 12.74 mg/L respectively. This is fol lowed by Ca2þ with mean concentration of 13.84 mg/L and 5.40 mg/L and then by Kþ and Mg2þ, which have mean concentrations of 2.97 and 5.40 and 0.20 and 0.06 mg/L, respectively. However, all the cations are below their respective guideline WHO and GSA guideline values. The cation dominance is in the order of Ca2þ
L (WHO, 2004). This could be due to the shallow nature of the HDW and poor construction (without concrete lining). This allows for contami nation from sources such as waste from livestock operations, septic tank or drained field effluent and crop fertilizers. 3.2. Concentrations of the heavy metals The results of the heavy metal concentrations in borehole and the hand dug wells are presented in Table 4. The mean concentrations of the heavy metals are generally low when compared with the various guideline values (Table 4). Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn, Zn and Fe showed appreciable levels in some wells, however, all the values were below their respective guideline values. Pb recorded the highest concentrations in both the hand dug wells and the mechanized boreholes ranging from 5
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Fig. 2. Spatial Distribution of TDS in samples from boreholes and hand-dug wells found within the study area.
<0.001 to 0.04 mg/L and <0.001–0.02 mg/l with mean values of 0.02 mg/l and 0.01 mg/l respectively. The primary sources of lead in most drinking water sources is the piping system, dust/soil containing lead and from anthropogenic activities (metal scrap dealers, blacksmith, land fill sites and improper disposal of waste). There is no piping system in the area and all the samples were taken from open hand dug wells with poor sanitary conditions and drainage system, hence, the likely source of the Pb could be from the atmospheric dust, soil and run off from metal scrap dealers, blacksmith and land fill sites. Fig. 3 shows the spatial distribution of Zn, Pb, Fe and Mn in the study area.
CDI though ingestion and dermal exposure pathways for carcinogenic and non-carcinogenic metals for adults and children. The heavy metals CDI varied widely among the different categories of population under study as well as for the scenarios. From Table 5, the amount of CDI of heavy metals through ingestion is much higher than the amount of heavy metals taken through dermal contact. Considering the mean values for the CDI, the dermal exposure pathway recorded generally a three or more orders of magnitude lower than the oral ingestion for both adults and children in the study area (Table 5). Therefore, ingestion is considered to be the primary exposure route of heavy metal concen trations within the study area for both adult and children. Based on the USEPA (2004), hazard quotient (HQ) values greater than 1 for any toxicant of interest can pose a health risk to consumers and measures must be put in place to reduce the concentrations. From Table 5, the HQ values for dermal route exposure were below unity for both adults and children for Fe, Mn and Zn. This indicates that there is minimal risk to human health when contact with the water is through the skin. However, HQ for oral route exposure was greater than 1 for Fe and Mn in both adults and children. Given that the HQ value is higher than 1 for adults and children, the health of this group is highly at risk. Table 5 shows the hazard index (HI), a summation of all the HQs (HQing þ HQderm) for the individual metals. HI for adult has Fe and Mn having values greater than 1; that is 1.351and 6.484 respectively whilst Zn had a value of 0.362 which is within the acceptable limits. On the other hand, HI for children had Mn and Fe greater than 1 with values of 6.274 and 1.306 respectively. The degree of risk posed is in the order of Mn > Fe > Zn for non-carcinogenic risk for both adult and children.
3.3. Human health risk assessment 150 questionnaires were administered in the study area. The ques tionnaire was structured into five sections; personal data (age, weight, height), bio data of water (source, consumption and storage), water quality (treatment and maintenance, health risk), demographic and socio-economic data. Table 5 shows the input parameters used for the risk assessment model obtained from the questionnaires. The exposure frequency was assumed to be 365 days/year since all the respondents had not travelled out of the zone during holidays and were always available. Using the exposure frequency of 365 days/year also helped to estimate the worstcase scenario risk. In the current study, both carcinogenic and non-carcinogenic health risks were used to assess the human health risks due to exposure to heavy metals through ingestion and dermal contact. Table 5 shows the 6
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Fig. 3. Spatial Distribution of heavy metal concentrations found within the study area. Table 5 CDI and HQ for oral and dermal exposure pathways. Non-carcinogenic
Adult Children
Carcinogenic
Adult Children
Metal
CDI Oral
CDI Dermal
HQ Oral
HQ Dermal
H1
Fe Mn Zn Fe Mn Zn Pb Pb
0.9419 0.1530 0.1082 0.9115 0.1480 0.1047 0.0013 0.0012
6.58E-04 1.07E-04 4.54E-05 6.26E-04 1.02E-04 4.31E-05 3.53E-06 3.36E-06
1.346 6.373 0.361 1.302 6.168 0.349 0.903 0.874
0.005 0.111 0.001 0.004 0.106 0.001 0.008 0.008
1.35 6.48 0.36 1.31 6.27 0.35 0.91 0.88
Since the communities depend on these water sources for drinking, reducing and controlling the concentration of Fe and Mn is necessary to minimise the potential health implication of consuming excess Mn and
Fe such as stomach upset, nausea, vomiting, diarrhoea, irregular heartbeat and low blood pressure. At the moment, the most common and cheap ways to remove heavy metals from drinking water include 7
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activated carbon and ion exchange (Fu and Wang, 2011). In the case of carcinogenic health risk using Pb, all the samples for both adults and children had HQ < 1 indicating no potential health risk. HI for carcinogenic risk for adult and children were all below unity and this means their concentrations found in the water do not pose any health risk to consumers with values of 0.911 and 0.882 respectively.
The sensitivity analysis for adult and children through oral exposure indicates that risk is very sensitive and has a strong to very strong pos itive correlation to exposure duration and concentration of the metals itself (Fig. 4). The sensitivity analysis for contact through dermal exposure for both adults and children also show a strong positive cor relation to exposure duration and concentration of the metals. Ingestion rate also showed a positive but weak correlation to exposure in both adults and children. Body weight however, showed an inverse and weak correlation to risk. This means that, the weight of an individual has no direct influence on the concentration of metals the body can accumulate and therefore does not influence risk. The sensitivity analysis result indicates that, the Cw and IR variables for oral ingestion can be used to decrease the risk of ingesting these heavy metals in the study area. Thus, reducing the heavy metal con centration in the drinking water, exposure duration and ingestion rate can help mitigate the non-carcinogenic risk for the studied population.
3.4. Sensitivity analysis The Spearman’s correlation coefficient was used to analyse the sensitivity of the parameters used in the risk assessment. Fig. 4 shows the relationship between body weight, exposure duration, ingestion rate and the concentrations of the heavy metals. The sensitivity analysis was carried out to easily identify the relevant variables contributing to the risk estimates in the model for both adults and children. The MonteCarlo simulation was used to help minimise the uncertainty of calcula tions in the risk assessment. Figures obtained directly from the field were given the highest priority whenever appropriate, to reduce the assess ment uncertainty caused by distribution factors like water ingestion rate, body weight, concentration of metal in water, exposure duration, exposure frequency and exposure time.
