Nitrate contamination of groundwater in the Lower Volta River Basin of Ghana: Sources and related human health risks

Ecotoxicology and Environmental Safety 191 (2020) 110227

Contents lists available at ScienceDirect

Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Nitrate contamination of groundwater in the Lower Volta River Basin of Ghana: Sources and related human health risks

T

Courage D. Egbia,b, Geophrey K. Anornua, Samuel Y. Ganyaglob,f,∗, Emmanuel K. Appiah-Adjeia,c, Si-Liang Lid,e, Samuel B. Damparef a

Regional Water and Environmental Sanitation Center, Dept of Civil Engineering, Kwame Nkrumah University of Science and Technology, Private Mail Bag, University Post Office, Kumasi, Ghana b National Isotope Hydrology Lab, National Nuclear Research Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon-Accra, Ghana c Geological Engineering Department, Kwame Nkrumah University of Science and Technology, Private Mail Bag, University Post Office, Kumasi, Ghana d The State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences Guiyang, 550002, China e Institute of Surface-Earth System Science, Tianjin University, China f Graduate School of Nuclear and Allied Sciences, University of Ghana, P. O. Box LG 80, Legon-Accra, Ghana

A R T I C LE I N FO

A B S T R A C T

Keywords: Groundwater hydrochemistry Nitrate contamination Bayesian model Health risk assessment Management options

A significant population within the Lower Volta River Basin of Ghana relies solely on untreated groundwater (GW) and surface water (SW) for various purposes. However, negative practices associated with increasing human activities pose threats to particularly GW quality in the basin. Using NO3− as a proxy, this study mainly focused on the status of GW contamination, origins of NO3− and potential human health risks through integrated hydrochemistry, correlation analysis, isotopes (15N, δ18O), Bayesian and USEPA human health risk models. Slightly acidic to alkaline GW and SW environments were observed. Electrical conductivity (EC) values above 1000 μS/cm were recorded in 45% of the GW with a maximum of 19370 μS/cm. NO3− in GW ranged from 0.12 to 733 mg/L with average 59.6 mg/L and positively correlated with K+, Ca2+, Mg2+, Cl−, Na+ and EC. In SW, a maximum of 5.3 mg/L of NO3− was observed. Largely, 75% of the GW exceeded local background NO3− value of 2.1 mg/L, while 35% were above the WHO recommended value of 50 mg/L. Bivariate and correlation relationships elucidated human contributions to sources of NO3−, Cl−, SO42− and K+ to GW in the basin. From NO3−/Cl− ratio, 43% of the GW and 21% of SW were affected by effluents and agrochemicals. Values for δ15N–NO3- and δ18O–NO3- ranged from +4.2‰ to +27.5‰ and +4.5‰ to +19.9‰ for GW, and from +3.8‰ to +14.0‰ and +10.7‰ to +25.2‰ for SW. Manure, septic effluents and mineralized fertilizers are sources of NO3− contamination of water in the basin. The Bayesian model apportioned 80% of GW NO3− contamination to sewage/manure. Hazard index indicated 70%, 50% and 48% medium to high-risk levels for infants, children and adults respectively, with 79% high-risk of SW NO2− contamination to infants. Immediate measures for GW and SW quality protection are recommended.

1. Introduction GW is the most accessible and reliable source of potable water across Sub–Saharan Africa. Though abundant, studies (MacDonald and Calow, 2009; Affum et al., 2015; Liddle et al., 2015; Ouedraogo et al., 2016) indicate that, human activities are compromising the quality of GW with accompanying public health conditions (Dorice et al., 2010; Baldursson and Karanis, 2011). Globally, a key pollutant of GW is nitrate, NO3− (Xue et al., 2009; Wu and Sun, 2016; He and Wu, 2018). The concentration of NO3− in water greater than 3 mg/L has been

ascribed to various sources of pollution including onsite sanitation, agricultural activities and waste disposal (Bordeleau et al., 2008; Kendall and Doctor, 2003; Burow et al., 2013; Pastén-Zapata et al., 2014; He et al., 2018). To assess decline in water quality from NO3− contamination, many countries including Ghana adopt the WHO reference of 50 mg/L (WHO, 2017). Concentrations above this guideline have been reported in India (Ahada and Suthar, 2018), Sub–Saharan Africa (Ouedraogo and Vanclooster, 2016; Ako et al., 2014; Rakotondrabe et al., 2018) and China (Fu et al., 2007; Li et al., 2019a, 2019b). Also, the impact of NO3− on coastal aquifers of some West

∗ Corresponding author. National Isotope Hydrology Lab, National Nuclear Research Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon-Accra, Ghana E-mail addresses: [email protected], [email protected] (S.Y. Ganyaglo).

https://doi.org/10.1016/j.ecoenv.2020.110227 Received 23 May 2019; Received in revised form 27 December 2019; Accepted 15 January 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

Fig. 1. Map of study area and sampling locations and major towns. Both Tertiary and Dahomeyan (Precambrian) formations are located inland while Quaternary is of coastal zone of the basin.

providing ample information on NO3− pollution sources and potential health risks. A first comprehensive study on NO3− contamination was recently conducted in the north-western part of Ghana (Anornu et al., 2017). However, this study also fell short in the quantitative apportionment to sources of NO3− contamination. Currently, information across all basins in the country in this regard is still lacking. Hence, the need for intensive and holistic investigations at local, transboundary, national and transnational levels. GW resources in rapidly developing coastal basins, notably, the Lower Volta River Basin (LVRB) of Ghana are worth considering in such studies. Presently, a significant population of 43% within the LVRB rely solely on untreated GW, 23.6% on untreated SW and 33.4% rely on a combination of treated piped water, untreated GW and SW for various purposes (GSS, 2012). The importance of GW in the area is underscored by its accessibility and broader uses: domestic, crop irrigation, cattle watering, hospitality industries, health centres and mineral water production. GW is drawn manually or mechanically from hand-dug wells (3–30 m) with an average depth of 5 m constructed in homes or public places by the inhabitants at a relatively cheaper cost compared to boreholes (40–110 m). Another significant advantage of the hand-dug wells is their yield of freshwater compared to saline water from boreholes (Egbi et al., 2019). Currently, SW along the entire stretch of the basin is characterized by bilharzia infestation (Mul et al., 2015). Growing dependence on the SW for fishing, aquaculture, commercial irrigation and tourism recreation could further compromise its quality. According to Ofosu et al. (2017), due to rapidly decreasing SW quality in the area, GW which is readily available to over 81% of the

African countries due to urbanization has been documented (Akouvi et al., 2008; Re et al., 2011; Nlend et al., 2018). Elevated levels of NO3− in GW may entail co-contamination with potentially toxic elements; the reason why much research attention is devoted to identifying sources of NO3− contamination of water resources. Globally, some studies have compared NO3− concentrations from human-induced sources to background values (Hackley, 2007; Menció et al., 2011; Kim et al., 2015; Li et al., 2018). Comparative analysis of GW NO3− concentrations to both WHO and national guidelines have also been documented (Yidana et al., 2010; Singh et al., 2013; Rajendran and Mansiya, 2015; Udhayakumar et al., 2016). Other investigations focused on its occurrence and mobility trends including sources identification (Hosono et al., 2013; Li et al., 2013; Zhang et al., 2015; Ji et al., 2017; Matiatos, 2016; Re et al., 2017; Meghdadi and Javar, 2018). Health risk assessment of GW NO3− contamination has recently gained global research interest (Fabro et al., 2015; Zhai et al., 2017; Sadler et al., 2016). Notwithstanding, in Sub – Saharan African countries like Ghana, little information on such research exists. An earlier inventory of GW NO3− levels in Ghana (Akiti, 1982), conducted in the northern sector indicated rising levels (0–65 mg/L) between 1977 and 1980 compared to 0.4–10.6 mg/L between 1959 and 1977. Subsequently, Loh et al. (2012) and Yidana et al. (2012) reported values of 194.65 mg/L and 120 mg/L and suggested manure and sewage as possible sources. Likewise, Helstrup et al. (2007) noted 62 mg/L (NO3−) in the Keta Basin of southern Ghana and indicated poorly managed sewage systems as probable transmission sources. Yet, these studies were limited in 2

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

2.2. Sampling and laboratory analysis

inhabitants, could become their alternative water source. Nevertheless, lack of sewer systems and presence of unsanitary environments such as pit latrines and burial tombs between 4 and 10 m close to wellheads in some homes are of potential threats to the GW quality. The practice of pumping GW for irrigation may also accelerate land subsidence and contaminants influx. This study hence, investigated the origins of GW contamination in the LVRB of Ghana and its related health risks, employing hydrochemistry, correlation matrix, isotopes (15N, δ18O) Bayesian and USEPA human health risk models. The study would provide information on NO3− contamination of water resources in the basin that can be relied on by mandated authorities to augment strategic water pollution control measures.

