Estimation of some trace metal pollutants in River Atuwara southwestern Nigeria and spatio-temporal human health risks assessment

Chemosphere 239 (2020) 124770

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Estimation of some trace metal pollutants in River Atuwara southwestern Nigeria and spatio-temporal human health risks assessment PraiseGod Chidozie Emenike a, f, *, Jordan Brizi Neris b, Imokhai Theophilus Tenebe c, Chidozie Charles Nnaji d, e, Peter Jarvis f a

Department of Civil Engineering, Covenant University, Ota, Ogun State, Nigeria Department of Exact and Technological Sciences, State University of Santa Cruz, Highway Jorge Amado - Km 16, CEP 45662-900, Ilh
eus, Bahia, Brazil Ingram School of Engineering, Texas State University, San Marcos, TX, USA d Department of Civil Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria e Faculty of Engineering and Built Environment University of Johannesburg, South Africa f Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, MK43 0AL, Bedford, United Kingdom b c

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Water quality and trace metals were investigated and compared with stipulated guidelines.
Cumulative carcinogenic and noncarcinogenic risks were extremely high.
As is responsible for the carcinogenic risk experienced in more than 50% of the location analysed.
There is a probability of developing cancer in 1 out of 10 adults and in 1 out of 20 children.

a r t i c l e i n f o Article history: Received 4 March 2019 Received in revised form 29 July 2019 Accepted 4 September 2019 Available online 5 September 2019 Handling Editor: Martine Leermakers

Keywords: River atuwara Pollution Health risk assessment

a b s t r a c t Over twenty thousand persons rely on water from Atuwara River for drinking and other domestic purposes, hence the need to ascertain the human health risk inherent in such practice. Seventy-two water samples were collected from River Atuwara during the dry and wet seasons of 2018, and the concentration of heavy metals (Pb, As, Ni, Cr, Zn, Cu, and Cd) were measured using ICP-OES. A newly developed human health risk assessment method, HHRISK code was used to estimate the health risks associated with consumption of water from Atuwara River. Results obtained revealed that the concentration of heavy metals in the river was as follows: Cd < Ni < Pb < Cr < Cu < As < Zn in the wet season and Cd < Pb < Ni < Cu < Cr < As < Zn during the dry season. Principal component analysis suggested that industrial effluents, agricultural activities and base-rock interaction are responsible for pollution of Atuwara River. The cumulative hazard index (HIcum) obtained was 678.0 ± 36.8 (for adult) and 1392.0 ± 132 (for child) for non-carcinogenic risks. A cumulative carcinogenic risk (CRcum) of 1.01Ee1±5.26Ee3 and 4.96Ee2±5.05Ee3 was obtained for adult and children respectively, suggesting that up to 1 in 10 adults and 1 in 20 children may suffer from cancer over their lifetime as a result of consumption and exposure

* Corresponding author. Department of Civil Engineering, Covenant University, Ota, Ogun State, Nigeria. E-mail addresses: [email protected], praisegod. emenike@cranfield.ac.uk (P.C. Emenike), [email protected] (J.B. Neris), itt1@ txstate.edu (I.T. Tenebe), [email protected] (C.C. Nnaji), p.jarvis@ cranfield.ac.uk (P. Jarvis). https://doi.org/10.1016/j.chemosphere.2019.124770 0045-6535/© 2019 Elsevier Ltd. All rights reserved.

2 Trace metals Southwestern Nigeria

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to water from River Atuwara. These results highlight the fact that unavailability of safe drinking water in many parts of the world remains a real and persistent risk which must be tackled. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction An important environmental concern that has attracted global attention is the poor quality of many freshwater resources due to the potential detrimental impacts on human health and ecology (Emenike et al., 2016; Hanna et al., 2019). Climate change and anthropogenic effects have had a significant impact on many rivers around the world resulting in the violation of national and international water quality standards. In addition, the accelerated development, both industrialisation and urbanisation, in low and middle income nations comes with negative impacts on rivers due to the uncontrolled discharge of industrial effluents and unplanned land use (Othman et al., 2012). As a result, researchers have devised appropriate and holistic means to quantify and interpret river water quality parameters and their implications for man and the environment (Singh and Saxena, 2018). One of the key objectives of river management is the provision of ecological services, mitigation of ecological problems, and water resource allocation upon which man depends (Tedford and Ellison, 2018; Wu et al., 2019). Monitoring and assessment of river systems are embarked upon to evaluate the performance of restoration actions and to identify a decline in river health (Steward et al., 2018). However, a number of concepts have been used by different investigators to evaluate river health. Zhao et al. (2019) stated that in order to evaluate the ecological concept of river health, a comprehensive index comprising of the hydrological characteristics, water quality, and riverbank conditions must be considered. Therefore, the state of wellness of any river depends on factors such as recovery after distortion, structural and functional consistency, sediment transport, recycling of nutrients, digestion of effluents, and the ability to protect local biota, which includes its surrounding community. In agreement, Wang et al. (2019) added that the ideology of river health should include three basic elements; chemical condition, physical habitat, and biotic structure. In other words, a river can be termed “healthy” when its ecological integrity can withstand strong interactions emerging from natural processes and human activities. Many activities alter the chemical process and biological communities of river ecosystem. These include community activities; industrial operations; the use of the river for water supply; and land-use change and nature. However, to enhance and sustain river health, an accurate evaluation of its present ecological status is a prerequisite (Singh and Saxena, 2018). Cairns and McCormick (1992) identified some indicators that are needful to assess river health. They include (i) the compliance indicator and (ii) the diagnostic indicator. The compliance indicator encompasses the anomalism for allowable limits, whereas the diagnostic indicators evaluate the cause and effect of deviation from established standards. To ensure continuous water supply for basic needs and uncompromised ecological stability, the evaluation and assessment of river health are paramount. Hameed et al. (2017) reported that obtaining the physical and chemical quantities of water quality parameters is a common and simplified approach of evaluating river health. Therefore, a clearer picture of river health and ecological endowment can be visualised by evaluating its physicochemical parameters (Singh and Saxena, 2018). Environmental investigation and appraisal of river water quality