3.5. Assumptions, uncertainty and limitations associated with the model Several studies have established an exposure-response relationship between the level/duration of exposure to heavy metals and the
Fig. 4. Sensitivity analysis for oral and dermal exposure to heavy metal contaminants. 8
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occurrence of heavy metals poisoning (Anawar et al., 2002; Obiri et al., 2010). However, the quantitative relationship between intake and risk is still not confirmed due to uncertainties associated with the prevalence of heavy metal poisoning and drinking of heavy metals contaminated water. The Monte Carlo Simulations greatly enhanced the health risk assessment due to the quantification of the uncertainties. However, the sensitivity analysis showed that, other uncertainties could still not be accounted for in some of the input parameters of the model that affected the risk assessment. For examples, the amount of the heavy metal consumed by the individual is an important factor in the risk estimate. In our estimation, we assume that, heavy metal in drinking water is the only source of daily heavy metal intake. However, several studies have proved that, heavy metals vegetables, cereals, root crops, dust particles, soil etc can pose significant health risk. Hence, our assumptions could lead to uncertainties of heavy metals exposure and underestimation of the risk. Therefore, future studies on risk assessment should consider taking more data from each exposure pathways to reduce the uncer tainty in the risk estimate.
model are the amount of the heavy metal consumed, exposure duration and ingestion rate. The current study therefore provides further insights on heavy metal contamination in drinking water and its potential effect on human health at Atonsu. It is therefore, imperative to take measures to reduce the heavy metals in some of the wells, properly construct the wells to avoid run off seepage and also implement a comprehensive monitoring of the water quality to avoid the potential risk to the population. Acknowledgments The research was funded by the Regional Water and Environmental Sanitation Centre (RWESCK), Civil Engineering Department, Kwame Nkrumah University Science and Technology (KNUST), Kumasi with funding from Ghana Government through the World Bank under the African Centre’s of Excellence project. The views expressed in this paper do not reflect those of the world bank, Ghana Government and KNUST. A special thanks goes to all research scientists, technicians and labora tory assistants of the Ghana Atomic Energy Commission (GAEC) for the technical support. The author wishes to thank editor and the anonymous reviewers for their constructive comments and suggestions.
3.6. Recommendations for groundwater management, prevention and control of heavy metals contamination Based on the field observations and analysis carried out, we recom mend the following for the prevention and control of the potential risk of heavy metals contaminations.
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� Boreholes and hand wells with high metals concentrations should be treated before drinking � There should be a comprehensive audit and water quality assessment of all drinking water sources in the area (chemical, bacteriological and heavy metal). � Future studies on risk assessment should include inhalation exposure to improve on the estimate. � The hand dug wells should be properly lined and the onsite sanita tions around the wells should be taken seriously to prevent runoff entering the wells. � The data generated from project implementing agencies and NGOs together with all other available data should be made available to the district assemblies to guide future groundwater management strategies. � There should be a strong advocacy, health education and public awareness on the effect of heavy metals contamination in drinking water on human health. 4. Conclusion The concentration, carcinogenic and non-carcinogenic health risk of Cr, Cd, Fe, Zn, Pb, Cu, Zn and Mn in drinking water (12 boreholes and 44 hand dug wells) in Atonsu, a suburb of Kumasi, Ghana is presented in this study. The results showed that, the chemical composition of the groundwater samples varies over a wide range both in salinity and ionic composition but lowly mineralized with TDS « 1000 mg/L. All the measured parameters were below their respective guideline values. However, high NO3 concentrations were recorded in most of the HDW with about 21% above the guideline value of 50 mg/L. Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn and Zn showed appreciable levels in some wells but below their respective guideline limits. However, 88.63% and 83.33% of the hand dug wells and boreholes had Pb above the guideline values respectively. Although other sources could be accounting for the presence of the heavy metals in water, anthropogenic activities are suspected to be the principal contributor. The non-carcinogenic hazard index (HI) for adult and children with respect to Fe and Mn were greater than 1 indicating some level of risk. However, the carcinogenic HI with respect to Pb is < 1. A sensitivity analysis for the oral and dermal exposure pathways for both adults and children indicated that the most important factors in risk 9
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Groundwater for Sustainable Development 10 (2020) 100327 freshwater sediments—a case study from Mangla Lake, Pakistan. Environ. Nanotechnol.Monit. Manag. 4, 27–36. Sathees, K., Thirumalaivasan, D., Radhakrishnan, N., Mathew, S., 2013. Groundwater Vulnerability Assessment Using SINTACS Model, Geomatics, Natural Hazards and Risk. Senthil Kumar, P., Senthamarai, C., Sai Deepthi, A.S.L., Bharani, R., 2013. Adsorption isotherms, kinetics and mechanism of Pb (II) ions removal from aqueous solution using chemically modified agricultural waste. Can. J. Chem. Eng. 91 (12), 1950–1956. Senthil Kumar, P., Ramalingam, S., Abhinaya, R.V., Kirupha, S.D., Vidhyadevi, T., Sivanesan, S., 2012. Adsorption equilibrium, thermodynamics, kinetics, mechanism and process design of zinc (II) ions onto cashew nut shell. Can. J. Chem. Eng. 90 (4), 973–982. USEPA, 2004. Risk Assessment Guidance for Superfund Vol. 1 Human Health Evaluation Manual, Part E, Supplemental Guidance from Dermal Risk Assessment. Office of Emergency and Remedial Response, Washington, DC, USA. WHO, 1996. Trace Elements in Human Nutrition and Health Geneva: World Health Organization. World Health Organization. WHO, 2004. Guidelines for Drinking Water Quality, third ed. World Health Organization, Geneva. WHO, 2011. Guidelines for Drinking-Water Quality, fourth ed. World Health Organization, Geneva. Yaashikaa, P.R., Kumar, P.S., Babu, V.M., Durga, R.K., Manivasagan, V., Saranya, K., Saravanan, A., 2019. Modelling on the removal of Cr (VI) ions from aquatic system using mixed biosorbent (Pseudomonas stutzeri and acid treated Banyan tree bark). J. Mol. Liq. 276, 362–370. Yousefi, M., Ghoochani, M., Hossein Mahvi, A., 2018. Health Risk Assessment to Fluoride in Drinking Water of Rural Residents Living in the Poldasht City. Northwest of Iran. Zhang, L.E., Huang, D., Yang, J., Wei, X., Qin, J., Ou, S., Zhang, Z., Zou, Y., 2017. Probabilistic risk assessment of Chinese residents’ exposure to fluoride in improved drinking water in endemic fluorosis areas. Environ. Pollut. 222, 118–125. Zietz, B.P., Dieter, H.H., Lakomek, M., Schneider, H., Keßler- Gaedtke, B., Dunkelberg, H., 2003. Epidemiological investigation on chronic copper toxicity to children exposed via the public drinking water supply. Sci. Total Environ. 302 (1), 127–144.
