Quantification of Metal Contaminants and Risk Assessment in Some Urban Watersheds

Adeleke Adeniyi^1*, Olumuyiwa Okedeyi², Mutiu Sowemimo¹, Kafeelah Yusuf¹, Ojo Oluwole³, Goodluck Odili³, Bernadine Ejiogu³, Iyabode Ajibade¹, Bamitale Fabiyi¹, Olubunmi Adeniji³

¹Department of Chemistry, Lagos State University, Ojo, Lagos, Nigeria.
²Department of Biochemistry, Lagos State University, Ojo, Lagos, Nigeria.
³Centre for Environmental Studies and Sustainable Development (CESSED), Lagos State University, Ojo, Lagos, Nigeria.

DOI: 10.4236/jwarp.2020.1211056                

Abstract

Contamination by heavy metals is a serious threat to aquatic systems due to their level of toxicity at elevated levels. The pollution of urban watersheds is of particular concern because of its potential impact on the watershed ecosystem and the receiving larger water bodies. This study assessed the occurrence and distribution of cadmium, copper, nickel, lead and zinc in water and sediment samples collected from three urban watersheds in Lagos, Nigeria. The concentrations of metals were determined using atomic absorption spectrometry. The health risk index (HRI) of water usage was evaluated for both adults and children. HRI for cadmium and lead in some of the watersheds recorded HRI > 1 values, a cause for health concern. The pH of water ranged from 6.48 ± 0.28 - 6.54 ± 0.47 (2016) and 6.18 ± 0.56 - 6.53 ± 0.17 (2018) respectively while, for sediments, the pH values ranged from 6.14 ± 0.48 - 6.9 ± 0.15 and 5.38 ± 0.22 - 6.4 ± 0.38 for 2016 and 2018 respectively. The levels of metals in the water samples during the 2016 sampling cycle were found to be within the World Health Organization (WHO) guideline limits for drinking water. However, the 2018 cadmium, lead and zinc concentrations for Ira-Ipaye and Akesan watersheds exceed the WHO guideline limits. Cadmium was not detected in Ira-Ipaye and Akesan 2016 sediment samples. Statistical t-test and analysis of variance (ANOVA) were used to ascertain significant differences of metals concentration in the three watersheds. The pH and metal concentration values obtained for water and sediment for the year 2016 and 2018 were non-significantly different.

Keywords

Metals, Cadmium, Lead, Watersheds, Health Risk

1. Introduction

Watersheds are important in the study and management of environmental water resources [1] [2] [3] [4]. A water resource is of no value if the quality is degraded such that it prevents the desired water uses [5] [6].

Studies have shown that atmospheric deposition, vehicular transportation-related activities and metallic building envelopes resulting from agricultural, domestic and industrial activities are among the major pollution sources of urban watersheds [7] [8]. The partitioning of sources is further complicated by source interactions [9]. The fate of these metal pollutants in the biological systems is known to be equally influenced by dissolved organic matter [10] [11] [12]. Similarly, climatic conditions have been shown to affect the transport, enrichment and bioavailability of heavy metals in watersheds [13]. Consequently, pollution sources should be more closely controlled and monitored for the purposes of enhanced water quality and ecological conservation [7] [14] [15] [16].

Healthy watersheds support environmental and ecological functions, including societal services such as water availability, flood protection, healthy aquatic food products and recreation [17] [18] [19]. Metal pollution source control is the major mitigation strategy that is in vogue in decontaminating polluted watersheds [7] [8].

Cadmium and lead have no known functions in plants, animals or humans [20] [21]. These metals have been implicated as endocrine disruptors in humans and other organisms amongst other adverse health effects [22] [23] [24] [25]. The effects of elevated levels of nickel in humans are cardiovascular diseases, dizziness, lung and nasal cancers among other debilitating side effects [26]. As important as copper and zinc are as nutrients, high doses can result in side effects like depression, gastrointestinal irritation, kidney and liver failure [24] [25] [26]. On the other hand, even at low concentrations, zinc is reported to be intolerable to aquatic organisms especially fish [27] [28].