are initiated to enlighten management authorities: either by ascertaining the extent of river stability after anthropogenic reception or oftentimes, to evaluate the river's restoration tendencies (Steward et al., 2018). Thus, analysing the river water quality and risk quotient are vital tools required to support sustainable water resource management (Field et al., 2007; Norris et al., 2007). However, researchers have integrated physical description of anthropogenic quantities in rivers with health-based risk calculations to provide an in-depth explanation of river health with the aim of suggesting a sustainable solution. The risk quotient technique has been identified as a simplified means of evaluating environmental risk which includes human-health based assessment in relation to river health (Minolfi et al., 2018). Globally, pollution of river bodies by substances that are usually considered as trace elements in the environment remains a burden to the ecosystem. Some trace elements appear in rivers in elevated concentrations from industrial effluents, sewage discharge, mining operations and refining activities (Titilawo et al., 2018). In high concentrations, these substances can be problematic and cause toxicological effects, not only to aquatic habitat but to humans and plants (if absorbed) (Lokhande et al., 2012). In Nigeria, as in many other developing and even developed countries, pollution of surface water has remained a real and persistent challenge. However, some researchers have documented the pollution status of river bodies within the region (Daso and Osibanjo, 2012; Shakirat and Akinpelu, 2013; Tenebe et al., 2018; Titilawo et al., 2018). River bodies in the region, particularly in Ogun State have witnessed the indiscriminate release of pollutants (potential toxic elements) from industries, agricultural operations and domestic sewage (Adewumi et al., 2011; Ogbiye, 2011; Omole et al., 2018). This is complicated by non-enforcement of effluent standards and non-compliance of industries with existing effluent discharge standards. This study focuses on River Atuwara, which passes through several riparian communities that altogether have a population of 500,000 (National Population Commission, 2010). Over 20,000 persons rely on water from River Atuwara for drinking and other domestic purposes. The region hosts both small and large-scale industries that practice indiscriminate discharge of untreated or partially treated effluent into the river. Some of the pollutants emanate from industries engaging in soap production (2), pharmaceuticals (10), wood and wood products (3), plastic and rubber production (12), food and beverages (2), fabrication from steel, iron and other metals (3), distilleries (2), abattoir (2). Previous studies have undertaken socio-economic, physicochemical and bacteriological assessment of the River (Ogbiye, 2011; Omole et al., 2018). However, no study has investigated the human health risk associated with consumption of water from the river. It is in this regard that the current investigation was conceived; Hence, the objectives of this study was to undertake an investigation of the spatial and temporal human health risks posed by water from River Atuwara to the riparian communities that rely on it for drinking and other domestic purposes. On a global scale, this study is intended to raise awareness on the level of risks faced by riparian communities in developing countries all over the world where piped water is unavailable.

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2. Materials and methods 2.1. Narrative of the study area Ado-Odo Ota is situated within the Dahomey basin of western Nigeria. The stratigraphic formation consists of Cretaceous deposition from the Palaeocene age. It also sits on an underlay of sedimentary rock and Paleocene Ostracods fauna (Rahaman, 1976; Omatsola and Adegoke, 1981). The southern region consists of overburdened topsoil of lateric origin and humic in nature. Underneath sites a layer of mottled clay, comprising irregular strips of overlapping shale with a calcareous arrangement (Oladosu and Ogundipe, 2017). The area is characterized by two major winds arising from the Northeast and Southwest. A dry and dusty breeze originates from the Northeast while a moist and warm wind comes from the Southwest. The combination of the two wind types results in warm, rainy season occurring from April and October, whereas November to March constitutes the hot, dry season. The month of June receives the highest amount of rainfall within the region, and the highest humidity is experienced in July (Ozebo et al., 2008). To add, the region is typified by two types of vegetation; lowland rain and freshwater swamp forest. The forest features consist of a horizontal structure with different composition of trees arranged in layers. The trees at the upper layer grow as tall as 45 m and are unevenly distributed within the region. The next layer of trees found in the forest grows as much as 25 m in length (Ayanwuyi et al., 2013; Ogbiye, 2011). River Atuwara (Fig. 1) is part of the connecting rivers to River Ogun which is part of the Ogun-Oshun river basin in Ado-Odo Ota (Latitude: 6 400 57.8400 and Longitude: 3 80 52.0300 ). River Atuwara

3

originates from Igbo-Elerin community, close to Ifo. It passes through several communities that depend on it for economic reasons. In relation to the Atlantic Ocean, the River Atuwara flows to the west and joins with other neighbouring rivers up to 65 km from its origin. River Atuwara meanders through several communities in Ado-Odo Ota, Ogun State which includes Adefarasin, Agbara, Ilasa, Igbala, Odo-itele, Ekusere, Igboloye, Onirowo, Oko-Omi, Odo-Ogbe, Ijaliye, Owode-Egba, Iju, Balogun, Orente, Benja, Ewupe I and Ewupe II. Major tributaries that meet at the right arm of River Atuwara include River Balogun, Mosafejo, and River Afara-Meje. The River Atuwara is linked to a wetland that irrigates up to 60% of the basin. The initial pattern that forms the river were unplanned drainages that were waterlogged to form wetlands. 2.2. Sample collection A total of 72 river water samples were extracted from 24 locations along River Atuwara during the wet season (July 2018) and dry season (November 2018). The significant sampling variation represents the extreme condition of the two seasons in a tropical region. Samples were taken in triplicates on the site at 0.5 m depth using a water sampler and transferred to a 1-L high-density polyethene (HDPE) bottle. Before arriving at the river, the HDPE bottles were previously washed with sulphur-free detergent, immersed in 2 M HNO3 for a day, and rinsed three times with distilled water to eliminate contaminants and any trace of acid. After that, the cleansed HDPE bottles were oven-dried at 105  C for 12 h and allowed to cool for 4 h before transporting them to the site (Olusola and Ademola Festus, 2015; Titilawo et al., 2018). Two sets of water samples were collected simultaneously; one of which was used for

Fig. 1. River Atuwara and location of industries (Taken from Ogbiye, 2011).