Lonati, G., Zanoni, F., 2012. Probabilistic health risk assessment of carcinogenic emissions from a MSW gasification plant. Environ. Int. 44, 80e91. Huang, Y., Li, T., Wu, C., He, Z., Japenga, J., Deng, M., Yang, X., 2015. An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils. Journal of Hazardous Materials 299, 540–549. Lonati, G., Cernuschi, S., Giugliano, M., Grosso, M., 2007. Health risk analysis of PCDD/F emissions from MSW incineration: comparison of probabilistic and deterministic approaches. Chemosphere 67 (9). S334eS43. Man, Y.B., Sun, X.L., Zhao, Y.G., 2010. Health risk assessment of abandoned agricultural soils based on heavy metal contents in Hong Kong, the world’s most populated city. Environ. Int. (36), 570–576. Miller, J.R., Hudson-Edwards, K.A., Lechler, P.J., Preston, D., Macklin, M.G., 2004. Heavy metal contamination of water, soil and produce within riverine communities of the Rıo Pilcomayo basin, Bolivia. Sci. Total Environ. 320 (2), 189–209. Morisset, T., Ramirez-Martinez, A., Wesololek, N., 2013. Probabilistic Mercury Multimedia Exposure Assessment in Small Children and Risk Assessment. Published in journal of Environment International by Elsevier Limited. https://doi.org/ 10.1016/j.envint. Naveedullah, M.Z.H., Yu, C., Shen, H., Duan, D., Shen, C., Lou, L., Chen, Y., 2014. Concentrations and human health risk assessment of selected heavy metals in surface water of the siling reservoir watershed in Zhejiang Province, China. Pol. J. Environ. Stud. 23 (3), 801–811. Nriagu, J.O., Pacyna, J.M., 1995. Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature 333 (6169), 134–139. Obiri, S., 2007. Determination of heavy metals in water from boreholes in dumasi in the wassa west district of western region of republic of Ghana. Environ. Monit. Assess. 130 (1–3), 455–463. Obiri, S., Dodoo, D.K., Essumang, D.K., Armah, F.A., 2010. Cancer and non-cancer risk assessment from exposure to arsenic, copper, and cadmium in borehole, tap, and surface water in the Obuasi municipality, Ghana. Hum. Ecol. Risk Assess. 16 (3), 651–665. Obiri-Danso, K., Adjei, B., Stanly, K.N., Jones, K., 2009. Microbiological quality and metal levels in wells and boreholes water in some peri- urban communities in Kumasi Ghana. Afr. J. Environ. Sci. Technol. 59–66. Saleem, M., Iqbal, J., Shah, M.H., 2015. Geochemical speciation, anthropogenic contamination, risk assessment and source identification of selected metals in
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Research paper
Spatial distributions and probabilistic risk assessment of exposure to heavy metals in groundwater in a peri-urban settlement: case study of Atonsu-Kumasi, Ghana Portia Annabelle Opoku a, b, *, Geophrey K. Anornu a, b, Abass Gibrilla c, Emmanuel de-Graft Johnson Owusu-Ansah d, Samuel Y. Ganyaglo c, Courage D. Egbi a, c a
Regional Water and Environmental Sanitation Centre, Kwame Nkrumah University of Science and Technology Kumasi, KNUST, Kumasi, Ghana Department of Civil Engineering, Kwame Nkrumah University of Science and Technology Kumasi, Ghana c Nuclear Chemistry and Environmental Research Centre, National Nuclear Research Institute, GAEC, Box LG 80, Legon-Accra, Ghana d Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana b
A R T I C L E I N F O
A B S T R A C T
Keywords: Groundwater Heavy metals Risk assessment Sensitivity analysis Ghana
Heavy metal contamination has been of major concern at local, regional, and global scales due to its direct effect on human health. In this study, physico-chemical parameters and heavy metals (Cr, Cd, Fe, Zn, Pb, Cu, Zn and Mn) concentration in drinking water comprising of 12 mechanized boreholes (MBH) and 44 hand dug wells (HDW) in Atonsu, a suburb of Kumasi, Ghana were assessed. Carcinogenic and non-carcinogenic health risk assessments, sensitivity and uncertainty analysis for children and adults were carried out using Monte Carlo simulations technique. The results showed that, the chemical composition of the groundwater varies over a wide range both in salinity and ionic composition but lowly mineralized (TDS« 1000 mg/L). All the physic-chemical parameters were below their respective guideline values. However, about 21% of HDW had NO3 values above the guideline value of 50 mg/L. Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn and Zn showed appreciable levels in some wells but below their respective guideline limits. However, 88.63% and 83.33% of the hand dug wells and boreholes had Pb above the guideline values. The noncarcinogenic hazard index (HI) for adult and children with respect to Fe and Mn were >1 signifying some level of risk. However, the carcinogenic HI with respect to Pb was <1. A sensitivity analysis for the oral and dermal exposure pathways for both adults and children indicated that the most important factors in the risk model are (1) the amount of the heavy metal consumed, (2) exposure duration and (3) ingestion rate. To protect public health, reduction of the metal’s concentrations, proper construction of the wells to prevent runoff seepage and comprehensive water quality monitoring program are recommended to avoid/minimise the potential risk to the population.
1. Introduction Groundwater is and will keep being a significant source of potable water for most rural and urban dwellers globally. In Sub-Saharan Africa, access to potable water supply is still a major challenge and groundwater drawn from boreholes and protected hand dug wells are the main improved water sources in most rural and peri-urban settlements (Foster et al., 2006). In Ghana, 62% of urban and 71% of rural communities depend on groundwater as their essential source of drinking water (Obiri-Danso et al., 2009). Boreholes and hand-dug wells have become a
major source of water for households and modern industrial use, particularly in territories and spots that do not get water from the local water supply company, Ghana Water Company Limited (GWCL). In view of the recognized well-being and economic effects associated with drinking contaminated water, the quality of groundwater meant for domestic and industrial consumption must be constantly monitored for sustainable groundwater security and management strategies (Sathees et al., 2013). Heavy metals are naturally occurring elements, and are present in varying concentrations in all ecosystems. They are found in elemental
* Corresponding author. Regional Water and Environmental Sanitation Centre, Kwame Nkrumah University of Science and Technology Kumasi, KNUST, Kumasi, Ghana. E-mail address: [email protected] (P.A. Opoku). https://doi.org/10.1016/j.gsd.2019.100327 Received 19 November 2018; Received in revised form 16 April 2019; Accepted 23 December 2019 Available online 3 January 2020 2352-801X/© 2020 Elsevier B.V. All rights reserved.