Sampling was carried out from late November to the middle of December 2016. The samples were taken in duplicates from 54 representative locations, comprising GW from active hand-dug wells and 1 available borehole (n = 40) and SW (n = 14) for hydrochemical analysis. The GW was sampled under pumping conditions to obtain adequate representative information from the groundwater system. The depth of each sampled well was measured and an average determined. The geo-reference of each sampling site was recorded using a Garmin GPS (Garmin Asia Corporation, Shijr, Taipei, Taiwan). The variables: temperature, pH, electrical conductivity (EC), total dissolved solids (TDS) were determined on the field with calibrated portable HACH meters. Alkalinity was also measured in situ by titrating acid (HCl) against each sample using methyl orange indicator. For hydrochemical analysis, each sample was filtrated using 0.45 μm membrane filter into 100 ml high-density polyethylene bottles preconditioned with 10% diluted HNO3. Each bottle was uniquely coded for identification, capped and tightly sealed with muslin tape. The samples were stored in ice-chest at a temperature below 0 °C and transported to the laboratory for analysis. For quality control/quality assurance, selected duplicates coded differently were analyzed alongside the main labelled ones. For specific isotopic characteristics of NO3− sources, 40 GW and 10 SW representative samples were taken across the entire study area for dissolved NO3− (δ15N and δ18O) analysis. The samples were preserved by adding KOH until a pH between 10 and 11 was attained. Major chemistry was determined at the National Isotope and the Nuclear Hydrology laboratories of the Ghana Atomic Energy Commission and National Institute of Hydrology, Roorkee, India respectively employing Dionex ICS–5000 Ion Chromatography. Compositions of nitrogen isotopes of NO3− and δ18O were measured via the denitrifier technique (Sigman et al., 2001; Li et al., 2013; Yue et al., 2017) using the isotope ratio mass spectrometer (IsoPrime, GV, UK) at the State Key Laboratory of Environmental Geochemistry, Chinese Academy of Sciences. As a first step, NO3− was reduced to N2O by denitrifying bacteria (Pseudomonas chlororaphis subsp. aureofaciens ATCC 13985). The 5 folds concentrated cells were placed in 20-mL capacity ampoules that were purified with high-grade N2 for not less than 180 min to allow non-aerobic environments. An appropriate amount of each sample (50 nmol of NO3−) was released into the conditioned vessels for the conversion of NO3− to N2O. The obtained N2O was then purified using a Trace Gas Pre-concentrator unit (Isoprime Ltd., Cheadle Hulme, Cheadle, UK) after the release of approximately 0.1–0.2 mL of 10 mol per mil aqueous NaOH to evacuate CO2 gas and prevent actions of microbes (Casciotti et al., 2002). The ratios of measured isotopes compositions were normalized using both international (USGS-32, USGS-34, USGS-35 and IAEA-N3) and internal standards. Both standards and analyzed samples have been subjected to similar treatment. Results obtained were written in delta parts per mil notations with precisions better than 0.3 and 0.5% for δ15N and δ18O respectively defined by:

2. Materials and methods 2.1. Study area The study was conducted in the LVRB of Ghana (Fig. 1) comprising inland and coastal zones. It is the section of the Volta Basin, south of the Akosombo hydro dam where the Volta River (VR) across its riparian countries finally discharges into the Atlantic Ocean. The area lies within latitudes 5°44/–6°2/N and longitudes 0°32/–0°42/E along the eastern coast of Ghana. The basin covers approximately 933.35 km2 within the equatorial coastal savannah zone (Benneh and Dickson, 1995; Avornyo et al., 2013). A section of it forms a famous international leisure spot, the Volta Estuary which is mainly along the coastal stretch. The northern part is a transnational trading and business hub bridged by the international highway linking Ghana and other West Africa countries both along the Sahel zones and the Gulf of Guinea. The topography is largely gentle and at relatively low elevation. The vegetation is largely interspersed with short grassland, clumps of trees, savannah woodland and coastal mangrove. The prevailing weather is regulated by South West monsoons air masses coming from the Gulf of Guinea and the Saharan hash hot airstreams (Avornyo et al., 2013). Two rainfall seasons resulting from the southwest monsoons (Martínez et al., 2007; Awadzi et al., 2008) prevail in the area with the most of the rains occurring between March and October and the least from November to February. The average yearly precipitation varies between 750 mm and 900 mm. There may be slight or no rain in the dry season. The annual temperatures vary between 22 and 32.6 °C. The relative humidity ranges between 70 and 90% due to closeness to the sea and the VR. The regular annual potential evapotranspiration is 1582 mm, exceeding the yearly precipitation. The basin is a typical fault-controlled sedimentary type in West Africa (Addo et al., 2018). An interconnection of underlain tertiary and quaternary formations stretching inland from Sogakorpe to Ada along the coast, consisting of recent unconsolidated sand, clay, silt and gravel (Kesse, 1985; Jayson-Quashigah et al., 2013) generally dominate the geology. Sedimentary rocks thickening approximately 8 m are predominant in the inland section (Kesse, 1985; Kortatsi, 1994) while along the coast, the quaternary formation is widely interspersed by a blend of sand, sandstone, clay and shale. The northeastern section of the basin falls under the Precambrian age of the Dahomeyan basement, consisting of felsic and mafic gneisses, as well as schists and migmatites (Akpati, 1978) and form slightly permeable clayed calcium carbonate and silty minerals. Aquifer type is mostly non-confined to semi-confined with significant GW production capacity (Gumma and Pavelic, 2013); however, most probable to be salty. Information on aquifer characteristics in the basin is documented by Ofosu et al. (2017). Details on GW recharge in the area has also been reported (Egbi et al., 2019). The land use and economic activities include crop irrigation, livestock rearing, fishing, aquaculture, tourism, salt mining, sand winning and trading. Information on land use and land cover map are given in Supplemental Table S2 and Supplemental Fig. S1.

δ‰ = ((Rsample / Rstandard ) − 1) × 1000

R represents

15

N/14N or

18

(1)

O/16O isotopic ratios

2.3. Analysis and presentation of data The resultant hydrochemical data was accepted within a ± 5% error balance (IEB) margin (Appelo and Postma, 2004) defined as:

IEB =

∑ Ca − ∑ Cb × 100% ∑ Ca +Ca + ∑ Cb

(2)

Ca and Cb are the concentrations of negative and positive ions 3

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

ingestion rate (USEPA, 2011). Average body weights (BW) for children (30.02 ± 20.49 kg) and adults (64.36 ± 17 kg) were also extracted. Corresponding average water ingestion rates (1.6 L/d, 3.4 L/d) were also obtained through a survey. The water ingestion rates obtained (IR) for adults and children in this study compare well with values (3.30 and 1.70 L/d) reported in the Upper East Region of Ghana (Craig et al., 2015). The observation implies that irrespective of location, average daily water consumption in Ghana might be subject to similar influences such as weather and frequency of activities. The USEPA model was applied in terms of hazard identification, assessment of dose-response, exposure and characterization of risk. The chronic daily doses (CDD) of NO3− and nitrite (NO2−) through oral ingestions of water were determined (USEPA, 2011; Ahada and Suthar, 2018; Adimalla and Qian, 2019):

(milliequivalent per litre). The data obtained was analyzed and presented using descriptive statistics in Minitab 16 and Excel-based statistical packages. For the determination of the hydrochemical facies, the obtained data was displayed on the trilinear diagram (Piper, 1944) using DIAGRAMMES Version 6.5 (Simler, 2009). Also, to test the hypothesis that: NO3− levels in GW were below the recommended limit of 50 mg/L (WHO, 2017), a log-normal distribution test was carried out. Also, non–parametric Mann-Whitney U test (Zhang et al., 2015; Menció et al., 2016) was conducted to validate the observations from the lognormal distribution test for inland and coastal samples since the data did not display a normal distribution. Bivariate plots and ratios of selected major ions were employed to analyze relationships between major GW constituents, ascertain dominant hydrogeochemical processes and illustrate potential pollution sources. The ions were selected based on their relative influence on water quality (Wang et al., 2014; Yang et al., 2016; Pazand et al., 2018). Additionally, the Pearson correlation coefficient matrix, a commonly used multivariate technique (Moya et al., 2015; Matiatos, 2016), was applied to identify trends of GW pollution regards to NO3− and other variables.

CDDOral =

To determine the mean probability estimate (MPE) of the relative contributions of NO3− from various origins, the Bayesian mixing model which is a Stable Isotopes Analysis in R (SIAR) software package (Parnell et al., 2010) was used. It is a robust quantification model for probability estimation for the apportionment of NO3− sources of pollution. The SIAR model can be expressed by the following relations:

HQ = CDD /RfD

(8)

k=1

(3)

Finally, the hazard index (HI), which is an assessment of a combined effect of pollutants that may cause adverse health effects due to chronic exposure were computed for NO3− and NO2− according to:

2 (μ jk , ωjk )

(4)

∑ (HI ) = HQ(Nitrate) + HQ(Nitrite)

k

Sjk ~N

(7)

where, C is the concentration of the substance of interest, in this case, NO3− and NO2− (mg/L), ED is exposure duration. The standard ED documented for adults is 30 years and for minors, 12 years. EF is the exposure frequencies (365 days/year) based on the fact that the residents daily depend on the GW for drinking purposes. The average exposure time (AT) is 10,950 days determined for adults and 4830 days for minors. The non – carcinogenic health risks for both ions were then determined and expressed as hazard quotients (HQ–NO3- and HQ–NO2), being the ratio of CDD to reference doses (RfD) for NO3− (1.6) and NO2− (0.1).