In the vicinity of the watersheds, land use is predominantly for residential purposes, small-scale farming and refined petroleum dispensing facilities. Whereas, the potential pollutant sources are direct disposal of domestic and agricultural wastes, runoffs and vehicular traffic emissions [29] [30] [31]. Other possible non-point pollution sources are the Agbara industrial estate [32] and accidental leakages from petroleum storage facilities and pipelines [33].

The aim of this study was to evaluate the occurrence and distribution of cadmium, copper, nickel, lead and zinc in water and sediment samples of three urban watersheds; with a view to determine the extent of metal pollution exacerbation over the two-year period, 2016 to 2018, the health risk indices would also be evaluated. The outcome of this study would assist the relevant government agencies to formulate an efficient monitoring strategy for watersheds in line with global best practices.

2. Materials and Methods

Study area: Ira-Ipaye and Agboroko watersheds are located along the ever-busy Isheri-LASU expressway, in the Ojo Local Government Area, of Lagos State, Nigeria. While the Akesan watershed is situated off the Isheri-LASU expressway but in the Alimosho Local Government Area, Lagos State. The global positioning system (GPS) data are indicated in Table 1 and Table 2, and shown in Figure 1. Increasing human settlements coupled with increased commercial and agricultural activities necessitated the choice of the three watersheds. The watersheds empty into the Obadore River enroute Ishashi/Agbara Rivers.

Sampling: Composite replicate samples each of both water and sediment samples were collected randomly once a week, for the period of five weeks respectively from the watersheds, between the months of September and October (2016); January and February (2018).

Water and sediment samples were collected following the standard procedures described by America Public Health Association [34] and Department of Water Affairs and Forestry [35]. Sediment samples were air-dried and sieved using 0.45 µm mesh sieve [28] [36] [37]. The pH of the water and sediment samples was determined using the Hanna pH meter. Plastic containers were used to collect

Figure 1. Map of sampling locations.

and store water samples, with 10 ml nitric acid added immediately. The samples were transported from the field to the laboratory for analysis. The samples were kept at 5˚C in the refrigerator until they were analyzed at the laboratory [33].

The samples were prepared for metal analysis using acid digestion as described earlier [27] [33]. Five heavy metals—cadmium, copper, nickel, lead and zinc were determined in an air-acetylene flame (Atomic Absorption Spectrophotometer (AAS) Model: Buck 210 VGP).

Quality control of analytical data: High purity chemicals and reagents (purchased from Merck and Aldrich Chemical company), together with distilled—deionized water were used. Stock solutions (Merck) of 1000 mg/L of the different metals were used to prepare the calibration standards. Pre-digested water and sediment samples were spiked with metal standards in triplicate for metal recovery studies as reported earlier [27] [38]. The AAS setting and operational conditions were done in accordance with the manufacturer’s specifications, and were calibrated with analytical grade standard solutions (1000 mg/L) after appropriate dilutions.

Statistical analysis: To estimate statistically significant differences between the 2016 and 2018 samples and within the respective watersheds, t-test and ANOVA statistical analyses at p < 0.05 levels of significance were employed [39]. The values are presented in Tables 5-7 respectively.

Risk assessment: To assess the potential health risks for adults and children, health risk indicators, such as chronic daily intakes (CDI) and health risk index (HRI) were calculated for the water samples [40] [41].

CDI (mg∙kg⁻¹∙day⁻¹) = C_m∙I_w/W_b

where C_m (mg∙L⁻¹) = metal concentration in water; I_w (L∙day⁻¹) = average daily intake of water (assumed to be 2 L∙day⁻¹ for an adult and 1 L∙day⁻¹ for a child); W_b = average body weights (assumed to be 72 kg for an adult and 32.7 kg for a child). The CDI data are shown in Table 8 and Table 9.