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forms and in a variety of chemical compounds. Human activities have drastically changed the biochemical cycle and balance of some heavy metals. According to Nriagu and Pacyna (1995) and Alaee et al., 2003, between 1850 and 1990, the productions of Pb, Cu and Zn have increased tenfold as a result of human activities. The main anthropo genic sources of heavy metals are mining and industrial activities like foundries, smelters, combustion of fossil fuel and gasoline, and waste incinerators. In recent times, trace or heavy metal contamination in groundwater has been a major source of concern on local, regional, and global scales. Based on their concentration levels and toxicity, they can cause serious health and environmental hazards. This concern is highlighted in several scientific studies on As contamination of groundwater in Bangladesh (Alam et al., 2003) and Cd, Pb, Cu, and Zn contamination of ground water and vegetables in Europe, Asia and Africa (Hadzi et al., 2018; Miller et al., 2004; Zietz et al., 2003; Anawar et al., 2002) with its related human health issues. Anthropogenic activities like improper disposal of waste, have been found culpable for the elevation of heavy metal con centrations in groundwater (Saleem et al., 2015). However, heavy metals are also found naturally in the earth’s crust. Exposure to high concentrations of heavy metals like Fe and Mn in drinking water usually imparts aesthetic mutations to some consumers while metals such as As, Cr, Pb can cause serious health problems including diarrhoea, derma titis, nausea, liver and kidney damage, as well as respiratory problems (Hadzi et al., 2018). To reduce the impact of heavy metals on human health and the environment, there has been a significant development of cost-effective technologies for removing heavy metals in drinking water and waste water such as Zn (Senthil Kumar et al., 2012), Pb (Senthil Kumar et al., 2013) and Cr (Yaashikaa et al., 2019). Femina Carolin et al., 2017 provides an extensive literature review on heavy metal removal technologies in drinking water and waste water. Atonsu is one of the major peri-urban territories in the Kumasi Metropolis in the Ashanti Region of Ghana (Abdul-Razak, 2012). The territory is part of the wetland zone within the Ashanti Region. As with all wetland areas, the water table here is very shallow and groundwater can be assessed few meters below the surface (Abdul-Razak, 2012). The majority of the masses living in this zone depend on hand-dug wells, springs and boreholes. These sources of water are relatively affordable when contrasted with penetrated wells which are sunk to more profound depths. The proliferation of hand dug wells is expected due to the deficient and irregular supply of water from GWCL. The inhabitants of Atonsu do not fetch water directly from the boreholes or hand dug wells but rather pump the water into their houses through a pipe distribution system. There is limited literature on heavy metal contamination in the Atonsu area. However, several studies have been carried out in other parts of Ghana which have indicated increased concentrations of heavy metals and other contaminants in groundwater, surface water and sed iments in peri urban areas (Cobbina et al., 2013; Obiri, 2007; Obiri et al., 2010; Armah et al., 2011). The extent and degree of heavy metal contamination reported in different parts of the country vary depending on geology and the anthropogenic activities; mining, industry, improper waste disposal, and heavy vehicular traffic occurring in the area. Despite the existence of several data on trace metal contamination in Ghana, most of the studies focused on their compliance with WHO (2004) acceptable limits without addressing the potential human health risk to children, teens and adults who are exposed to these metals via drinking water using comprehensive risk assessment models. Health risk assessment models provide a systematic form of quanti tative and semi-quantitative descriptions of the potential human or environmental health effect attributed to exposure to contaminants in drinking water. The conventional approach is usually based on a single or deterministic value of the input parameters; concentrations of the contaminant, ingestion rate, body weight, contact frequency and dura tion parameters, to compute the risk. The approach provides a numeral value for average scenarios of the risk; it may over or under estimate the actual risk because uncertainty factors are not considered (Lonati and
Zanoni, 2012). The probabilistic approach using the Monte Carlo simulation provides a more realistic risk assessment that accounts for different kinds of uncertainties, sensitivity to different exposure path ways and the effects of various intervention scenarios (Fallahzadeh et al., 2018; Augustsson and Berger, 2014; Morisset et al., 2013). The probabilistic approach using the Monte Carlo simulation regards all the input parameters for calculating the risk as distributions or random variables to obtain a wide range of outcomes, a risk or hazard quotient (HQ), after repeated simulations usually 10,000 or more (Zhang et al., 2017; Yousefi et al., 2018). The final outcome of a given risk is obtained in a form of a probability distribution. This method has been used in various studies to assess the risk of pollutants in drinking water (Zhang et al., 2017; Fallahzadeh et al., 2018; Lonati et al., 2007). The main purpose of the study was to assess the spatial distribution of heavy metal contamination in drinking water and their potential human health risk (oral ingestion and dermal exposure) within the Atonsu community through a quantitative health risk assessment model. The sensitivity and uncertainty analysis due to exposure to heavy metals were also evaluated. The results of the water quality analysis were buttressed by collecting data on cases of water-borne diseases (diar rhoeal diseases, suspected cholera and typhoid fever) within the com munity from the Kumasi South Hospital, The Hart Adventist Hospital and Christ the King Hospital. The outcome and findings of this study can serve as a baseline data and provide information on human health risk associated with the consumption of heavy metal contaminated drinking water. 2. Materials and methods 2.1. Study area Atonsu is a peri-urban community in the Kumasi metropolis in the Ashanti Region of Ghana, Africa. The study area is located between 6� 370 4000 N and 6� 400 000 N and 1� 340 2000 W and 1� 370 000 W as shown in Fig. 1. It has a population of about 8790. The area lies in the transitional forest zone; specifically within the moist semi-deciduous south-east ecological zone and is characterised by a wet sub-equatorial climate (Ghana Statistical Service, 2014). The Kumasi metropolis lies within the plateau of the south-west physical region which ranges from 250 to 300 m above sea level. The topography is undulating. The area lies within the Birimian Super-group which is a major part of the Paleo-proterozoic terrain of Ghana underlain, by supra-crustal and intrusive rocks which display a characteristic pattern of NE-trending, parallel, approximately evenly-spaced volcanic belts called Volcano-Plutonic Group and inter vening sedimentary basins called the Sedimentary-Volcano-Sedimen tary Group (Geological Map of Ghana, 2009). The metropolis is tra versed by a major river (Owabi) and streams like Subin, Wiwi, Sisai, Aboabo and Nsuben (Ghana Statistical Service, 2014). 2.2. Water sample collection, preparation and treatment Water samples were collected from fifty-six (56) point sources comprising twelve (12) mechanized boreholes (MBH) and forty-four (44) hand-dug wells (HDW) as shown in Fig. 1. 500 ml of each sample was collected into plastic bottles prewashed with a 1:1 concentrated nitric acid and distilled water solution and eventually rinsed with only distilled water. Sample bottles were labeled with unique codes and date of collection for easy identification. The samples were collected in du plicates after every fifth sample. Sample bottles were acidified to pH < 2 with concentrated HNO3 before being transported to the laboratory. The container was rinsed three times with water from the well of interest before samples were collected from each point source. In-situ mea surement of pH, electrical conductivity (EC), total dissolved solids (TDS), temperature, salinity and the geographical coordinates were done. All water samples were then kept in an ice chest containing ice bricks at a temperature below 5 � C and transported to the Ghana Atomic 2
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Fig. 1. Map of the study area showing sampling sites.