2.4. The SIAR model

Xij =

C × IR × EF × ED BW × AT

∑ Pk (Sjk + Cjk ) + εij

2 Cjk ~N (λjk , τ jk )

(5)

εij ~N (0, σ j2)

(6)

NO3−

(9) NO2−

and were determined for the The HQ and HI values for first 180 days for infants because the conversion of NO3− to nitrite (NO2−) and formation of methemoglobinemia in infants increase within this period (WHO, 2017). The estimated NO3− ingestion for infants considered all aspects of water consumption, similar to Sadler et al. (2016). An HQ or HI > 1 indicates potential health risk, while values < 1 means no possible health risk (USEPA, 2011; Mortada and Shokeir, 2018).

Where, Xij denotes the isotopic amount j of the mixture i, with i = 1, 2,3,., N and j = 1, 2, 3, …, J; Sjk represents the particular amount k of isotope j (k = 1,2, 3, …, K), which is usually expressed with mean μjk and standard deviation ωjk; pk is the proportional contribution of source k, which is estimated by the SIAR model. Cjk represents the isotope fractionation factor of isotope j on source k, which is normally distributed with mean λjk and standard deviation τjk; and εij is the residual error denoting the extra non-quantified difference of the individual compositions, following a normal distribution with average 0 and standard deviation σj. Additional details of the SIAR model is presented by Parnell et al. (2010). For the current investigation, the fractionation factor cjk is assumed to be zero (0) in Eq (5) since denitrification was identified to be less significant.

3. Results 3.1. Statistical analysis of water hydrochemistry The statistical analysis (maximum, minimum, mean, standard deviation, and CV) of various physicochemical parameters and isotopic composition of the GW and SW are shown in Supplemental Table S1. The pH of the GW ranged from 6.02 to 8.26 with a coefficient of variation of 7.43. That of the SW varied from 6.24 to 8.79 with a coefficient of variation of 10.13. EC varied from 129 to 15850 μS/cm for GW with values > 1000 μS/cm in 45% of the samples across the basin. For the SW, EC ranged from 61.40 μS/cm – 3120 μS/cm. The concentration of NO3− in the GW varied from 0.12 mg/L – 733 mg/L with a mean of 59.59 mg/L. The well with the highest concentration of NO3− in this study also recorded the maximum value of 1050 mg/L in 2014 when samples were taken for a preliminary water quality evaluation in the basin (Egbi et al., 2019). Results from the above-mentioned preliminary investigation also indicated that NO3− concentrations were above the recommended level of 50 mg/L (WHO, 2017) in 45% of analyzed GW samples. The results of NO3− for this study were further subjected to a lognormal distribution analysis to test the hypothesis that GW NO3− levels in the area were below the WHO recommended limit. From the results,

2.5. Human health risk evaluation In this paper, the health risk assessment model designed by the Environmental Protection Agency of the United States (USEPA, 2011) was modified to reflect the values appropriate of the population in the LVRB. The model is widely used to describe possible human risk posed by exposure to pollutants in water usage. (Chen et al., 2016; Sadler et al., 2016; Ahada and Suthar, 2018; Emenike et al., 2018). With approval (Supplemental plate), an average weight of infants between 3 and 6 months (4.85 ± 0.93 kg) was extracted from the birth records database of the Richard Novati Catholic Hospital, Sogakorfe. It is a most attended hospital in the area with adequate records in this regard. The average weight was used to estimate the daily water ingestion (1034 mL/d) for infants (3–6 months) based on the recommended 4

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

samples representing 50% for inland and 18% for coastal zones exceeded the benchmark, establishing higher NO3− contamination of the inland GW system. Furthermore, the non-parametric Mann-Whitney U test was conducted to validate the above observations since the data did not follow a normal distribution. From the results, NO3− concentration significantly varied (p = 0.017) between the inland and coastal zones based on chosen α level of 0.05. In SW, NO3− content varied between 0.03 mg/L and 5.33 mg/L. Overall, 35% of the analyzed GW exceeded the WHO benchmark. Also, concentrations of 75% of the samples exceeded the baseline value of 2.1 mg/L for NO3− for the Upper West Region of Ghana (Akiti, 1982) adopted for this study. This baseline value was adopted as no such reference for NO3− exists in the southern part of the country; for that matter, the study area. The spatial distribution of NO3− in the GW is displayed in Supplemental Fig. S2. In SW, NO3− content varied between 0.03 mg/L and 5.33 mg/L. Comparatively, both inland and coastal zones had higher GW NO3− distributions than in SW (Fig. 2a). Except for two wells which had NO2− concentration of 5.79 mg/L and 8.59 mg/L, the levels in GW were within the recommended value of 3 mg/L (WHO, 2017). In SW, NO2− concentrations ranged from 0.01 to 4.42 mg/L with values above the recommended limit in 43% of the samples. The results also indicate that except for two wells which had NO2− concentration of 5.79 mg/L and 8.59 mg/L, NO2− contents of the GW were within the recommended value of 3 mg/L (WHO, 2017). Since NO3− contamination may be accompanied by other nutrients such as SO42−, a correlation between NO3− and SO42−, molar ratios of SO42− to Cl− and a plot of SO42−/Cl− versus Cl−which are often used to track possible anthropogenic sources of SO42− were also employed. From literature, SO42−/Cl− ratios < 0.15 signify salinity influence while > 0.15 imply additional input sources of SO42− such as fertilizers and effluents (Lee and Song, 2007). In this study, weak relationships (R2 = 0.02 and 0.08) were observed between SO42− and NO3− while ratios of SO42− to Cl− ranged from 0.03 to 2.14 and 0.04–3.21 for GW and SW respectively. Also, approximately 35% of the GW samples were enriched by SO42− (> 0.15) as illustrated in Supplemental Fig. S3. 3.2. Correlation and compositions of nitrate isotopic signatures To apportion possible sources of GW NO3− in link with other variables, the Pearson correlation matrix defined by significant positive coefficient ≥0.5 was performed for selected GW hydrochemical species (Table 1). From the results, the highest significant associations were observed in descending order between Cl− and Na+, Cl− and Mg2+, Mg2+ and Na+, Ca2+ and K+, Cl− and EC, K+ and NO3−, Ca+2 and Mg2+, Ca2+ and NO3−, Mg2+ and EC, Cl− and Ca2+, Ca2+ and Na+, SO42− and EC. NO3− positively correlated with other parameters in the order K+ > Ca > Mg2+ > Cl− > Na+ > EC. Also, the results of δ15N–NO3- analysis for GW ranged between +4.2‰ and +27.5‰ while values for δ18O–NO3- varied between +4.5‰ and +19.9‰. For the SW, the values ranged from +3.8‰ to +14.0‰ for δ15N–NO3- and

Fig. 2. Whisker plots for (a) GW NO3− distribution in inland and coastal zones and SW and (b) Bivariate plots showing a positive association between (NO3− + Cl−)/HCO3− for GW and (c) Distribution of NO3−/Cl− ratio range in GW and SW.

Table 1 Pearson correlation matrix between 10 major selected water quality parameters. Parameter

NO3−

pH

EC

Na+

K+

Mg2+

Ca2+

Cl−

SO42-

HCO3−

NO3− pH EC Na+ K+ Mg2+ Ca2+ Cl− SO42 HCO3−

1 0.208 0.485 0.564 0.884 0.693 0.877 0.648 −0.130 0.070

1 0.197 0.157 0.438 0.272 0.393 0.177 0.158 0.687

1 0.946 0.477 0.857 0.621 0.886 0.738 0.531

1 0.555 0.927 0.743 0.978 0.661 0.472

1 0.659 0.891 0.643 −0.031 0.273

1 0.878 0.959 0.452 0.527

1 0.823 0.155 0.386

1 0.541 0.423

1 0.572

1

Correlation is significant at p < 0.05 level (2-tailed). Bolded underlined = Very strongly correlation, Bolded = moderately correlated, plain = weakly correlated. 5

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

+10.7‰ – +25.2‰ for δ18O–NO3-. In order to relate the findings on NO3− sources to water pollution, the percentages of major land use activities and associated sanitation issues in the respective zones of the basin were adequately categorized (Supplemental Table 2).

attributed to weathering, aerosol deposition and seawater intrusion. Likewise, the maximum EC value reported for the SW suggests freshwater – seawater interaction since the sampling location from which the sample was taken is about 5 Km from the sea. Regarding GW NO3− content, the statistical analysis indicated a comparatively high level of contamination inland than along the coast. The observed maximum concentrations of NO3− measured for samples taken from the same well in 2014 and 2016 respectively is attributable to discharges from a pit latrine located a few meters from this particular well. Though pit latrines are the most affordable toilet facility in the basin (GSS, 2012), the above observation indicates that they are equally potential sinks for GW contamination. This inference further suggests that the promotion of pit latrines as ideal places of convenience in most developing countries due to their cost-effectiveness and claim that leakages from these facilities pose minor threats to GW (Ravenscroft et al., 2017) might not be accurate. The WHO (2003) as part of its guidelines proposed that due to possible concurrent occurrence of NO3− and NO2− in water, the sum of the ratios of concentration (C) of each to its respective guideline value (GV) should not exceed unity as expressed below.