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physicochemical analysis and the other for trace metal analysis. The water samples obtained for trace metal analysis was acidified with 2 M of HNO3 (analytical grade). The essence is to avoid precipitation and metal attraction to the walls of the container. Sensitive water quality parameters such as pH, TDS and EC were measured in situ using Hanna edge HI2030 EC/TDS/salinity meter and Hanna HI98130 probe. In addition, the water samples were further filtered with a Whatmann 0.45 mm filter paper and stored in a refrigerator at 4  C before transporting to the lab (in an icebox) for analysis. 2þ 2þ  Major cations and anions (Cl, Naþ, Kþ, HCO 3 , Mg , Ca , NO3 , 2 2 CO3 and SO4 ) were measured using HACH DR 3900 UV spectrophotometer. Regarding the acidified water samples, 25 mL of each sample was transferred into a beaker and digested with 70% HNO3 (3 mL) and evaporated to near dryness. However, during the evaporation process, the beaker was not allowed to go dry. Thereafter, an aliquot of 70% HNO3 (5 mL) was added and refluxed while the beaker was sealed with a watch glass. To achieve complete digestion, the reflux continued until there was no significant colour change in the solution. The resultant solution was further evaporated to near dryness and remained in the beaker until it cools. The remains in the beaker were rinsed with HNO3 (1 mL), transferred into a volumetric flask (25 mL), added to the graduated mark on the flask neck with Millipore™ water, stored in HDPE bottle before metal analysis. The trace metals (Cr, Ni, Cu, Zn, As, Cd and Pb) were measured by ICPeOES (Optima 5X00 DV; PerkinElmer), and the detection limits were 0.1 mg/L for Cd, 0.2 mg/L for Zn, 0.4 mg/L for Cu, 1 mg/L for Pb, 0.2 mg/L for Cr, 0.5 mg/L for Ni, and 1.0 mg/L for As. 2.3. Quality control For analytical precision, water samples were analysed in triplicate (mean value was recorded) in which the results showed the effectiveness and sensitivity of the equipment. Multiparameter equipment was calibrated at the instance of any sampling procedure. Reagents blanks and certified reference materials were used for the calibration of the equipment, and the concentration of trace elements in blank gave values < 3% of the measured concentration in the water samples. Analytical validation was achieved by the analysis of certified reference material of GSBZ50009-88 and during the analysis of each element, the wavelength with least spectral interference was selected. The recoveries of samples spiked with standards were found to range from 94.6 to 107.3%, the samples were measured in triplicates, and the relative standard deviation (RSD) was within 6% for both dry and wet season. 2.4. Data analysis After the water samples were analysed, descriptive statistics were evaluated using GraphPad Prism version 7.0 and correlation statistics were performed using JMP® 14.3.0 (data analysis software system). In order to lessen the dimensionality of the dataset, principal component and factor analysis (PC/FA) was performed with JMP® 14.3.0. 2.5. Human health risk assessment Human health risk assessment is an important tool applied in the management of the contaminated areas, as it identifies risks arising from chemical contaminants exposures and assists in risk management and contaminated areas remediation (Andrade et al., 2017; Azevedo et al., 2016). In this study, the HHRISK code which is a new and novel method developed by Neris et al. (2019) was used. The HHRISK code allows for an agile and accurate risk assessment. The major novelty of this methode is the generation of a spatiotemporal matrix for the analysis of the aggregated risk for multiple

exposure pathways and the cumulative for exposure to multiple chemicals, as well as estimation of uncertainties associated with risk calculations. It is based on a modification of equations provided by the United States Environmental Protection Agency (U.S. EPA, 2004). In this study, two routes of human contamination by different chemical species (Pb, Cd, Ni, Zn, As, Cu and Cr(VI)) were evaluated, the water intake route, whose daily intake dose was calculated by Eq (1), and the dermal contact route, whose daily absorption dose was calculated by Eq. (2) (Neris et al., 2019; Subhani et al., 2015; U.S. EPA, 2004; 1989). The risk assessment was performed for adults and children considering a residential scenario. Analysis of water concentrations in dry and wet season was realized, the spatiotemporal risk assessment was performed semiannually (Dt ¼ 0.5 y), alternating the chemical species concentrations in dry and wet seasons (see Table S1).

Dingwat


 tj ¼


 Dderwat tj ¼

Pj

C $IRw $EF$Dt i¼1 water BW$AT

Pj

C $ i¼1 water

CF $SAW $PC$ETw $EF$Dt BW$AT

(1)

(2)

where: Ding-wat is the daily intake dose (mg kg1 d1), Cwater is the chemical concentration in water (mg L1), IRw is the ingestion rate of water (L d1), EF is the exposure frequency (d y1), Dt is the time variation (y), Dder-wat is the daily absorption dose (mg kg1 d1), CF is the volumetric conversion factor (L cm3), SAw is the skin surface area available for contact with water (cm2), PC is the dermal permeability (cm h1), ETw is the water exposure time (h d1), BW is the body weight (kg) and AT is the averaging time (d). Chemical species may cause non-carcinogenic and carcinogenic effects, and the assessment of the two types of effects on human health are calculated differently. Non-carcinogenic risks appear after exposure doses exceed certain values, which vary depending on the chemical species and the exposure route. The carcinogenic risks assessment estimates the probability of a resident developing life-long cancer by being exposed to chronic carcinogenic species (Bempah and Ewusi, 2016). The non-carcinogenic hazard quotient (HQ) and the potential carcinogenic risk (CR) for each chemical species and for each exposure route were calculated using Eqs. (3) and (4) (Neris et al., 2019). Table S2 (in the supplementary material) illustrates the RfD and SF values used to perform the risk calculations.

HQ ðtÞ ¼

DðtÞ RfD

CRðtÞ ¼ DðtÞ$SF

(3)

(4)

where: D is the exposure level (or intake) for a chemical species considering a specific exposure pathway over a certain period of time, RfD is the reference dose for that chemical species, and SF is the slope factor, which converts estimated daily doses averaged over a lifetime directly in the probability of an individual developing cancer. The risk assessment was performed with more than one exposure route (n), for this reason, it was necessary to calculate the aggregated hazard index (HIagg), which is the sum of all calculated HQ for each exposure route, as shown in Eq. (5). The same can be done for the carcinogenic risk by calculating the aggregated potential carcinogenic risk (CRagg) using Eq. (6) (Neris et al., 2019).

P.C. Emenike et al. / Chemosphere 239 (2020) 124770

5

where: xi is the ith exposure parameter involved in each case, s(xi) represents the standard uncertainty of the ith parameter, and the (vF/vxi) is the partial derivate by the ith variable, also known as sensitivity coefficients (c(xi)). The U.S. EPA (1989) defined that values of HI < 0.1 imply that there are no risks, 1 > HI  0.1 the risks are low, 4 > HI  1 the risks are average and HI  4 results in high risks to human health. When the CR value exceeds 1$104 (1 in 10,000 people presents the possibility of developing cancer) there are high carcinogenic risks and are considered unacceptable. When CR are between 1$104 and 1$106 there is low risk, and for values less than 1$106 there is no risk for residents (Li et al., 2014).