Energy Commission (GAEC) laboratory. At the laboratory, the samples were kept in a refrigerator at 4 � C prior to analysis (Naveedullah et al., 2014).
were acid digested and subsequently analysed for the heavy metals of interest through the same procedure (Brady et al., 2014). The samples were further analysed in triplicates and after every 5 samples, a cali bration standard was used to check the response and efficiency of the instrument alongside the blank which was constantly used to check for contamination. Reproducibility and recovery tests were conducted using a Multi Element Reference Standard (Standard Agilent Technologies Multi Element Reference Standard 2A) as the unknown. The percentage recovery obtained for the standard ranged from 99.2 to 105.8%. This suggests that, the error linked with the analysis is negligible; hence, the results are reliable.
2.3. Digestion, analysis of water samples and quality control Acid digestion of the samples was performed using a commercial high-pressure laboratory microwave oven (Milestone Ethos 1600 Mi crowave Lab station, Sorisole, Italy). This equipment has ten 100 mL tetrafluoro methoxy (TFM) tubes operating at a frequency of 50 Hz, with an energy input of 2200 W. Analytical grade chemicals and reagents were used for digesting the samples; HCl (35% m/v), HNO3 (69% m/v) and HClO4 (71–73% m/v). High purity deionised water (resistivity 18.2 MΩ cm) was used as blank, dilution and preparation of solutions where required. The trace metals were analysed using a Fast-sequential Atomic Ab sorption Spectrometer (Varian AA240FS). The equipment was first calibrated with standards, and the lamps optimized then the water samples were aspirated through the capillary tube of the Atomic Ab sorption Spectrometer (AAS) and the concentrations and absorbance of heavy metals were displayed on the monitor. To underscore the reli ability of the analytical method, quality control (QC) and blank samples
2.4. Health risk assessment and risk characterization The major routes for heavy metal entry into the human body are (1) direct ingestion of substrate particles (Ding), (2) inhalation of suspended particles through the mouth and nose (Dinh), and (3) dermal absorption of elements in particles stuck to exposed skin (Dder) (De Miguel et al., 2007). For this research, health risk assessment of heavy metals in drinking water were estimated using the oral ingestion and dermal absorption scenarios for receivers (adults and children) based on the USEPA, (2002) 3
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risk assessment methodology. Inhalation as an entry way was excluded from the assessment because of unavailable toxicological data such as inhalation reference dose, and transfer efficiency from water to air in either database or published literature (Zhang et al., 2017).
2.7. Monte Carlo simulation and sensitivity analysis Health risk assessments are usually accompanied with high un certainties especially when single point values are used to estimate risk for a given population. To cater for uncertainty and variability analysis, input values were described with probabilistic distributions and subject it through a Monte-Carlo Simulation with hypercube sampling method in 100,000 iterations. This process was applied in order to separate uncertainty from variability to minimise their influences on the risk assessment estimates. Priorities were given to the values obtained from the study area obtained from the questionnaires administered. The sensitivity analysis was also performed to identify the input parameters which have significant impact on the model output. In this study, the Monte-Carlo Simulation and sensitivity analysis were done in Microsoft excel with in-built @ Risk software (Palisade corporation, version 7.4). All input parameters are shown in Table 3.
2.5. Exposure models The questionnaires administered helped to highlight the parameters needed for the risk assessment of the heavy metals in the water. It also gathered information regarding the treatment, usage and general quality of the water. The exposure doses in water through ingestion and dermal contact was computed using equations (1) and (2) (Naveedullah et al., 2014). CDIing
w
¼
Cw � IRw � EF � ED BW � AT
(1)
CDIder
w
¼
Cw � SA � Kp � ETs � EF � ED � CF BW � AT
(2)
2.8. Data and statistical analysis
CDIing w and CDIder w are the chronic daily intake through ingestion and dermal contact of water respectively in mg/kg/day. Cw is the concen tration of the heavy metal in the water sample in mg/L, IRw is the ingestion rate of the water in L/day, EF is the exposure frequency in days/year, ED is the exposure duration in years, BW is the body weight in kg, AT is the average time of non-carcinogenic and carcinogenic risk in days, CF is the unit conversion factor for water in 1L/1000 cm3, Kp is the dermal permeability coefficient in cm/hr, SA is exposed skin area in cm3 and ETs is the exposure time in hr/day.
A total of 150 questionnaires were administered and 145 re spondents completed their questionnaires, giving a response success rate of 96.67%. Some additional data, needed for some of the input param eters were obtained from nearby hospitals after obtaining ethical clearance from the Committee on Human Research, Publications and Ethics (CHRPE), School of Medical Sciences, KNUST, Kumasi. The @Risk 7.5 software with add-on to Excel was constructed in Microsoft Excel and used to estimate the probability of health risk and sensitivity anal ysis. The software subjected the input data to the Monte-Carlo simula tion of 100,000 iterations with hypercube sampling for probability of health risk and sensitivity analysis in order to account for the variability and uncertainty in the model parameters.