3.3. Contributions by NO3− pollution sources Results of the mean probability estimates of the proportional contributions of the sources of NO3− pollution of GW and SW in the area based on the SIAR mixing model revealed four NO3− sources (Fig. 4 and Supplemental Table S3). The posterior illustrations of the likelihood of sources contributing to NO3− pollution based on the sample test are also presented in Supplemental Figs. S4a and S4b. For the GW, the proportional contributions varied significantly in the order of 80% for sewage/manure, 7% for soil nitrogen (SN), 7% for precipitation and 6% for chemical fertilizers. The model output also displayed a normal distribution for all four input sources of NO3− for the SW with mean contributions of 34% for SN followed by 32% for chemical fertilizers, 25% for sewage/manure and 9% for precipitation. Contributions of NO3− in precipitation was the lowest for both water sources.

Cnitrate C + nitrite ≤ 1 GVnitrate GVnitrite

3.4. Human health risk assessment

(10)

In this study, 30% of the GW located inland and 10% along the coast had ratios > 1 based on the above relation proving that GW NO3− contamination in the basin differs significantly depending on ongoing specific site anthropogenic activities. Besides, parallel to other related studies (Marghade et al., 2012; Li et al., 2019a, 2019b), a plot of (NO3− + Cl−)/HCO3− versus TDS for the GW samples (Fig. 2b) showed a positive relationship supporting the impact of human activities on its quality. Furthermore, to establish a relationship between the GW NO3− contamination and water types, a Piper plot was generated. It displayed Na+ + K+ and Cl− + NO3− as dominant cations and anions (Supplemental Fig. S5a) with Na–Cl facies constituting 75%. The dominance of Cl− and NO3− ions further explicate GW contamination in the basin since high GW NO3− content is often associated with Cl− enrichment (Park et al., 2005). This observation also suggests that a common anthropogenic origin contributes to their distribution in the GW. The diagram also shows sub-dominant water types: HCO3–Ca (21%). The HCO3–Ca water represents freshly recharged GW which is in agreement with the contour plot of HCO3− concentrations below 200 mg/L inland compared to increasing contents up to 433 mg/L along the coast (Supplementary Figs. 5b and 5c) indicating GW flow direction. The remaining 4% (Ca–Mg–Cl mixed type) denotes mixing trends between freshwater and saline water. In terms of the SW, considering the large size of the VR, the relatively low NO3− content reported with a maximum of 5.3 mg/L suggests dilution could be a responsible cause. Since the GW NO2− concentrations were within the acceptable range for 95% of the analyzed samples; it follows that denitrification might not be significant because NO3- removal often results in the release of excess nitrogen as NO2− (Kendall and Doctor, 2003). However, dependence on the few wells with the high NO2− contents for domestic water supply, especially for drinking, should be avoided. As illustrated (Supplemental Fig. S6a), the elevated NO2− level in the SW can be attributed to conversion of excess ammonium (NH4+) entering the SW to NO2− from sewage, human waste. This is because the comparatively high NH4+ values (0.67 mg/L, 0.51 mg/L, 0.36 mg/L and 0.32 mg/L) including the maximum NO3− and NO2− values of 5.30 mg/L and 4.42 mg/L recorded for the SW correspond to locations characterized by recreational spots and hotels along the banks of the VR. Normally, NH4+ is expected to be consumed through the uptake of nitrogen by aquatic plants in flowing SW (Kendall, 1998) where nitrogen only exceeds 1 mg/L in cases of severe contamination. The above observation could result in further growth and spread of aquatic weeds, mainly at locations associated with leisure spots. It could also lead to fish poisoning since high nitrogen in SW is known to cause fish toxicity

In this section, the potential health risk of NO3− and NO2− based on water consumption among infants (< 1 year), children (> 1 year but < 18 years) and adults (> 18 years) was assessed. From the results (Supplemental Table S4), HQ–NO3- for infants ranged from 0.01 to 68.67 ± 11.412 for GW and 0.00–0.49 ± 0.141 for SW. Values of HQ–NO2- ranged from 0.02 to 12.87 ± 2.387 for GW and 0.02 to 6.71 ± 2.242 for SW. The Corresponding HI values for the GW varied from 0.04 to 69.01 with an average of 6.48 ± 13.31 while that of the SW ranged from 0.04 to 7.12 with mean 3.97 ± 2.336. For children category, the GW HQ–NO3- varied from 0.004 to 24.42 with a mean of 1.985 ± 4.06. A range of 0.001–0.18 with a mean of 0.036 ± 0.049 was obtained for the SW. The GW HQ–NO2- values varied from 0.009 to 4.58 with a mean of 0.27 ± 0.85. Also, that of the SW varied from 0.008 to 2.39 with an average of 1.38 ± 0.797. The HI – NO2− values among children ranged from 0.01 to 24.54 with a mean of 2.30 ± 4.734 for GW. They also ranged from 0.01 to 2.53 with a mean of 1.41 ± 0.831 for SW. Among adults, values of HQ–NO3- for GW ranged from 0.004 to 24.21 ± 4.023 while that of NO2− varied from 0.009 to 4.54 with mean, 0.26 ± 0.842. Of the sampled wells, 45% had HQ–NO3- > 1. All the SW HQ–NO3- values were < 1 with the highest of 0.18. Also, only 5% of GW HQ–NO2- values were > 1. The SW HQ values for NO2− ranged between 0.01 and 2.37 with a mean, 1.36. The SW HQ–NO2values > 1 represents 78.6% of the SW samples. Also, the HI values ranged from 0.013 to 24.34 with mean a mean of 2.28 ± 4.692 for GW. The values varied from 0.014 to 2.512 with mean, 1.40 ± 0.824 for the SW. For the classification of non-carcinogenic health risks (Rahman et al., 2019), HI < 0.1, HI > 0.1 < 1, HI ≥ 1 < 4 and HI ≥ 4 mean negligible, low, medium and high levels of risks respectively. Results of the percentage GW HI classification reported for this study revealed an overall 70% medium to high-risk ranges for infants. Also, 50% and 48% medium to high-risks were observed for children and adults (Fig. 5). 4. Discussion 4.1. Hydrochemical compositions and evidence of NO3− contamination The pH of both the SW and GW indicate slightly acidic to alkaline conditions. The weak coefficients of variation of pH for both water sources as defined by Yang et al. (2016) signify that pH does not variedly influence water chemistry in the basin. The high EC for reported GW in this study was also highlighted by Egbi et al. (2019) and was 6

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

4.2. NO3− relationships To further elucidate anthropogenic contributions of GW contamination in link with NO3− and other variables, bivariate plots of NO3− and selected variables were employed. A plot of NO3− versus Cl− initially showed a weak linear relationship (R2 = 0.05). However, when samples with NO3− values above the recommended limit and corresponding Cl− concentrations were plotted, a strong correlation of 0.74 was obtained (Supplemental Fig. S6b), emphasizing common anthropogenic pathways of both ions in the GW in areas associated with high contamination. Likewise, for SW, a positive relationship (R2 = 0.4) was obtained. From the results of the Pearson correlation matrix (Table 1), the significant association between NO3− and K+ can be attributed to inorganic fertilizer and manure because potassium nitrate and livestock waste are often applied to the soil for crop production in the area. Although fertilizers and manure significantly contribute to soil fertility in the area (Bosompem, 2015), information on the amount applied and periods of application is lacking, thereby limiting any appropriate linkages to be made in this regard. Also, the strong participation of Ca2+ and Mg2+ with NO3− could be closely related to dissolved calcareous materials from clam shells which are of great commercial benefits in the area. Often, after removing the meat of the clam which is a delicacy, the empty shells which are rich in Ca2+ and Mg2+ are heaped for sale as ingredients for poultry feed preparation and as materials for foundation of buildings and pavements. This can then result in their leaching into the GW system as evidenced by the positive relationships between HCO3− and Ca+2, HCO3− and Mg2+ (Table 1) indicating their common origin. A possible common source of these ions in the GW could be therefore linked to leached clam shells since HCO3−, Ca+2 and Mg2+ are not associated with GW – rock interactions in the basin (Egbi et al., 2019). This deduction is supported by the detection of Ca2+ and Mg2+ in higher concentrations from elemental analysis of Volta clam shells (Serfor-Armah et al., 2010). The moderate associations between NO3− and Cl−, NO3− and Na+, including NO3− and EC typically emphasizes impacts of wastewater on the GW. The possibility of significant removal of NO3− from the GW was also probed. From literature, NO3− reduction has been linked to presence of redox-active elements like iron (Fe) and Mn, which are also geochemically derived (Rivett et al., 2008). In this investigation, Fe was used because it is the dominant redox-sensitive element in pan soil in the area (Avornyo et al., 2013). If NO3− removal is occurring, increased Fe content with decreasing NO3− concentration is expected. However, this trend was not observed (Supplemental Fig. S6c), suggesting denitrification might not be significant in the GW. The observation was clarified by obtaining a slope of 0.14 on δO18 versus δ15N plot. Usually, in the case of denitrification, a slope of about 0.5 would be obtained (Cey et al., 1999; Kendall et al., 2007). In addition, Li et al. (2019a, 2019b) in agreement with Kendal et al. (2007) noted that during GW denitrification, a negative correlation is expected on a plot of In(NO3−/ Cl−) against δ15N–NO3-. Contrary, for this study, a weak correlation of 0.07 was obtained, proving that currently, NO3− removal from the GW is not significant. The observation could be due to less interaction between infiltrating rain and the lithological strata, which should play an influential role in aiding NO3− removal during GW recharge before it reaches the aquifer. A quantitative analysis of NO3− with dissolved oxygen and the incorporation of isotopic fractionation concept may be appropriate in further probing GW denitrification in the basin.