HRI = CDI/RfD

where the oral toxicity reference dose (RfD, mg∙kg⁻¹), the values are: Cd, Cu, Ni, Pb and Zn; 1 × 10⁻³, 4 × 10⁻², 2 × 10⁻², 4 × 10⁻³, 3 × 10⁻¹ respectively. The HRI values are listed in Table 10 and Table 11.

3. Results and Discussion

3.1. Results

Table 1. Sampling locations and the result of some chemical parameters in the water samples of the three watersheds (2016).

Notes: ND, not detected; WHO, World Health Organization; GPS, Global positioning system data (latitude; longitude).

Table 2. Sampling locations and the result of some chemical parameters in the water samples of the three watersheds (2018).

Table 3. Sampling locations and the result of some chemical parameters in the sediment samples of the three watersheds (2016).

Table 4. Sampling locations and the result of some chemical parameters in the sediment samples of the three watersheds (2018).

Note: ND, not detected.

Table 5. t-test (95% confidence level) of pH and metals in 2016 vs 2018 water and sediments samples.

Note: values in parentheses are for sediments; t_tab = 2.31.

Table 6. ANOVA of metal levels in water and sediment in the three watersheds (2016).

Notes: ns, non-significant difference at p < 0.05; *significant difference at p < 0.05.

Table 7. ANOVA of metal levels in water and sediment in the three watersheds (2018).

Notes: ns, non-significant difference at p < 0.05; *significant difference at p < 0.05.

Table 8. Chronic daily intakes (CDIs, mg∙kg⁻¹∙day⁻¹) of metals through water consumption in 2016.

Note: values in parentheses are for children, others are for adults.

Table 9. Chronic daily intakes (CDIs, mg∙kg⁻¹∙day⁻¹) of metals through water consumption in 2018.

Note: values in parentheses are for children, others are for adults.

Table 10. Health risk index (HRI) of metals through water usage for 2016.

Note: values in parentheses are for children, others are for adults.

Table 11. Health risk index (HRI) of metals through water usage for 2018.

Note: values in parentheses are for children, others are for children.

3.2. Discussion

The 2016 and 2018 pH values (Table 1 and Table 2), recorded for Ira-Ipaye and Agboroko water samples were below the limit of 6.5 - 8.5 prescribed by WHO for drinking water [42]. Nevertheless, Akesan’s 2016 and 2018 values of 6.54 ± 0.47 and 6.53 ± 0.17 respectively were within the recommended WHO guideline limits. These observations are consistent with values reported earlier for the Rio Tercero reservoir, Argentina, 6.64 - 7.69 [18]. It reveals the unwholesomeness of the water from Ira-Ipaye and Agboroko watersheds [43].

The pH of the sediment samples (Table 3 and Table 4) in the three watersheds ranged from 6.14 ± 0.48 - 6.90 ± 0.15 (2016) and 5.38 ± 0.22 - 6.40 ± 0.38 (2018) respectively. Earlier workers have reported that pH is an important variable that influences the behaviour of metals in the environment [28] [44]. Nonetheless, the values recorded here are lower on average compared to the 6.23 ± 0.45 - 8.73 ± 0.20 values reported for sediments harvested from the Ogun river catchments, Ketu, Lagos, Nigeria [28].

Decreases in pH values have been found to aggravate toxicity in aquatic organisms [45]. Furthermore, pH is crucial to the partitioning of metals across components of the aquatic ecosystem, for example, it is reported that at pH > 6, nickel adsorbs/co-precipitates with iron and manganese (oxy) hydroxides and can also adsorb to suspended organic matter [46].