 Sulphate ðSO2 4 Þ and Nitrate ðNO3 Þ are important parameters that should be considered during river water analysis. A high sulphate concentration may result in respiratory disease (Subba Rao, 1993), whereas nitrates in heightened concentration have been associated with gastric cancer, hypertension, methaemoglobinaemia and goitre (Majumdar and Gupta, 2000). In the current investigation, the sulphate concentration ranged from 10.82 mg/L to 1284.0 mg/L during the wet season and 13.29 mg/L to 1290.0 mg/L. Amongst the samples analysed, only one sample (ST7) exceeded the WHO threshold (250 mg/L). Nitrate concentration in all samples (both dry and wet season) was high; 83.3% of the samples exceeded the recommended values for drinking water (10 mg/L). Agricultural activity was the likely cause of this pollution, an observation seen elsewhere from the run-off of fertilizers (Singh et al., 2014; Kahlown et al., 2006). The concentration of chloride (Cl) varied from 3.03 mg/L to 14.92 mg/L in the wet season and 3.91 mg/L to 15.80 mg/L in the dry season. Cl concentration in the water samples were within the permissible standard set by the WHO. Furthermore, the concen tration of Naþ, Kþ, CO2 3 , and HCO3 in all samples collected from both seasons were also within the limits set by the WHO. The range  of values observed for of Naþ, Kþ, CO2 3 , and HCO3 during the wet season was 7.17e15.3 mg/L, 1.56e8.19 mg/L, 32.59e117.2 mg/L, and 213e363 mg/L respectively. Whereas in the dry season, 7.12e17.45 mg/L, 1.56e8.19 mg/L, 227e374 mg/L were recorded for Naþ, Kþ and HCO 3 respectively. Major ions such as Mg2þ and Ca2þ ranged from 8.11 mg/L to 16.80 mg/L and 45.79e139.0 mg/L respectively during the wet season. During the dry season, the range of values recorded was 9.99 mg/L to 18.68 mg/L for Mg2þ and 48.23 mg/L to 141.5 mg/L for Ca2þ. We observed that 12.5% of the samples surpassed the threshold set by the WHO for Ca2þ in water during the wet season, whereas during the dry period, the percentage increased to 20.8%. Xiao et al. (2004) noted that the interaction between river water and rocks are responsible for the accumulation of Mg2þ and Ca2þ in water. However, none of the samples exceeded the WHO standard for Mg2þ in both seasons.

3. Results and discussion

3.2. Trace element concentration

3.1. Water quality parameters

Table S3 (Supplementary Material) also presents the seasonal variation of heavy metal contents in River Atuwara. The current investigation revealed the Pb concentration ranged from 0.05 mg/L to 5.69 mg/L in the wet season and 0.05 mg/L to 2.97 mg/L in the dry season. These values are considerably high as all samples collected in both seasons surpassed the WHO threshold for Pb in water (0.01 mg/L). Specifically, Pb values recorded the highest value at ST15 during the wet season. The presence of lead (Pb) in water could originate from the use of chemical manure, gasoline additives, and pesticides (Aboud and Nandini, 2009). The concentration of Cd varied from 0.04 mg/L to 0.99 mg/L in the wet season and 0.04e1.01 mg/L in the dry season. Besides, no significant difference in Cd concentration was observed in both seasons. However, in relation to the WHO guidelines (0.003 mg/L), 100% of the Cd values exceeded the stipulated guideline in both seasons. In general, cadmium (Cd) in the environment could emanate from activities such as incineration of municipal waste, batteries (NieCd type), and flashing of fossil fuels (Rabiul Islam et al., 2017). Sewage sludge from abattoirs and fertilizer application are major occurrence around the region and may be responsible for Cd presence in River Atuwara (Adewumi et al., 2011). Copper (Cu) concentration varied between 0.185 mg/L to 28.21 in the wet season and 0.14 mg/L to 31.12 mg/L in the dry season. The highest concentration of Cu was observed at ST7 during the dry

n X

HIagg ðtÞ ¼

HQi ðtÞ

(5)

CRi ðtÞ

(6)

i¼1 n X

CRagg ðtÞ ¼

i¼1

For the final risk assessment, the sum of the risks arising from all exposure routes and from each chemical species (w) was calculated, obtaining the cumulative hazard index (HIcum) and the cumulative potential carcinogenic risk (CRcum) by Eqs. (7) and (8). The standard uncertainties of the calculated parameters were obtained using Eq. (9) (Neris et al., 2019).

HIcum ðtÞ ¼

w X

HI agg ðtÞ k

(7)

agg

(8)

$s2 ðxi Þ

(9)

k¼1

CRcum ðtÞ ¼

w X

CRk ðtÞ

k¼1

s2F ¼

 N  X vF 2 i¼1

vxi

The current research identified seasonal discrepancies in water quality parameters obtained from River Atuwara (Table S3). The pH value was slightly alkaline (average pH of 8.06) during the wet season. However, ST7 recorded an acidic pH value of 2.38 at this time. Most of the samples obtained during the wet season were within the WHO permissible limit except for ST7, ST8 and ST18. During the dry season, the pH value varied between 2.81 and 6.62. Almost 95.8% of the samples were below the recommended WHO permissible pH limits 6.5e8.5 for drinking water. According to Achary et al. (2016), low rainfall and high evaporation may cause the pH of river water to drop. Electrical conductivity (EC) of the river water samples ranged from 50.12 mS/cm to 177.2 mS/cm during the wet period and 56.94 mS/cm to 184.0 mS/cm during the dry season. Mean concentration of TDS during the dry and wet season were 572.5 and 591.6 mg/L respectively. Most samples collected were within the WHO limits for TDS in water (1000 mg/L) except for ST7 that exceeded the WHO limit in both seasons. However, a report by Dar et al. (2008) mentioned that water having a TDS > 500 mg/L may cause gastrointestinal reactions if consumed. The likely cause for the high TDS in the river was from the discharge of domestic wastes and from the use of chemical products during water treatment (Rasool et al., 2016b).