Fig. 3. (a) NO3−/Cl− molar ratios vs Cl− concentrations for water samples (b) δ15N–NO3- versus NO3− and (c) Dual isotopes schematic representation of NO3− pollution sources (Kendall et al., 2007; Li et al., 2019a, 2019b).

(Twitchen and Eddy, 1994; Levit, 2010). From literature (Liu et al., 2006; McArthur et al., 2012), water with high salinity exhibits a small ratio of NO3− to Cl−. Conversely, a comparatively higher ratio reflects pollution from agricultural sources, sewage and livestock manure. A NO3−/Cl− reference range (0.05–0.22) without human effects has also been reported (Li, 2014). The NO3−/Cl− ratios for this study ranged from 0.0001 to 2.80 and 0.009–0.87, representing 43% and 21% for the GW and SW respectively (Fig. 2c). The above-reported NO3−/Cl− ratios accordingly support the fact that sources of water in the area are been contaminated by human activities. Also, the enrichment of the GW by SO42− as noted in Subsection 3.1 shows that aside NO3−, other substances from organic sources are contributing to the GW contaminating.

4.3. Identification of principal NO3− pollution sources The primary sources of NO3− in water can be identified through specific analysis of relationships between NO3− and Cl− and δ15N and δ18O of NO3− isotopes as these ions maintain their intrinsic tracer characteristic in water. A plot of NO3− to Cl− molar ratio against corresponding Cl− (Fig. 3a) showed that the GW is mostly affected by 7

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

Fig. 4. Spatial proportional contributions of four NO3− sources in the basin derived from the SIAR model. Boxplots illustrate the 5th (light gray, 25th (gray), 50th (dark gay), 75th (gray) and 95th (light gray)) percentiles from bottom to top.

and livestock production. This observation is important as domestic sewage contamination of GW may be associated with pathogens like Salmonella enterica, which could pose ill-health conditions such as typhoid in humans. Further, the dual isotopes plot showed that denitrification could be occurring in the SW, resulting in the observed relatively high SW NO2− content, as noted in Subsection 4.1. Comparing the current study to the first comprehensive work on NO3− contamination of water resources in Ghana, this study shows fairly elevated GW NO3− concentration (maximum of 731.15 mg/L) to that of 143.94 mg/L reported north of the country (Anornu et al., 2017). Besides, NO3− input sources from fertilizers which were not significant in the earlier stated parallel study were also clearly distinguished in the current one. The above comparative analysis conclusively suggests that aquifers in the LVRB are under pressure of contamination from increasing human activities. Hence, immediate preemptive measures are required.

domestic and municipal effluents. Agricultural inputs were also evident in the SW. Nevertheless, this approach was unable in precisely distinguishing mixed sources of NO3−. Also, despite the successful use of δ15N–NO3- versus NO3− (Fig. 3b) to categorize NO3− contributions from sewage/manure and soil organic matter, the method is somewhat limited in differentiating NO3− originating from rainfall and fertilizers. As a significant advantage, a scatter plot of the coupled isotopes (δ18O–NO3- and δ15N–NO3-) sufficiently addressed these challenges (Fig. 3c). The duet isotopes plot additionally distinguished sources of animal manure which were not clearly illustrated using the hydrochemical relationships. Though this approach was also inadequate in discriminating NO3− originating from sewage and manure, logically, effluents could be predominant since most of the wells are privately owned and located in residential areas, mostly with no sewage systems. The principal sources of NO3− contamination identified in this study based on land use activities in the area (Supplemental Table S2) are therefore related to poor sanitary conditions (effluents, pit latrines, open defecation), tourism and recreational activities, crop irrigation 8

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

Results of the percentage distribution of HI values for GW (Fig. 5) showed that infants are most at risk of NO3− and NO2− ingestion. Results of the SW HI values also indicate that the inhabitants are at potential risk of drinking water from approximately 81% of the SW sampling locations. Since most mothers in the area use boiled water for their bottle-fed infants to ensure they consume pathogen-free water, the ingestion of NO3− and NO2− by infants may even be higher as these ions are likely to be released in higher concentrations through boiling. Also, infants and pregnant women may ingest both substances through meals prepared with the same water resulting in competitive inhibition of iodine uptake with potential thyroid dysfunction (WHO, 2017). Moreover, in relating NO3− ingestion during pregnancy, issues have been raised on possible increasing NO2− levels that might diffuse through the placenta and possibly result in cumulative methemoglobin content in the fetus (Zhou, 2015). Long term exposure to NO3− and NO2− may also result in the inadequate supply of oxygen to organs, leading to brain damage and even death in minors (Ahada and Suthar, 2018) including other health risks (Parvizishad et al., 2017). Water with elevated NO3− used as a drinking source and watering of farm animals have also been reported to cause low feeding among livestock (Shukla and Saxena, 2018). Therefore, the dependence on both the GW and SW as sources of drinking and watering of flocks by farmers in the area similarly has the potential to induce low appetite in the animals resulting in their loss of weight and possible financial consequences to their owners. Based on these findings, the following specific management options were proposed to aid protect and preserve the water sources in the basin from further contamination.

Fig. 5. Distribution of GW percentage HI based on non-carcinogenic risk classification for infants. Children and adults. HI < 0.1 = negligible, HI > 0.1 < 1 = low, HI ≥ 1 < 4 medium, HI ≥ 4 = high.

4.4. Contributions by source apportionment based on SIAR model Based on the SIAR model (Fig. 4), the highest percentage apportioned to sewage/manure confirms their dominance as sources of NO3− contamination of water in the basin. Also, the highest percentage contribution from SN to the SW could be due to erosion and discharge of accumulated NO3− containing substances in soils into the SW during runoff. Another possible source of the SN is mineralized soil organic matter. The δ15N–NO3- versus NO3− plot supports the above deduction as some of the SW samples plotted within the soil NO3− zone (Fig. 3c). Additionally, the relatively high percentage contribution from chemical fertilizers further highlights the impact of agricultural practices upstream and within the study area along the banks of the VR. The lowest contribution of NO3− in precipitation to both GW and SW could be attributed to the period of sampling when minimal rainfall normally occurs in the basin. Although the contribution of NO3− from precipitation was not evident from the dual isotopes plot, the application of the SIAR model was robust in resolving this. The SIAR model as a reliable and convenient tool is capable of disentangling and estimating specific and likely origins of NO3− that otherwise, the dual isotopes plot is unable to differentiate (Xue et al., 2012; Xia et al., 2017).

• Wastewater treatment and management systems should be built to • • • • •

4.5. Human health risk assessment Owing to their weak and developing immune systems, infants are particularly possibly at risk of adverse health effects from substance exposure such as NO3− via ingestion (Richard et al., 2014). In this regard, a non-carcinogenic oral health risk for NO3− was computed. For the GW, the HQ and HI values were high for correspondingly elevated NO3− concentrations. Based on the HQ values obtained, 65% of the wells pose a high risk of NO3− contamination to infants. Out of the wells with potentially high NO3− risk to infants, 54% belong to the inland section of the basin, indicating a relatively high risk of NO3− to infants in this zone. Similarly, GW HQ–NO3- showed that 48% are of potential health risk to children. For adults, there is a potential exposure risk of NO3− ingestion in the use of almost 45% of the sampled wells. Regarding the SW, HQ–NO3- values showed no obvious risk for infants and children. However, 79% of the SW had corresponding HQ values for NO2− > 1 for both age groups suggesting a higher risk of SW NO2− contamination to minors. At the moment, while SW NO3− and GW NO2− contents might pose minimal or no health hazards to adults in the area, it is still crucial to prioritize the periodic appraisal of water quality in the basin using NO3− and NO2− as prime indicators. This will help safeguard the public health from the potential exposure to harmful substances through the drinking of contaminated water.

avoid discharge and leakage of effluents into both the SW and GW respectively, especially at the northeastern zone of the basin. Suitable agricultural practices such as proper application of fertilizers and manure to cultivated fields along the banks of the SW and southern zone of the area should be employed to prevent discharge of pollutants into the SW environments. Prior to the drilling of new wells to augment water supply in the basin, a thorough survey and site characterization is required to prevent siting of wells in the proximity of contaminated sites. Direct aquaculture in the VR as currently being practised, should be regulated and alternate recommended methods adopted. Buffer zones with enough vegetation cover to prevent flow backs from agricultural fields around the VR should be enforced. Authorities need to coactively prioritize awareness creation on improving sanitation and water quality protection across the basin.