2.6. Hazard quotient (HQ) and hazard index (HI) The health risk was characterised using the hazard quotient (HQ) and hazard index (HI) due to exposure to toxicants (WHO, 1996; USEPA, 2004). These were calculated using the equations (3)–(6). For non-carcinogens: HQ ¼
CDI Ref D
3.1. Groundwater chemistry Table 4 presents a statistical overview of the hydro-chemical data of HDW and MBH in the study area. The HDW had pH being slightly acidic to slightly basic and ranging from 5.31 to 8.10 while the MBH pH ranged from 5.58 to 7.4. The chemical composition of the HDW and MBH varies over a wide range both in salinity and ionic composition but lowly mineralized with TDS< 600 mg/L and <100 mg/L respectively (Table 4). The EC and TDS of the HDW ranged from 92 μS/cm to 1065 μS/cm with an average of 371 μS/cm and 51 mg/l to 586 mg/l with an average of 204 mg/l respectively. All water samples are relatively fresh with TDS « 1000 mg/L. Salinity of the HDW range from 0.00 ppt to 0.50 ppt with an average of 0.10 ppt while all the MBH recorded salinity of 0 ppt. Fig. 2 shows spatial distribution of EC and TDS in both MBH and HDW. TDS is a measure of the total ionic concentrations of dissolved minerals in water. Natural groundwater contains mineral ions which dissolve slowly in soil particles, sediments, and rocks as the water moves along mineral surfaces in the pores or fractured zones within the aquifer. Other dissolved solids are from precipitation water or river water that recharges the aquifer. Others are also influenced by human activities or other forms of pollution which can affect the concentrations of TDS in
(3)
n X
HI ¼
3. Results and discussion
(4)
HQi i¼1
The variable RefD represent the reference dose in a specific pathway in mg/kg/d. The reference doses for non-carcinogenic heavy metals are listed in Table 1. For carcinogens: The risk posed by individual carcinogenic metals was computed as the product of the slope factor (SF) and chronic daily intake (CDI). The total cancer risk was calculated by adding the risk associated with in dividual carcinogenic heavy metals. (5)
Risk ¼ SFi � CDIi n X
(6)
SFi � CDIi
Rtotal ¼ i¼1
The classification based on Man et al. (2010) and Fryer et al. (2006) were used to assess the level of risk on the health of humans (Table 2).
Table 2 Risk assessment classification.
Table 1 Metals and their reference doses used for risk assessment. Metal
RefDing (mg/kg/d)
RefDderm (mg/kg/d)
Reference
Pb Zn Fe Mn
0.0014 0.3000 0.7000 0.0240
0.00042 0.06000 0.14000 0.00096
Huang et al. (2015) Huang et al. (2015) Naveedullah (2014) Naveedullah (2014)
4
Index
Classification
Reference
HI < 1
No obvious health risk
Man et al. (2010)
^ Rt > 1:0*104
Unacceptable, poses health risk
Fryer et al. (2006)
^ Rt < 1:0*106
No significant health risks
WHO (2011)
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Table 3 Input parameters used in risk assessment model. Input Parameters
Symbol
Distribution type for the simulation
Values Children
Adults
Concentration of heavy metal in water Chronic daily intake through ingestion
Cw CDIdermal
– –
Measured calculated
Measured calculated
Chronic daily intake through dermal absorption
CDIingestion
–
calculated
calculated
Ingestion Rate Exposure frequency Exposure Duration Exposure Time Body Weight Averaging Time Skin Surface Area Dermal permeability factor
IngR EF ED ETs BW AT SA CF
Normal Triangular Fixed value lognormal Lognormal Fixed value Lognormal Fixed value
1.7 � 0.4a (180; 345; 365) 18 0.13 � 0.0085a 50.12 � 1.45a 2190 7422 � 1.25a 1 � 10 3
3.3 � 1.00a (180; 345; 365) 50 0.13 � 0.0085a 63.97 � 1.65a 18250 18182 � 1.10a 1 � 10 3
Unit
References
mg/L mg/kg/ day mg/kg/ day L/day days/year years h/day kg days cm2 L/cm3
This Study This Study US EPA (2004) This Study This Study Zhang et al. (2017) US EPA (2004) US EPA (2004)
Values in the bracket means (Min; Max; Mean). a Represents mean � SD. Table 4 Comparison of borehole and hand-dug well water quality results with WHO guideline value. Parameter
WL Depth pH EC TDS Sal Temp Naþ Kþ Ca2þ Mg2þ TH HCO3 F Cl NO3 PO34 SO24 2þ Pb Co Cr Fe Mn Zn Cu Cd
Hand dug wells, HDW (N ¼ 44)
Mechanized Boreholes, MBH (N ¼ 12)
WHO Guide Value
% Outside WHO Guide Value (HDW)
% Outside WHO Guide Value (MBH)
unit
Mean
Min.
Max.
Mean
Min
Max
m m
1.57 2.75 6.54 371.39
0.02 0.27 5.31 92.00
6.55 8.07 8.10 1065.00
– – 6.77 116.73
– – 5.58 47.80
– – 7.47 250.00
– – 6.5–8.5 –
– – 47.73 –
45.46 0
204.45 0.10 28.58 38.97 2.97 13.84 0.20 110.55 65.70 0.02 28.72 26.53 0.40 6.50 0.022 – – 0.047 – – – –
51.00 0.00 26.90 6.80 0.50 3.20 0.01 36.00 10.00 0.00 3.11 0.00 0.00 0.00 <0.001 <0.005 <0.006 <0.006 <0.002 <0.001 <0.003 <0.002
586.00 0.50 29.90 70.30 8.71 65.40 0.58 328.00 170.00 0.18 85.39 87.23 7.96 26.43 0.04 <0.005 <0.006 0.20 0.11 0.01 <0.003 <0.002
62.31 0.00 30.63 12.74 1.90 5.40 0.06 65.67 37.33 0.01 5.49 5.14 0.22 1.17 0.01 – – – – – – –
14.71 0.00 28.1 3.60 0.21 1.60 0.01 48.00 10.00 0.00 0.77 0.00 0.00 0.00 <0.001 <0.005 <0.006 <0.006 <0.002 <0.001 <0.003 <0.002
137.00 0.00 34.50 21.00 4.57 12.80 0.34 180.00 70.00 0.01 11.55 22.00 0.85 6.25 0.02 <0.005 <0.006 0.0095 0.006 0.003 <0.003 <0.002
– – – 200 30 200 150 500 – 1.5 250 50 – 500 0.01 – 0.05 0.30 0.40 3.0 2 0.003
– – – 0 0 0 0 0 – 0 0 20.50 – 0 88.63 – 0 0 0 0 0 0
– – – 0 0 0 – 0 – 0 0 0 – 0 83.33 – 0 0 0 0 0 –
μS/
cm mg/L Ppt (� C) mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
WL ¼ water level; EC ¼ Electrical conductivity; TDS ¼ Total Dissolved Solids; Sal ¼ salinity; Tempe ¼ temperature; TH ¼ total hardness.
water samples (DANR and NRSC, 2003). The observed low EC, TDS and salinity of the MBH was due to the fact that, all the MBH were fitted with desalinization systems for private home use. The dominant cation for both HDW and MBH is Naþ with mean concentration of 38.97 mg/L and 12.74 mg/L respectively. This is fol lowed by Ca2þ with mean concentration of 13.84 mg/L and 5.40 mg/L and then by Kþ and Mg2þ, which have mean concentrations of 2.97 and 5.40 and 0.20 and 0.06 mg/L, respectively. However, all the cations are below their respective guideline WHO and GSA guideline values. The cation dominance is in the order of Ca2þ
L (WHO, 2004). This could be due to the shallow nature of the HDW and poor construction (without concrete lining). This allows for contami nation from sources such as waste from livestock operations, septic tank or drained field effluent and crop fertilizers. 3.2. Concentrations of the heavy metals The results of the heavy metal concentrations in borehole and the hand dug wells are presented in Table 4. The mean concentrations of the heavy metals are generally low when compared with the various guideline values (Table 4). Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn, Zn and Fe showed appreciable levels in some wells, however, all the values were below their respective guideline values. Pb recorded the highest concentrations in both the hand dug wells and the mechanized boreholes ranging from 5
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Fig. 2. Spatial Distribution of TDS in samples from boreholes and hand-dug wells found within the study area.