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P.C. Emenike et al. / Chemosphere 239 (2020) 124770

season Copper (Cu) is regarded as an essential trace element in most aquatic systems. As such, many organisms require it for survival. Cu helps in haemoglobin synthesis but causes organ damage when consumed in high concentration. Chromium (Cr) content at the sampled points in River Atuwara ranged between 0.27 mg/L to 4.94 mg/L in the wet season and 0.38 mg/L to 7.05 mg/L during the dry season. The concentration of Cr obtained from all samples surpassed the WHO guideline. Cr compounds are predominantly discharged into coastal rivers from anthropogenic activity (Chen et al., 2019; Rasool et al., 2016a). Achary et al. (2016) added that Cr toxicity is highly detrimental to the environment and it typically occurs in rivers adsorbed onto suspended particles. Cr is a byproduct of many industrial processes such as wood preservation, electroplating, painting, and tanning (Bluskov et al., 2005; Islam et al., 2014). The results obtained for the current study align with previous studies conducted in the region which identified that industrial and agricultural activities such as wood preservation and fertilizer application are carried out around River Atuwara (Omole, 2011). Arsenic (As) originates from sedimentary strata and occurs naturally in many groundwater sources. The occurrence of As in groundwater often results from the oxidative decomposition of sulphide materials enriched with arsenic and reduction of arsenicabsorbing minerals, especially minerals that contain iron oxide (Guo et al., 2014). Anthropogenic activities may also result in contamination of water sources, particularly surface water. Mining activities and the application of pesticides contribute to this effect (Zhang et al., 2019). In relation to River Atuwara, there has been no recorded case of arsenic mining, indicating that As presence in the river cannot be traced to mining activities. At points along the River Atuwara, close to ST12, ST16, and ST22, agricultural activities are commonplace (cultivation of vegetables). These practices entail the use of arsenic-containing pesticides such as gallium arsenide and sodium arsenite, which in turn contaminates the river with As. Previous investigations have shown that river water has better quality in the wet season than in the dry season (Febri et al., 2016). Meanwhile, other researchers have argued that the dry season is preferable because some pollutants precipitate at low water levels and reduced flow rates (Lai et al., 2017; Sunohara et al., 2016). Investigations have shown that industries discharge their wastewater illegally and covertly into the River Atuwara through subsurface discharges (Omole, 2011). This was further confirmed during sample collection. The river also receives effluents from a gin producing factory which could also act to detrimentally impact water quality and protection. High values of heavy metal concentrations were observed in the River Atuwara during the wet and dry season. The As content remained consistently high due to the constant discharge of wastewater into the river. Despite the evidence for deterioration in water quality from the indiscriminate discharge of wastewater into River Atuwara, seen here and previously (Omole, 2011) and Ogbiye (2011), no water protection or remediation scheme has been implemented. Industrial growth, intensive agriculture, and the construction of new residential buildings has introduced heavy metals into River Atuwara, thereby causing deterioration of its water quality. Comparing the mean values of heavy metals obtained from the current study with major rivers globally, they were observed to be higher than the mean values obtained from Liaohe River and Taizihe River in China (Chen et al., 2019; Gao et al., 2014), Ennore Creek in coast of India (Jayaprakash et al., 2015), Warri River in Nigeria (Wogu and Okaka, 2011), Tinto
novas et al., 2010), Ajay River basin in India River in Spain (Ca (Singh and Kumar, 2017) and lesser than that observed in River Kaduna in Nigeria (Abui et al., 2017).

3.3. Source identification of trace elements Correlation analysis and Principal component with factor analysis (PC/FA) were used to analyze the physicochemical parameters obtained in the present study. First, the correlation between the water variables in both seasons was calculated (Table 1), and it was observed that in the wet season, a strong relationship exists between TDS-EC, SO4-EC, Ni-EC, Cu-EC, Cd-EC, CO3-EC and Pb-EC.  Also, CO2 3 has a strong relationship with SO4 , Ni, Cu, Cd and Pb. In addition, Ni has a very strong relationship with Cd, Cu, and Pb, which implies that these metals originated from a similar source. Furthermore, Cu was correlated to Cd and Pb. Cr has a relationship with Ni, Cu, and Cd whereas Pb had a relationship with Ni, Cu, and Cd (Table 1). Considering these relationships, it is probable that these pollutants emanated from anthropogenic activities (Jiang et al., 2015). These compound groupings were typical of discharges associated with metallurgy, chemical disposal and electroplating (Chen et al., 2019; Emenike et al., 2018a, 2018b; Wuana and Okieimen, 2011). Arsenic did not correlate with any other metal except Kþ, indicating that its presence is as a result of agricultural activities including those of wood preservation and the application of arsenic-containing pesticides (Emenike et al., 2018a, 2018b; Wogu and Okaka, 2011). The strong relationship observed between the trace elements (Cd, Ni, Pb, and Cu), TDS, and EC infer that the metals were partly responsible for the high EC and TDS in both seasons combined with moderate relationship observed between Mg2þ and TDS. In addition, the strong relationship between TDS, SO42 and Mgþ indicates intrusion of the aquifer structure into River Atuwara (Subba Rao et al., 2019). The relationships between the trace elements in the dry season was similar to that seen in the wet season (Table 1). Cr was linked with Ni, Cu, and Cd, while Ni was correlated with Cu and Cd, and Cu was related to Cd. The results indicated that the discharge of untreated effluents into the River Atuwara is a routine activity for the industries located in the region, regardless of the season (Tenebe et al., 2019). As was not related to any trace element which could mean that agricultural activities were operated at a minimum during the dry season. Therefore, it was inferred that the pollution observed at River Atuwara originates from combined sources, such as anthropogenic and geodetic, but a higher proportion of the pollution was derived from anthropogenic activity. PC/FA was used to carry out the compositional analysis by adopting the factor method which combines varimax rotation and prior communality technique. Based on this, four factors were identified that explained the variance existing between the water quality variables for both seasons. The factors selected were those loaded with eigenvalues >1 (see Fig. 2). In this regard, the four factors selected explained 74.8% and 71.6% of the entire variance during the dry and wet season respectively. In the wet season, 49.0% of the variance was explained by factor 1 followed by 13.02% by factor 2, 7.43% by factor 3, and 2.15% by factor 4. To understand the cluster pattern and compositional association between the variables, consideration was given to factors above 0.5 (see Table 2). To begin with, factor 1 displayed high loadings from  Cu, Ni, Cd, Pb, Cr, TDS, Mg, EC, CO2 3 , and SO4 , during the wet season, probably originating from the effluents from Abattoir, metal smelting, chemical industries, distilleries, and pharmaceutical companies. The loadings of Cl, Kþ, Naþ, Caþ, and As observed in factor 2 were clustered together, supportive of the view that they share a common origin, inferring the likely use of fertilizers, pesticides, and chemical manure. The high loadings on factor 3 and factor 4, from HCO 3 respectively, tell of the hardness of River Atuwara caused by base-rock interaction. In the dry season, 45.1% of the variance was explained by factor 1, 16.8% by factor 2, 8.6% by factor 3, and 4.3% by factor 4 (Fig. 2).