5. Conclusions This study investigated the origins of GW and SW NO3− contamination and associated health risks in the LVRB of Ghana using hydrochemistry, the Pearson correlation matrix, isotopes, Bayesian and USEPA human health risk assessment models. It is a first comprehensive attempt of its kind in a typical coastal basin similar to other basins along the West African coast whose water resources are under threats of contamination from increasing human activities. The study revealed that although elevated GW NO3− contamination was noticed across the basin, residents at inland zone are at greater potential risks of concurrent ingestion of NO3− and NO2−. Based on hydrochemical and isotopic analyses, sewage, manure and chemical fertilizers were identified as the principal sources of NO3− contamination of the sources of water. Results of the Bayesian model displayed spatial variations of NO3− sources with significant contributions from sewage/manure and chemical fertilizers in both GW and SW. Human health risk assessment indicated medium risk levels of GW NO3− contamination for children and adults while a high-risk level of contamination was observed for infants. The findings also showed that, currently, NO2− contamination 9

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

of SW poses a potential health risk to all age categories in the basin. Overall, anthropogenic activities are negatively impacting water quality, especially GW quality in the basin. Monitoring and evaluation of levels of nitrogen species as indicators of water pollution from human activities should hence be prioritized. The findings gave insights into the current water quality status in the area and would assist mandated authorities in making adequate evident based decisions towards its protection. The combined approach employed adequately addressed the purpose of the study and could be globally applied.

On Impact of Agricultural Activities on Groundwater. pp. 87–98. Ako, A.A., Eyong, G.E.T., Shimada, J., Koike, K., Hosono, T., Ichiyanagi, K., Richard, A., Tandia, B.K., Nkeng, G.E., Roger, N.N., 2014. Nitrate contamination of groundwater in two areas of the Cameroon volcanic line (banana plain and mount Cameroon area). Appl. Water Sci. 4 (2), 99–113. https://doi.org/10.1007/s13201-013-0134-x. Akouvi, A., Dray, M., Violette, S., de Marsily, G., Zuppi, G.M., 2008. The sedimentary coastal basin of Togo: example of a multilayered aquifer still influenced by a palaeoseawater intrusion. Hydrogeol. J. 16 (3), 419–436. Akpati, B.N., 1978. Geologic structure and evolution of the Keta Basin, Ghana, West Africa. Geol. Soc. Am. Bull. 89 (1), 124–132. Anornu, G., Gibrilla, A., Adomako, D., 2017. Tracking nitrate sources in groundwater and associated health risk for rural communities in the White Volta River basin of Ghana using isotopic approach (δ15N, δ18ONO3 and 3H). Sci. Total Environ. 603, 687–698. Appelo, C.A.J., Postma, D., 2004. Geochemistry, Groundwater and Pollution. CRC press Available from: www.who.int/water_sanitation_health/gdwqrevision/nitrate-nitritefs-2017.pdf, Accessed date: 2 August 2018. Avornyo, V.K., Adjadeh, T.A., Amatekpor, J.K., 2013. Pan soils in the lower Volta Basin of Ghana. West African J. Appl. Ecol. 21 (2), 63–77. Awadzi, T.W., Ahiabor, E., Breuning-Madsen, H., 2008. The soil-land use system in a sand spit area in the semi-arid coastal savanna region of Ghana–development, sustainability and threats. West African J. Appl.Ecol. 13 (1), 132–143. Baldursson, S., Karanis, P., 2011. Waterborne transmission of protozoan parasites: review of worldwide outbreaks–an update 2004–2010. Water Res. 45 (20), 6603–6614. Benneh, G., Dickson, K.B., 1995. A New Geography of Ghana, Revised edition. . Bordeleau, G., Savard, M.M., Martel, R., Ampleman, G., Thiboutot, S., 2008. Determination of the origin of groundwater nitrate at an air weapons range using the dual isotope approach. J. Contam. Hydrol. 98 (3–4), 97–105. Bosompem, O.A., 2015. Land-Use/Land Cover Change and its Relationship to Changing Climate and Crop Production in the Lower Volta Basin, Ghana. Doctoral Dissertation. University of Ghana, Legon. Burow, K.R., Jurgens, B.C., Belitz, K., Dubrovsky, N.M., 2013. Assessment of regional change in nitrate concentrations in groundwater in the Central Valley, California, USA, 1950s– 2000s. Environ. Earth Sci. 69 (8), 2609–2621. Casciotti, K.L., Sigman, D.M., Hastings, M.G., Böhlke, J.K., Hilkert, A., 2002. Measurement of the oxygen isotopic composition of nitrate in seawater and freshwater using the denitrifier method. Anal. Chem. 74 (19), 4905–4912. Cey, E.E., Rudolph, D.L., Aravena, R., Parkin, G., 1999. Role of the riparian zone in controlling the distribution and fate of agricultural nitrogen near a small stream in southern Ontario. J. Contam. Hydrol. 37 (1–2), 45–67. Chen, Jie, Wu, Hao, Qian, Hui, 2016. Groundwater nitrate contamination and associated health risk for the rural communities in an agricultural area of ningxia, northwest China. Expo. Health. https://doi.org/10.1007/s12403-016-0208-8. Craig, L., Lutz, A., Berry, K.A., Yang, W., 2015. Recommendations for fluoride limits in drinking water based on estimated daily fluoride intake in the Upper East Region, Ghana. Sci. Total Environ. 532, 127–137. Dorice, K., Josephine, N., Margaret, T.A., Gaston, L., Veronique, K.K.B., Henriette, A.B., Emmanuel, E.G., 2010. Bacterial contamination of water points of the upper Mfoundi watershed, Yaounde, Cameroon. Afr. J. Microbiol. Res. 4 (7), 568–574. Egbi, C.D., Anornu, G., Appiah-Adjei, E.K., Ganyaglo, S.Y., Dampare, S.B., 2019. Evaluation of water quality using hydrochemistry, stable isotopes, and water quality indices in the Lower Volta River Basin of Ghana. Environ. Dev. Sustain. 21 (6), 3033–3063. Emenike, C.P., Tenebe, I.T., Jarvis, P., 2018. Fluoride contamination in groundwater sources in Southwestern Nigeria: assessment using multivariate statistical approach and human health risk. Ecotoxicol. Environ. Saf. 156, 391–402. Fabro, A.Y.R., Ávila, J.G.P., Alberich, M.V.E., Sansores, S.A.C., Camargo-Valero, M.A., 2015. Spatial distribution of nitrate health risk associated with groundwater use as drinking water in Merida, Mexico. Appl. Geogr. 65, 49–57. Fu, B.J., Zhuang, X.L., Jiang, G.B., Shi, J.B., Lu, A.Y.H., 2007. Feature: Environmental Problems and Challenges in China. GSS, 2012. 2010 Population and Housing Census: Summary Report of Final Results. Gumma, M.K., Pavelic, P., 2013. Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environ. Monit. Assess. 185 (4), 3561–3579. Hackley, K.C., 2007. Groundwater Quality of Springs and Wells of the Sinkhole Plain in Southwestern Illinois: Determination of the Dominant Sources of Nitrate. Circular no. 570. He, S., Wu, J., 2018. Hydrogeochemical characteristics, groundwater quality, and health risks from hexavalent chromium and nitrate in groundwater of Huanhe Formation in Wuqi County, Northwest China. Expo. Health 1–13. He, X., Wu, J., He, S., 2018. Hydrochemical characteristics and quality evaluation of groundwater in terms of health risks in Luohe aquifer in Wuqi County of the Chinese Loess Plateau, northwest China. Hum. Ecol. Risk Assess. Int. J. 1–20. Helstrup, T., Jørgensen, N.O., Banoeng-Yakubo, B., 2007. Investigation of hydrochemical characteristics of groundwater from the Cretaceous-Eocene limestone aquifer in southern Ghana and southern Togo using hierarchical cluster analysis. Hydrogeol. J. 15 (5), 977–989. Hosono, T., Tokunaga, T., Kagabu, M., Nakata, H., Orishikida, T., Lin, I.T., Shimada, J., 2013. The use of δ15N and δ18O tracers with an understanding of groundwater flow dynamics for evaluating the origins and attenuation mechanisms of nitrate pollution. Water Res. 47 (8), 2661–2675. Jayson-Quashigah, P.N., Appeaning Addo, K., Kufogbe, S.K., 2013. Shoreline monitoring using medium resolution satellite imagery, a case study of the eastern coast of Ghana. J. Coast. Res. 65, 511–516. Ji, X., Xie, R., Hao, Y., Lu, J., 2017. Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural

Main findings

• NO contamination of GW above the WHO limit was observed in 35% of sampled wells across the basin. • The levels of contamination pose potential health risks to in3

−

habitants.