<0.001 to 0.04 mg/L and <0.001–0.02 mg/l with mean values of 0.02 mg/l and 0.01 mg/l respectively. The primary sources of lead in most drinking water sources is the piping system, dust/soil containing lead and from anthropogenic activities (metal scrap dealers, blacksmith, land fill sites and improper disposal of waste). There is no piping system in the area and all the samples were taken from open hand dug wells with poor sanitary conditions and drainage system, hence, the likely source of the Pb could be from the atmospheric dust, soil and run off from metal scrap dealers, blacksmith and land fill sites. Fig. 3 shows the spatial distribution of Zn, Pb, Fe and Mn in the study area.
CDI though ingestion and dermal exposure pathways for carcinogenic and non-carcinogenic metals for adults and children. The heavy metals CDI varied widely among the different categories of population under study as well as for the scenarios. From Table 5, the amount of CDI of heavy metals through ingestion is much higher than the amount of heavy metals taken through dermal contact. Considering the mean values for the CDI, the dermal exposure pathway recorded generally a three or more orders of magnitude lower than the oral ingestion for both adults and children in the study area (Table 5). Therefore, ingestion is considered to be the primary exposure route of heavy metal concen trations within the study area for both adult and children. Based on the USEPA (2004), hazard quotient (HQ) values greater than 1 for any toxicant of interest can pose a health risk to consumers and measures must be put in place to reduce the concentrations. From Table 5, the HQ values for dermal route exposure were below unity for both adults and children for Fe, Mn and Zn. This indicates that there is minimal risk to human health when contact with the water is through the skin. However, HQ for oral route exposure was greater than 1 for Fe and Mn in both adults and children. Given that the HQ value is higher than 1 for adults and children, the health of this group is highly at risk. Table 5 shows the hazard index (HI), a summation of all the HQs (HQing þ HQderm) for the individual metals. HI for adult has Fe and Mn having values greater than 1; that is 1.351and 6.484 respectively whilst Zn had a value of 0.362 which is within the acceptable limits. On the other hand, HI for children had Mn and Fe greater than 1 with values of 6.274 and 1.306 respectively. The degree of risk posed is in the order of Mn > Fe > Zn for non-carcinogenic risk for both adult and children.
3.3. Human health risk assessment 150 questionnaires were administered in the study area. The ques tionnaire was structured into five sections; personal data (age, weight, height), bio data of water (source, consumption and storage), water quality (treatment and maintenance, health risk), demographic and socio-economic data. Table 5 shows the input parameters used for the risk assessment model obtained from the questionnaires. The exposure frequency was assumed to be 365 days/year since all the respondents had not travelled out of the zone during holidays and were always available. Using the exposure frequency of 365 days/year also helped to estimate the worstcase scenario risk. In the current study, both carcinogenic and non-carcinogenic health risks were used to assess the human health risks due to exposure to heavy metals through ingestion and dermal contact. Table 5 shows the 6
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Fig. 3. Spatial Distribution of heavy metal concentrations found within the study area. Table 5 CDI and HQ for oral and dermal exposure pathways. Non-carcinogenic
Adult Children
Carcinogenic
Adult Children
Metal
CDI Oral
CDI Dermal
HQ Oral
HQ Dermal
H1
Fe Mn Zn Fe Mn Zn Pb Pb
0.9419 0.1530 0.1082 0.9115 0.1480 0.1047 0.0013 0.0012
6.58E-04 1.07E-04 4.54E-05 6.26E-04 1.02E-04 4.31E-05 3.53E-06 3.36E-06
1.346 6.373 0.361 1.302 6.168 0.349 0.903 0.874
0.005 0.111 0.001 0.004 0.106 0.001 0.008 0.008
1.35 6.48 0.36 1.31 6.27 0.35 0.91 0.88
Since the communities depend on these water sources for drinking, reducing and controlling the concentration of Fe and Mn is necessary to minimise the potential health implication of consuming excess Mn and
Fe such as stomach upset, nausea, vomiting, diarrhoea, irregular heartbeat and low blood pressure. At the moment, the most common and cheap ways to remove heavy metals from drinking water include 7
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activated carbon and ion exchange (Fu and Wang, 2011). In the case of carcinogenic health risk using Pb, all the samples for both adults and children had HQ < 1 indicating no potential health risk. HI for carcinogenic risk for adult and children were all below unity and this means their concentrations found in the water do not pose any health risk to consumers with values of 0.911 and 0.882 respectively.
The sensitivity analysis for adult and children through oral exposure indicates that risk is very sensitive and has a strong to very strong pos itive correlation to exposure duration and concentration of the metals itself (Fig. 4). The sensitivity analysis for contact through dermal exposure for both adults and children also show a strong positive cor relation to exposure duration and concentration of the metals. Ingestion rate also showed a positive but weak correlation to exposure in both adults and children. Body weight however, showed an inverse and weak correlation to risk. This means that, the weight of an individual has no direct influence on the concentration of metals the body can accumulate and therefore does not influence risk. The sensitivity analysis result indicates that, the Cw and IR variables for oral ingestion can be used to decrease the risk of ingesting these heavy metals in the study area. Thus, reducing the heavy metal con centration in the drinking water, exposure duration and ingestion rate can help mitigate the non-carcinogenic risk for the studied population.
3.4. Sensitivity analysis The Spearman’s correlation coefficient was used to analyse the sensitivity of the parameters used in the risk assessment. Fig. 4 shows the relationship between body weight, exposure duration, ingestion rate and the concentrations of the heavy metals. The sensitivity analysis was carried out to easily identify the relevant variables contributing to the risk estimates in the model for both adults and children. The MonteCarlo simulation was used to help minimise the uncertainty of calcula tions in the risk assessment. Figures obtained directly from the field were given the highest priority whenever appropriate, to reduce the assess ment uncertainty caused by distribution factors like water ingestion rate, body weight, concentration of metal in water, exposure duration, exposure frequency and exposure time.