Table 1 Correlation analysis of water parameters at different seasons. EC

TDS

CO23

Cl

NO 3

SO24

Ca2þ

HCO 3

Kþ

Mg2þ

Naþ

Cr

Ni

Cu

Zn

As

Cd

Pb

1 0.957 0.971 0.025 0.461 0.960 0.474 0.156 0.434 0.528 0.042 0.433 0.950 0.937 0.105 0.163 0.852 0.823

1 0.958 0.066 0.428 0.987 0.517 0.308 0.432 0.512 0.060 0.443 0.980 0.973 0.043 0.146 0.872 0.851

1 0.026 0.460 0.960 0.477 0.154 0.434 0.526 0.044 0.435 0.950 0.938 0.106 0.166 0.854 0.823

1 0.077 0.157 0.333 0.225 0.717 0.103 0.828 0.524 0.175 0.243 0.253 0.381 0.325 0.159

1 0.441 0.087 0.015 0.542 0.696 0.340 0.076 0.446 0.371 0.019 0.260 0.359 0.576

1 0.444 0.174 0.353 0.516 0.164 0.520 0.994 0.990 0.126 0.079 0.918 0.817

1 0.155 0.496 0.149 0.279 0.211 0.424 0.419 0.055 0.367 0.366 0.498

1 0.333 0.068 0.347 0.301 0.190 0.175 0.480 0.242 0.029 0.333

1 0.241 0.789 0.215 0.353 0.281 0.129 0.549 0.170 0.648

1 0.145 0.183 0.471 0.438 0.068 0.112 0.372 0.485

1 0.629 0.180 0.245 0.271 0.459 0.356 0.205

1 0.540 0.597 0.309 0.228 0.669 0.154

1 0.992 0.139 0.078 0.925 0.827

1 0.166 0.030 0.940 0.778

1 0.201 0.402 0.007

1 0.087 0.347

1 0.657

1

1 0.977 0.982 0.025 0.461 0.960 0.474 0.151 0.433 0.528 0.042 0.608 0.945 0.938 0.106 0.107 0.808 0.106

1 0.958 0.065 0.430 0.988 0.516 0.299 0.431 0.512 0.060 0.627 0.979 0.973 0.049 0.102 0.844 0.063

1 0.026 0.460 0.960 0.477 0.148 0.433 0.526 0.044 0.610 0.946 0.938 0.107 0.109 0.810 0.108

1 0.076 0.156 0.333 0.221 0.714 0.103 0.828 0.492 0.212 0.243 0.254 0.375 0.352 0.373

1 0.441 0.086 0.029 0.547 0.696 0.340 0.040 0.407 0.372 0.021 0.217 0.291 0.037

1 0.444 0.170 0.351 0.516 0.164 0.694 0.994 0.991 0.126 0.026 0.891 0.146

1 0.148 0.491 0.149 0.279 0.285 0.419 0.418 0.056 0.244 0.336 0.078

1 0.322 0.062 0.329 0.191 0.177 0.171 0.461 0.360 0.017 0.449

1 0.246 0.789 0.105 0.310 0.280 0.130 0.470 0.096 0.183

1 0.145 0.028 0.456 0.439 0.067 0.013 0.306 0.077

1 0.580 0.218 0.244 0.272 0.385 0.405 0.362

1 0.737 0.758 0.297 0.254 0.811 0.381

1 0.998 0.155 0.020 0.910 0.186

1 0.166 0.007 0.917 0.202

1 0.209 0.333 0.758

1 0.159 0.280

1 0.441

1

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pH Wet Season pH 1 EC 0.935 TDS 0.956 2CO3 0.935  Cl 0.066 NO 0.386 3 SO420.946 Ca2þ 0.524 HCO 0.243 3 Kþ 0.457 2þ Mg 0.473 þ Na 0.024 Cr 0.398 Ni 0.933 Cu 0.917 Zn 0.052 As 0.259 Cd 0.830 Pb 0.805 Dry Season pH 1 EC 0.875 TDS 0.886 CO20.874 3  Cl 0.208 NO 0.407 3 2SO4 0.863 Ca2þ 0.549 HCO 0.245 3 Kþ 0.523 Mg2þ 0.463 þ Na 0.086 Cr 0.454 Ni 0.835 Cu 0.816 Zn 0.009 As 0.186 Cd 0.682 Pb 0.052

7

8

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Fig. 2. Scree plots, percentage of factor variance and factor plot of water parameters at different seasons.

These results were consistent with those observed during the wet season for factor 1 (Table 3). Factor 2 recorded high loadings from þ Cl, Kþ, Caþ and Naþ. The high loading of HCO2 3 in factor 3, and Ca in factor 4 infers that the contamination may originate from a rock source (Subba Rao et al., 2019).

3.4. Health risk evaluation The values of HIcum and CRcum obtained for a residential scenario were much higher than the proposed safe limits defined by the U.S. EPA (U.S. EPA, 1989) for all of the sampling stations (ST) (Fig. 3). For non-carcinogenic risks, HIcum ranged from 81.3 to 678.0 for adults and from 151.1 to 1329.0 for children. For carcinogenic risks, CRcum ranged from 4.62E-3 to 1.01E-1 for adults and from 2.11E-4 to

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9

Table 2 Factor analysis of water analysis during wet and dry season. Wet Season

SO2e 4 Ni CO2e 3 EC TDS Cu Cd Pb Mg2+ Cr Zn Ca2+ Cl Na+ K+ As HCOe 3 NOe 3 pH

Dry Season Factor 1

Factor 2

Factor 3

Factor 4

0.992 0.986 0.984 0.984 0.981 0.980 0.900 0.820 0.523 0.490 0.118 0.418 e0.127 e0.135 0.369 0.101 0.178 e0.451 e0.945

e0.039 e0.062 0.144 0.143 0.073 e0.098 e0.186 0.222 0.060 e0.327 e0.244 0.725 0.704 0.690 0.591 0.495 0.222 e0.053 e0.124

0.005 0.028 e0.055 e0.053 0.121 0.022 e0.156 0.142 e0.040 e0.331 e0.466 e0.088 0.095 0.228 0.141 0.118 0.958 0.112 e0.048

0.118 0.106 e0.089 e0.092 0.131 0.120 0.158 0.092 e0.005 0.200 0.011 0.490 e0.032 e0.034 0.075 e0.025 0.008 0.030 e0.108