CRediT authorship contribution statement Courage D. Egbi: Conceptualization, Investigation, Formal analysis, Software, Writing - original draft, Visualization, Writing - review & editing. Geophrey K. Anornu: Supervision, Writing - review & editing. Samuel Y. Ganyaglo: Conceptualization, Investigation, Project administration, Writing - review & editing, Supervision. Emmanuel K. Appiah-Adjei: Supervision, Writing - review & editing. Si-Liang Li: Formal analysis, Funding acquisition, Writing - review & editing. Samuel B. Dampare: Conceptualization, Funding acquisition. Declaration of competing interests There is no conflict of interest issue regarding the publication of this manuscript. Acknowledgements The manuscript is part of the doctoral thesis of the first author with funding support from the Regional Water and Environmental Sanitation Centre, Kumasi, the Ghana Government and the World Bank under the Africa Centres' of Excellence project. Funding support was also obtained from the Opening Fund of the State Key Laboratory of Environmental Geochemistry, CAS, China, to enable the analysis of isotopes of nitrate. Opinions stated here solely pertain to the investigators. We duly acknowledge the IAEA through which this project was initiated as a coordinated research project (CRP F33020-RC-17430). We appreciate the Ghana Atomic Energy Commission, and laboratory staff of the Nuclear Hydrology, Roorkee, India for logistics assistance and analysis of samples. Thanks to the editor and reviewers. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110227. References Addo, K.A., Nicholls, R.J., Codjoe, S.N.A., Abu, M., 2018. A biophysical and socioeconomic review of the Volta delta, Ghana. J. Coast. Res. 34 (5), 1216–1226. Adimalla, N., Qian, H., 2019. Groundwater quality evaluation using water quality index (WQI) for drinking purposes and human health risk (HHR) assessment in an agricultural region of Nanganur, south India. Ecotoxicol. Environ. Saf. 176, 153–161. Affum, A.O., Osae, S.D., Nyarko, B.J.B., Afful, S., Fianko, J.R., Akiti, T.T., Adomako, D., Acquaah, S.O., Dorleku, M., Antoh, E., Barnes, F., 2015. Total coliforms, arsenic and cadmium exposure through drinking water in the Western Region of Ghana: application of multivariate statistical technique to groundwater quality. Environ. Monit. Assess. 187 (2), 1. Ahada, C.P., Suthar, S., 2018. Groundwater nitrate contamination and associated human health risk assessment in southern districts of Punjab, India. Environ. Sci. Pollut. Control Ser. 25 (25), 25336–25347. Akiti, T.T., 1982. Nitrate levels in some granitic aquifers from Ghana. Proc. Intern. Symp.

10

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

South. Parnell, A.C., Inger, R., Bearhop, S., Jackson, A.L., 2010. Source partitioning using stable isotopes: coping with too much variation. PLoS One 5 (3), e9672. Parvizishad, M., Dalvand, A., Mahvi, A.H., Goodarzi, F., 2017. A Review of Adverse Effects and Benefits of Nitrate and Nitrite in Drinking Water and Food on Human Health. Pastén-Zapata, E., Ledesma-Ruiz, R., Harter, T., Ramírez, A.I., Mahlknecht, J., 2014. Assessment of sources and fate of nitrate in shallow groundwater of an agricultural area by using a multi-tracer approach. Sci. Total Environ. 470, 855–864. https://doi. org/10.1016/j.scitotenv.2013.10.043. Pazand, K., Khosravi, D., Ghaderi, M.R., Rezvanianzadeh, M.R., 2018. Identification of the hydrogeochemical processes and assessment of groundwater in a semi-arid region using major ion chemistry: a case study of Ardestan basin in Central Iran. Gr. Water Sustain. Dev. 6, 245–254. Piper, A.M., 1944. A graphic procedure in the geochemical interpretation of water‐analyses. Eos, Trans. Am. Geophys. Union 25 (6), 914–928. Rahman, M.M., Bodrud-Doza, M., Muhib, M.I., Hossain, K.F.B., Hossain, M.S., Akter, S., Sikder, M.T., Shammi, M., Uddin, M.K., 2019. Human Health Risk Assessment of Nitrate and Trace Metals via Groundwater in Central Bangladesh. Rajendran, A., Mansiya, C., 2015. Physico-chemical analysis of ground water samples of coastal areas of south Chennai in the post-Tsunami scenario. Ecotoxicol. Environ. Saf. 121, 218–222. Rakotondrabe, F., Ngoupayou, J.R.N., Mfonka, Z., Rasolomanana, E.H., Abolo, A.J.N., Ako, A.A., 2018. Water quality assessment in the Bétaré-Oya gold mining area (EastCameroon): multivariate statistical analysis approach. Sci. Total Environ. 610, 831–844. Ravenscroft, P., Mahmud, Z.H., Islam, M.S., Hossain, A.K.M.Z., Zahid, A., Saha, G.C., Ali, A.Z., Islam, K., Cairncross, S., Clemens, J.D., Islam, M.S., 2017. The public health significance of latrines discharging to groundwater used for drinking. Water Res. 124, 192–201. Re, V., Faye, S.C., Faye, A., Faye, S., Gaye, C.B., Sacchi, E., Zuppi, G.M., 2011. Water quality decline in coastal aquifers under anthropic pressure: the case of a suburban area of Dakar (Senegal). Environ. Monit. Assess. 172 (1–4), 605–622. Re, V., Sacchi, E., Kammoun, S., Tringali, C., Trabelsi, R., Zouari, K., Daniele, S., 2017. Integrated socio-hydrogeological approach to tackle nitrate contamination in groundwater resources. The case of Grombalia Basin (Tunisia). Sci. Total Environ. 593, 664–676. Richard, A.M., Diaz, J.H., Kaye, A.D., 2014. Reexamining the risks of drinking-water nitrates on public health. Ochsner J. 14 (3), 392–398. Rivett, M.O., Buss, S.R., Morgan, P., Smith, J.W., Bemment, C.D., 2008. Nitrate attenuation in groundwater: a review of biogeochemical controlling processes. Water Res. 42 (16), 4215–4232. Sadler, R., Maetam, B., Edokpolo, B., Connell, D., Yu, J., Stewart, D., Park, M.J., Gray, D., Laksono, B., 2016. Health risk assessment for exposure to nitrate in drinking water from village wells in Semarang, Indonesia. Environ. Pollut. 216, 738–745. Serfor-Armah, Y., Amoah, C.M., Odamtten, G.T., Opata, N.S., Akaho, E.H.K., 2010. Determination of elemental and nutrient composition of water, clam mantle and shell of the Volta clam (Galatea paradoxa) using neutron activation analysis. J. Radioanal. Nucl. Chem. 286 (1), 39–45. Shukla, S., Saxena, A., 2018. Global status of nitrate contamination in groundwater: its occurrence, health impacts, and mitigation measures. In: Handbook of Environmental Materials Management, pp. 1–21. Sigman, D.M., Casciotti, K.L., Andreani, M., Barford, C., Galanter, M.B.J.K., Böhlke, J.K., 2001. A bacterial method for the nitrogen isotopic analysis of nitrate in seawater and freshwater. Anal. Chem. 73 (17), 4145–4153. Simler, R., 2009. DIAGRAMMES: Logiciel d’hydrochimie multilangage en distribution libre. Laboratoire d'Hydrogéologie d'Avignon, France. Singh, B., Jain, V., Mohan, A., 2013. Monitoring of groundwater chemistry in terms of physical and chemical parameters of Gajraula, a semi-urbanized town of North India. Phys. Chem. Earth 58, 34–41 Parts A/B/C. Twitchen, I.D., Eddy, F.B., 1994. Sublethal Effects of Ammonia on Freshwater Fish. Udhayakumar, R., Manivannan, P., Raghu, K., Vaideki, S., 2016. Assessment of physicochemical characteristics of water in Tamilnadu. Ecotoxicol. Environ. Saf. 134, 474–477. US Environmental Protection Agency, 2011. Exposure Factors Handbook: 2011 Edition. EPA/600/R–09/052F. Wang, R., Bian, J.M., Gao, Y., 2014. Research on hydrochemical spatio-temporal characteristics of groundwater quality of different aquifer systems in Songhua River Basin, eastern Songnen Plain, Northeast China. Arabian J. Geosci. 7 (12), 5081–5092. World Health Organization, 2003. Nitrate and Nitrite in Drinking-Water: Background Document for Development of WHO Guidelines for Drinking-Water Quality (No. WHO/SDE/WSH/04.03/56). World Health Organization. World Health Organization, 2017. Guidelines for Drinking-Water Quality: First Addendum to the, fourth ed. World Health Organization, Geneva. Wu, J., Sun, Z., 2016. Evaluation of shallow groundwater contamination and associated human health risk in an alluvial plain impacted by agricultural and industrial activities, mid-west China. Expo. Health 8 (3), 311–329. Xia, Y., Li, Y., Zhang, X., Yan, X., 2017. Nitrate source apportionment using a combined dual isotope, chemical and bacterial property, and Bayesian model approach in river systems. J. Geophys. Res.: Biogeosciences 122 (1), 2–14. Xue, D., Botte, J., De Baets, B., Accoe, F., Nestler, A., Taylor, P., Van Cleemput, O., Berglund, M., Boeckx, P., 2009. Present limitations and future prospects of stable isotope methods for nitrate source identification in surface-and groundwater. Water Res. 43 (5), 1159–1170. Xue, D., De Baets, B., Van Cleemput, O., Hennessy, C., Berglund, M., Boeckx, P., 2012. Use of a Bayesian isotope mixing model to estimate proportional contributions of multiple