3.5. Assumptions, uncertainty and limitations associated with the model Several studies have established an exposure-response relationship between the level/duration of exposure to heavy metals and the
Fig. 4. Sensitivity analysis for oral and dermal exposure to heavy metal contaminants. 8
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occurrence of heavy metals poisoning (Anawar et al., 2002; Obiri et al., 2010). However, the quantitative relationship between intake and risk is still not confirmed due to uncertainties associated with the prevalence of heavy metal poisoning and drinking of heavy metals contaminated water. The Monte Carlo Simulations greatly enhanced the health risk assessment due to the quantification of the uncertainties. However, the sensitivity analysis showed that, other uncertainties could still not be accounted for in some of the input parameters of the model that affected the risk assessment. For examples, the amount of the heavy metal consumed by the individual is an important factor in the risk estimate. In our estimation, we assume that, heavy metal in drinking water is the only source of daily heavy metal intake. However, several studies have proved that, heavy metals vegetables, cereals, root crops, dust particles, soil etc can pose significant health risk. Hence, our assumptions could lead to uncertainties of heavy metals exposure and underestimation of the risk. Therefore, future studies on risk assessment should consider taking more data from each exposure pathways to reduce the uncer tainty in the risk estimate.
model are the amount of the heavy metal consumed, exposure duration and ingestion rate. The current study therefore provides further insights on heavy metal contamination in drinking water and its potential effect on human health at Atonsu. It is therefore, imperative to take measures to reduce the heavy metals in some of the wells, properly construct the wells to avoid run off seepage and also implement a comprehensive monitoring of the water quality to avoid the potential risk to the population. Acknowledgments The research was funded by the Regional Water and Environmental Sanitation Centre (RWESCK), Civil Engineering Department, Kwame Nkrumah University Science and Technology (KNUST), Kumasi with funding from Ghana Government through the World Bank under the African Centre’s of Excellence project. The views expressed in this paper do not reflect those of the world bank, Ghana Government and KNUST. A special thanks goes to all research scientists, technicians and labora tory assistants of the Ghana Atomic Energy Commission (GAEC) for the technical support. The author wishes to thank editor and the anonymous reviewers for their constructive comments and suggestions.
3.6. Recommendations for groundwater management, prevention and control of heavy metals contamination Based on the field observations and analysis carried out, we recom mend the following for the prevention and control of the potential risk of heavy metals contaminations.
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� Boreholes and hand wells with high metals concentrations should be treated before drinking � There should be a comprehensive audit and water quality assessment of all drinking water sources in the area (chemical, bacteriological and heavy metal). � Future studies on risk assessment should include inhalation exposure to improve on the estimate. � The hand dug wells should be properly lined and the onsite sanita tions around the wells should be taken seriously to prevent runoff entering the wells. � The data generated from project implementing agencies and NGOs together with all other available data should be made available to the district assemblies to guide future groundwater management strategies. � There should be a strong advocacy, health education and public awareness on the effect of heavy metals contamination in drinking water on human health. 4. Conclusion The concentration, carcinogenic and non-carcinogenic health risk of Cr, Cd, Fe, Zn, Pb, Cu, Zn and Mn in drinking water (12 boreholes and 44 hand dug wells) in Atonsu, a suburb of Kumasi, Ghana is presented in this study. The results showed that, the chemical composition of the groundwater samples varies over a wide range both in salinity and ionic composition but lowly mineralized with TDS « 1000 mg/L. All the measured parameters were below their respective guideline values. However, high NO3 concentrations were recorded in most of the HDW with about 21% above the guideline value of 50 mg/L. Co, Cr, Cu and Cd were below the detection limit of the instrument used for the analysis. Mn and Zn showed appreciable levels in some wells but below their respective guideline limits. However, 88.63% and 83.33% of the hand dug wells and boreholes had Pb above the guideline values respectively. Although other sources could be accounting for the presence of the heavy metals in water, anthropogenic activities are suspected to be the principal contributor. The non-carcinogenic hazard index (HI) for adult and children with respect to Fe and Mn were greater than 1 indicating some level of risk. However, the carcinogenic HI with respect to Pb is < 1. A sensitivity analysis for the oral and dermal exposure pathways for both adults and children indicated that the most important factors in risk 9
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Lonati, G., Zanoni, F., 2012. Probabilistic health risk assessment of carcinogenic emissions from a MSW gasification plant. Environ. Int. 44, 80e91. Huang, Y., Li, T., Wu, C., He, Z., Japenga, J., Deng, M., Yang, X., 2015. An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils. Journal of Hazardous Materials 299, 540–549. Lonati, G., Cernuschi, S., Giugliano, M., Grosso, M., 2007. Health risk analysis of PCDD/F emissions from MSW incineration: comparison of probabilistic and deterministic approaches. Chemosphere 67 (9). S334eS43. Man, Y.B., Sun, X.L., Zhao, Y.G., 2010. Health risk assessment of abandoned agricultural soils based on heavy metal contents in Hong Kong, the world’s most populated city. Environ. Int. (36), 570–576. Miller, J.R., Hudson-Edwards, K.A., Lechler, P.J., Preston, D., Macklin, M.G., 2004. Heavy metal contamination of water, soil and produce within riverine communities of the Rıo Pilcomayo basin, Bolivia. Sci. Total Environ. 320 (2), 189–209. Morisset, T., Ramirez-Martinez, A., Wesololek, N., 2013. Probabilistic Mercury Multimedia Exposure Assessment in Small Children and Risk Assessment. Published in journal of Environment International by Elsevier Limited. https://doi.org/ 10.1016/j.envint. Naveedullah, M.Z.H., Yu, C., Shen, H., Duan, D., Shen, C., Lou, L., Chen, Y., 2014. Concentrations and human health risk assessment of selected heavy metals in surface water of the siling reservoir watershed in Zhejiang Province, China. Pol. J. Environ. Stud. 23 (3), 801–811. Nriagu, J.O., Pacyna, J.M., 1995. Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature 333 (6169), 134–139. Obiri, S., 2007. Determination of heavy metals in water from boreholes in dumasi in the wassa west district of western region of republic of Ghana. Environ. Monit. Assess. 130 (1–3), 455–463. Obiri, S., Dodoo, D.K., Essumang, D.K., Armah, F.A., 2010. Cancer and non-cancer risk assessment from exposure to arsenic, copper, and cadmium in borehole, tap, and surface water in the Obuasi municipality, Ghana. Hum. Ecol. Risk Assess. 16 (3), 651–665. Obiri-Danso, K., Adjei, B., Stanly, K.N., Jones, K., 2009. Microbiological quality and metal levels in wells and boreholes water in some peri- urban communities in Kumasi Ghana. Afr. J. Environ. Sci. Technol. 59–66. Saleem, M., Iqbal, J., Shah, M.H., 2015. Geochemical speciation, anthropogenic contamination, risk assessment and source identification of selected metals in
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