Table 3 Values of CR for adults and children for each exposure route. Sampling stations

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Adult

Child

CR dermal

CR ingestion

CR dermal

CR ingestion

7.26E-05 4.86E-05 1.33E-04 5.35E-05 5.74E-05 4.81E-04 2.32E-04 5.42E-04 5.90E-04 4.14E-04 1.16E-04 2.65E-04 1.60E-04 2.30E-04 4.12E-05 3.55E-04 2.70E-05 3.77E-04 6.92E-05 2.12E-04 4.47E-05 5.50E-04 3.11E-04 6.43E-05

1.24E-02 8.26E-03 2.26E-02 9.10E-03 9.77E-03 8.19E-02 3.95E-02 9.22E-02 1.00E-01 7.05E-02 1.98E-02 4.51E-02 2.73E-02 3.91E-02 7.01E-03 6.04E-02 4.59E-03 6.42E-02 1.18E-02 3.60E-02 7.60E-03 9.36E-02 5.29E-02 1.09E-02

2.20E-05 1.47E-05 4.03E-05 1.62E-05 1.74E-05 1.46E-04 7.04E-05 1.64E-04 1.79E-04 1.26E-04 3.53E-05 8.03E-05 4.87E-05 6.96E-05 1.25E-05 1.08E-04 8.18E-06 1.14E-04 2.10E-05 6.41E-05 1.35E-05 1.67E-04 9.43E-05 1.95E-05

6.08E-03 4.07E-03 1.11E-02 4.48E-03 4.81E-03 4.03E-02 1.94E-02 4.54E-02 4.94E-02 3.47E-02 9.74E-03 2.22E-02 1.34E-02 1.92E-02 3.45E-03 2.97E-02 2.26E-03 3.16E-02 5.79E-03 1.77E-02 3.74E-03 4.61E-02 2.61E-02 5.38E-03

4.96E-2 for children. Interestingly, the highest cumulative carcinogenic and non-carcinogenic risks occurred at ST 9 for both adults and children. The lowest risks cumulative carcinogenic and noncarcinogenic risks for both adults and children occurred at ST 2 and ST 17 respectively. These values of HIcum and CRcum are extremely high and suggest that 1 in 10 adults and 1 in 20 (Table 3) children are likely to develop cancer in their lifetime if they continue the use of water from Atuwara River for drinking. However, these results should be interpreted with caution because the underlying assumption in this study is that Atuwara River is the only source of drinking water available to these communities. But this is not absolutely the case in many rural and semi-urban communities people rely on a variety of sources for drinking water. A

EC CO2e 3 SO2e 4 TDS Ni Cu Cd Cr Mg2+ Pb Zn Cl Na+ K+ As HCOe 3 NOe 3 pH Ca2+

Factor 1

Factor 2

Factor 3

Factor 4

0.990 0.990 0.986 0.976 0.974 0.967 0.844 0.630 0.550 0.115 0.108 e0.091 e0.096 0.398 0.070 0.172 e0.483 e0.873 0.398

0.121 0.121 e0.104 e0.007 e0.157 e0.177 e0.330 e0.420 0.110 e0.325 e0.236 0.758 0.757 0.612 0.443 0.157 e0.144 e0.185 0.523

e0.039 e0.042 0.013 0.131 0.032 0.030 e0.119 e0.247 e0.048 e0.430 e0.455 0.119 0.232 0.158 0.287 0.972 0.135 e0.064 e0.026

0.013 0.018 0.150 0.189 0.149 0.159 0.205 0.243 e0.077 0.031 0.009 0.069 0.039 0.149 e0.008 0.032 0.110 e0.170 0.728

very substantial proportion of rural drinking water is still supplied by rainwater which does not pose as much human health risk as river water. Besides, many of the children will grow up and migrate to urban areas where drinking water sources are far better than what is currently available to them. This paints a very grim picture of the future for the riparian community not only around Atuwara River but also for other riparian communities all over the world. In general, the chemical species that contributed most to the HIcum values were As [ Cr(VI) > Cd  Pb > Zn > Ni  Cu. For ST 9, 10, 11, 12 and 23 this order changed slightly because the risks caused by Pb ions were greater than those of Cd ions, while at ST 15 and 17 the order changed to Zn [ As > Cr(VI) > Cd > Pb > Ni ¼ Cu and Cr(VI) > As > Cd > Zn > Pb > Ni ¼ Cu, respectively. Among the metal ions evaluated, As was the only one that contributed to the carcinogenic risk calculation, since the SF values of the other chemical species are not available (U.S. EPA, 2016), resulting in CR values equal to zero. In contrast, all metal ions contributed to the non-carcinogenic risks associated with the consumption of the river water. Fig. 4 illustrates the contribution of each chemical species to the HIcum value for adults and children. The results indicate that the greatest risks were caused by As present in water, except at ST 15 and 17, where the chemical species Zn and Cr (VI) were dominant. With the exception of ST 7, 15, 17 and 21, As was responsible for more than 50% of the risks in all sampling locations, while Cr(VI) was responsible for more than 10% of the HICum score for half of the study locations. These two ions are considered highly toxic to humans, resulting in high risks even at very low concentrations due to their low RfD values (U.S. EPA, 2016). The high concentration of As in the river can be attributed to the use of fertilizers and herbicides for agriculture and use of wood preservatives. At ST 7 the concentrations of all ions were much higher compared to other sampling points, resulting in higher HIagg values for Cu (24.4), Zn (44.6) and Ni (29.9) for ions considered less toxic. The high concentrations of these chemical species at ST7 can be explained by the wastewater discharged from a Gin industry just before these ST, as illustrated on the map (Fig. S1 in the supplementary material). Studies have confirmed that effluents from distilleries can contain high concentrations of lead, zinc, copper, cadmium and iron if they are not properly treated (Ale et al., 2008;

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Fig. 3. Values of a) HIcum and b) CRcum calculated for adults and children in the 24 sampling stations.