watershed. Environ. Pollut. 229, 586–594. Kendall, C., Doctor, D.H., 2003. Stable isotope applications in hydrologic studies. Treatise Geochem. 5, 605. Kendall, C., 1998. Tracing nitrogen sources and cycling in catchments. Isotope Tracers in Catchment Hydrology. pp. 519–576. Kendall, C., Elliott, E.M., Wankel, S.D., 2007. Tracing anthropogenic inputs of nitrogen to ecosystems. Stable Isot. Ecol. Environ. Sci. 375–449. Kesse, G.O., 1985. The Mineral and Rock Resources of Ghana. Kim, H., Kaown, D., Mayer, B., Lee, J.Y., Hyun, Y., Lee, K.K., 2015. Identifying the sources of nitrate contamination of groundwater in an agricultural area (Haean basin, Korea) using isotope and microbial community analyses. Sci. Total Environ. 533, 566–575. Kortatsi, B.K., 1994. Groundwater utilization in Ghana. 222. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, pp. 149–156. Lee, J.Y., Song, S.H., 2007. Evaluation of groundwater quality in coastal areas: implications for sustainable agriculture. Environ. Geol. 52 (7), 1231–1242. Levit, S.M., 2010. A Literature Review of Effects of Ammonia on Fish. Center for Science in Public Participation Bozeman, Montana 2010. Li, C., Li, S.L., Yue, F.J., Liu, J., Zhong, J., Yan, Z.F., Zhang, R.C., Wang, Z.J., Xu, S., 2019a. Identification of sources and transformations of nitrate in the Xijiang River using nitrate isotopes and Bayesian model. Sci. Total Environ. 646, 801–810. Li, P., 2014. Research on Groundwater Environment under Human Interferences: a Case Study from Weining Plain, Northwest China. Changan University, Changan (Doctoral Thesis). Google Scholar. Li, P., He, S., Yang, N., Xiang, G., 2018. Groundwater quality assessment for domestic and agricultural purposes in Yan’an City, northwest China: implications to sustainable groundwater quality management on the Loess Plateau. Environ. Earth Sci. 77 (23), 775. Li, P., He, X., Guo, W., 2019b. Spatial groundwater quality and potential health risks due to nitrate ingestion through drinking water: a case study in Yan’an City on the Loess Plateau of northwest China. Hum. Ecol. Risk Assess. Int. J. 1–21. Li, S.L., Liu, C.Q., Li, J., Xue, Z., Guan, J., Lang, Y., Ding, H., Li, L., 2013. Evaluation of nitrate source in surface water of southwestern China based on stable isotopes. Environ. Earth Sci. 68 (1), 219–228. Liddle, E.S., Mager, S.M., Nel, E.L., 2015. The suitability of shallow hand dug wells for safe water provision in sub-Saharan Africa: lessons from Ndola, Zambia. Appl. Geogr. 57, 80–90. Liu, C.Q., Li, S.L., Lang, Y.C., Xiao, H.Y., 2006. Using δ15N-and δ18O-values to identify nitrate sources in karst ground water, Guiyang, Southwest China. Environ. Sci. Technol. 40 (22), 6928–6933. Loh, Y.S., Banoeng-Yakubo, B., Yidana, S.M., Asiedu, D., Akabzaa, T., Jørgensen, N.O., 2012. Hydrochemical characterisation of groundwater in parts of the Volta Basin, northern Ghana. Ghana Min. J. 13 (1), 24–32. MacDonald, A.M., Calow, R.C., 2009. Developing groundwater for secure rural water supplies in Africa. Desalination 248 (1–3), 546–556. Marghade, D., Malpe, D.B., Zade, A.B., 2012. Major ion chemistry of shallow groundwater of a fast growing city of Central India. Environ. Monit. Assess. 184 (4), 2405–2418. Martínez, M.L., Intralawan, A., Vázquez, G., Pérez-Maqueo, O., Sutton, P., Landgrave, R., 2007. The coasts of our world: ecological, economic and social importance. Ecol. Econ. 63 (2–3), 254–272. Matiatos, I., 2016. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: a case study of Asopos basin (Central Greece). Sci. Total Environ. 541, 802–814. McArthur, J.M., Sikdar, P.K., Hoque, M.A., Ghosal, U., 2012. Waste-water impacts on groundwater: Cl/Br ratios and implications for arsenic pollution of groundwater in the Bengal Basin and Red River Basin, Vietnam. Sci. Total Environ. 437, 390–402. Meghdadi, A., Javar, N., 2018. Quantification of spatial and seasonal variations in the proportional contribution of nitrate sources using a multi-isotope approach and Bayesian isotope mixing model. Environ. Pollut. 235, 207–222. Menció, A., Boy, M., Mas-Pla, J., 2011. Analysis of vulnerability factors that control nitrate occurrence in natural springs (Osona Region, NE Spain). Sci. Total Environ. 409 (16), 3049–3058. Menció, A., Mas-Pla, J., Otero, N., Regàs, O., Boy-Roura, M., Puig, R., Bach, J., Domènech, C., Zamorano, M., Brusi, D., Folch, A., 2016. Nitrate pollution of groundwater; all right…, but nothing else? Sci. Total Environ. 539, 241–251. Mortada, W.I., Shokeir, A.A., 2018. Does nitrite and nitrate levels in drinking water impact the health of people in Dakahlia governorate, Egypt? Environ. Sci. Pollut. Control Ser. 1–11. Moya, C.E., Raiber, M., Taulis, M., Cox, M.E., 2015. Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach. Sci. Total Environ. 508, 411–426. Mul, M., Obuobie, E., Appoh, R., Kankam-Yeboah, K., Bekoe-Obeng, E., Amisigo, B., Logah, F.Y., Ghansah, B., McCartney, M., 2015. Water resources assessment of the Volta River basin. Int. Water Manag.Inst. (IWMI) 166. Nlend, B., Celle-Jeanton, H., Huneau, F., Ketchemen-Tandia, B., Fantong, W.Y., BoumNkot, S.N., Etame, J., 2018. The impact of urban development on aquifers in large coastal cities of West Africa: present status and future challenges. Land Use Policy 75, 352–363. Ofosu, E.A., Mul, M., Boateng-Gyimah, M., Annor, F., Ampomah, B.Y., 2017. Overview of the Re-operation and Re-optimisation of the Akosombo and Kpong Dams Project. Ouedraogo, I., Vanclooster, M., 2016. A meta-analysis and statistical modelling of nitrates in groundwater at the African scale. Hydrol. Earth Syst. Sci. 20 (6), 2353–2381. Ouedraogo, I., Defourny, P., Vanclooster, M., 2016. Mapping the groundwater vulnerability for pollution at the pan African scale. Sci. Total Environ. 544, 939–953. Park, S.C., Yun, S.T., Chae, G.T., Yoo, I.S., Shin, K.S., Heo, C.H., Lee, S.K., 2005. Regional Hydrochemical Study on Salinization of Coastal Aquifers, Western Coastal Area of

11

Ecotoxicology and Environmental Safety 191 (2020) 110227

C.D. Egbi, et al.

Yue, F.J., Li, S.L., Liu, C.Q., Zhao, Z.Q., Ding, H., 2017. Tracing nitrate sources with dual isotopes and long-term monitoring of nitrogen species in the Yellow River, China. Sci. Rep. 7 (1), 8537. Zhai, Y., Zhao, X., Teng, Y., Li, X., Zhang, J., Wu, J., Zuo, R., 2017. Groundwater nitrate pollution and human health risk assessment by using HHRA model in an agricultural area, NE China. Ecotoxicol. Environ. Saf. 137, 130–142. Zhang, Q., Sun, J., Liu, J., Huang, G., Lu, C., Zhang, Y., 2015. Driving mechanism and sources of groundwater nitrate contamination in the rapidly urbanized region of south China. J. Contam. Hydrol. 182, 221–230. Zhou, Z., 2015. A Global Assessment of Nitrate Contamination in Groundwater (Internship report). International Groundwater Resources Assessment Center.

nitrate sources in surface water. Environ. Pollut. 161, 43–49. Yang, Q., Li, Z., Ma, H., Wang, L., Martín, J.D., 2016. Identification of the hydrogeochemical processes and assessment of groundwater quality using classic integrated geochemical methods in the Southeastern part of Ordos basin, China. Environ. Pollut. 218, 879–888. Yidana, S.M., Banoeng-Yakubo, B., Akabzaa, T.M., 2010. Analysis of groundwater quality using multivariate and spatial analyses in the Keta basin, Ghana. J. Afr. Earth Sci. 58 (2), 220–234. Yidana, S.M., Banoeng-Yakubo, B., Aliou, A.S., Akabzaa, T.M., 2012. Groundwater quality in some Voltaian and Birimian aquifers in northern Ghana—application of mulitvariate statistical methods and geographic information systems. Hydrol. Sci. J. 57 (6), 1168–1183.

12