Emenike et al., 2017; Omole et al., 2018). The above-average risks obtained at STs 12, 16 and 22 can be attributed to dredging and construction activities as well as continual discharge of abattoir wastewater around these points. The lowest human health risks occurred at STs 17 and 24 which are farthest from residential areas. Except for ST 7 and ST 21, Cu and Ni ions caused low health risks for adults and children (HIagg  1), due to their low concentrations and high RfD. The low toxicity of these ions can be explained by the fact that they are essential elements for the proper functioning of the human body (Martin, 2006). However, the chemical species Zn, which is also part of this group, caused average health risks (4  HIagg > 1), because their concentrations in water were much higher compared those of Cu and Ni. The risks caused by the other evaluated ions (As, Cr(VI), Pb and Cd) were high, since HIagg  4. Fig. 5 illustrates the exposure pathways and seasonal contributions to HIcum values, while Table 3 presents the CR values for each exposure pathway. The results indicated that adult and child risks were caused predominantly by contaminated water ingestion. For water sampled at ST 22, the water ingestion exposure route was identified to be the most significant, contributing 98.9% of HIcum, while the largest contribution of the dermal contact exposure route was predicted to be associated with the water sampled from ST 17 consisting of 16.7% of the total HIcum value. Carcinogenic risks were almost entirely due to water ingestion exposure route (99.4% of CRcum), while the dermal contact risk was negligible.

3.5. Spatio-temporal dynamics of human health risk Risks for adults and children were slightly higher in the dry seasons compared to those in the wet season. This can be explained by the increase in rainfall in the wet season, resulting in dilution of the chemical species concentrations in the River Atuwara water. Other studies have reported lower concentration of contaminants in rivers during rainy season compared to dry season (Girardi et al., 2016). However, this dilution does not attenuate the associated risk of consuming the water to any reasonable degree because the result of spatio-temporal analysis suggests that even a short period of exposure presents a high degree of risk (Fig. 6). Fig. 6 further shows that the most of the sample stations had values of HIcum > 4 for both adults and children after the first 6 months of exposure. In other words, for 58% of the evaluated areas, the non-carcinogenic risks for adults were very high even after the six months of exposure. The HIcum values at STs 6, 7, 8 and 22 were higher than 10, with the highest value of 16.1 ± 5.8 and 137.3 ± 43.2 for adults and children respectively occurring at ST 8 after six months of exposure. The risk posed to children who drink the river water as indicated by HIcum is 8 times higher than the risk posed to adults who drink the same water. This clearly shows that children are exposed to greater risks when exposed to unsafe drinking water sources (Armah and Gyeabour, 2013; Duan et al., 2017; Ngole-Jeme and Fantke, 2017). For ST 1, 3, 4, 5, 11, 19 and 21 the non-carcinogenic risks found after 6 months of exposure were in the range 1 HIcum  4, reaching

P.C. Emenike et al. / Chemosphere 239 (2020) 124770

Fig. 4. Contributions of each chemical species in the final value of HIcum for a) adults and b) children.

11

Carcinogenic risks for both adults and children after 6 months of exposure were unacceptable (CRcum > 1E-4) for all STs except at ST 17, whose value was 7.75E-5 ± 2.28E-5. ST 8 presented the highest CRcum (2.42E-3 ± 8.81E-4 for adults and 5.15E-3 ± 1.65E-3 for children), where exposure to such water quality was estimated to present high chances of developing cancer by 1 in every 413 adults and I in every 194 children after just six months of contaminants exposure. Generally, the risk of health impairment resulting from the intake of contaminated water increases with length of exposure. Intermittent and inconsistent intake may allow the body time to recuperate as opposed to continual daily intake even in minute quantities. Though ST 17 presented did not present substantial risks in 6 months of exposure, the CRcum value for that station rose to an unacceptable risk level when exposure period increased to one year (CRcum ¼ 1.78E-4 ± 4.61E-5). These results demonstrate that residents who use untreated water from Atuwara River for bathing or drinking are exposed a degree of both non-carcinogenic and carcinogenic risk, even after a very short period of exposure. Adults and children living near ST 6, 7, 8, 9 and 22 are at highest risk. These risks are caused by the high concentration of toxic ions (predominantly As and Cr(VI)) present in river waters. This pollution arises from untreated, or poorly treated, wastewater discharges from distilleries and the high utilization of pesticides, fertilizers and preservative wood substances. To reduce the risks for the residents, stronger control and regulation of untreated wastewater discharged into the river from industrial sources is needed. Closer inspection and legislation on the use of herbicides or fertilizers would also be beneficial, especially for substances that may contain high concentrations of arsenic.

4. Conclusion

Fig. 5. Contributions of a) exposure routes and b) seasons for non-carcinogenic risks (HIcum) in adults and children.

a peak after 1 year of exposure. The lowest HIcum values in the first months of exposure were found at STs 2, 17 and 24 with values of 1.45 ± 0.36, 1.50 ± 0.28 and 1.72 ± 0.48 respectively for adults and 12.0 ± 2.7, 11.9 ± 2.1 and 14.3 ± 3.6 for adults. The HIcum values obtained for children are about 3 times higher than the proposed safety limit for HIcum.

According to the outcome of the current investigation, levels of heavy metals in River Atuwara surpassed the stipulated guidelines suggesting a higher degree of contamination. Pb, As, Ni, Cr, Zn, Cu, and Cd concentrations were high in most samples. PC/FA and CA analysis indicated refining and industrial/agricultural activities as the reason for contamination. Analysis of water samples that considered the different seasons revealed that pollution was higher in the dry season than in the wet season. The results also showed that the mean concentration of trace metals in the investigated region of River Atuwara followed the increasing order of Cd < Ni < Pb < Cr < Cu < As < Zn in the wet season and an increasing order of Cd < Pb < Ni < Cu < Cr < As < Zn during the dry season. The concentration of trace metals and HIcum observed at ST7 was very high. This was possibly due to the outright disposal of effluent by a distillery directly at point ST7. The non-carcinogenic risk of trace metals was elevated for both adults and children, especially at ST2 and ST9. The carcinogenic risk presents a high HIcum for adults and children respectively, implying that the modelled probability of developing cancer was 1 out of 10 adults and in 1 out of 20 children for continual exposure and consumption of River Atuwara water. Amongst the metals evaluated for carcinogenic risk, As poses a severe threat and was responsible for the carcinogenic risks observed. To this end, river management policies should be implemented with strict co-operation from industrial and agricultural operators within the region on the need to treat their wastewater so as to restore environmental balance, river rejuvenation, protection of aquatic habitat and ecosystem preservation.

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Fig. 6. Spatio-temporal analysis of HIcum for a) adults and b) children and CRcum for c) adults and d) children.

Acknowledgements The authors appreciate Mr Franklin Oranusi for the laboratory assistance and the anonymous reviewers for their constructive and insightful contributions required to bring this paper to this state. Appendix A. Supplementary data Supplementary data to this article can be found online at

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