The concentration profile of metal in the water samples (Tales follows a decreasing concentration order: Agboroko, Zn > Cu > Ni > Pb > Cd; Akesan, Zn > Cu > Pb ~ Ni ~ Cd; Ira-Ipaye, Zn > Pb > Cu > Ni ~ Cd (2016). The trend for 2018 water samples are respectively, Zn > Cu > Pb ~ Ni ~ Cd; Pb > Zn > Cu > Cd > Ni; Zn > Pb > Cd > Cu > Ni for Agboroko, Akesan and Ira-Ipaye watersheds. As evident in Table 1 and Table 2, zinc has the highest concentration in the water samples across the watersheds for 2016, while in 2018 it was lead that the highest concentration during the sampling cycles. Nevertheless, the levels (µg/L) of cadmium, copper, nickel, lead and zinc for 2016 water samples were below the recommended guideline limits prescribed by the World Health Organization for drinking water [42].

However, cadmium, lead and zinc for 2018 (Akesan and Ira-Ipaye) were above the recommended limits (µg/L) of, 5.0, 50.0 and 5000.0 respectively. This is an indication that the water quality of two of the three watersheds under spotlight has deteriorated over the two-year period. This gives cause for concern and there is the need to ascertain the possible anthropogenic activities that could be responsible for the higher pollutant load. The upper Dandenong creek catchment watershed in Victoria, Australia, the urban watershed in California and Michigan’s Southeastern watershed experienced a similar profile of heavy metal [14] [22] [47].

In terms of occurrence and distribution, the metals in the sediments (Table 3 and Table 4) follow a decreasing concentration order, Zn > Pb > Cu > Ni > Cd for Ira-Ipaye, Agboroko and Akesan respectively (2016 samples). However, the 2018 metal profile is as follows: Zn > Pb > Cu > Cd > Ni (Ira-Ipaye); Pb > Cu > Zn > Ni ~ Cd (Agboroko); Pb > Zn > Cu > Cd > Ni (Akesan). The values reported here are found to be lower than metal concentration values (ng/g) reported for the two watersheds in Abiete-Toko gold district (southern Cameroon), Cd (20 - 231); Pb (2470 - 8220); Cu (8370 - 48,600); Ni (9150 - 686,000) and Zn (22,200 - 199,600) respectively [26]. Nevertheless, the concentrations of cadmium, copper and lead levels in Lake Greenwood (South Carolina) sediments experienced a reduction between the three intervening years of 2012 and 2015 [37]. This observation is at variance with the findings of this study. However, the relatively higher metal levels in the sediments compared to the water samples are in consonance with values reported by earlier workers in the Hrazdan River (Armenia) and Lishui River, southern China watershed systems [36] [48].

The t-test statistical analyses (Table 5) of pH and metal values obtained for water and sediments in the 2016 and 2018 sampling cycles revealed statistically non-significant differences. This is a pointer to the fact that the sources of pollution are unaltered [33]. Nevertheless, the calculated F-statistic, using analysis of variance (ANOVA) (Table 6 and Table 7) for the samples reveal statistically significant differences in the water samples for, copper, nickel and zinc (2016) and for zinc (2018).

Nevertheless, in the sediment samples, Cd, Cu, Ni, Pb and Zn are significant across board for 2016 [39]. However, only zinc was statistically significant among the 2018 samples across the three watersheds of Ira-Ipaye, Agboroko and Akesan respectively. These observations are an indication that metal contamination has become widespread in the watersheds within the two-year period of 2016 to 2018. This is a cause for concern, particularly for the health of aquatic biota in the watersheds [49] [50]. The statistically significant values could also be ascribed to the fact that the three watersheds experienced varying degrees of metal contaminants during the intervening period [43] [51].

The chronic daily intakes (CDIs) and health risk index (HRI) for water usage in 2016 and 2018 sampling cycles are shown in Tables 8-11 respectively. The HRI values recorded are <1 for cadmium, copper, nickel, lead and zinc for the 2016 water in the three locations of Ira-Ipaye, Agboroko and Akesan respectively. HRI values of <1 are an indication that the health risk associated with the water usage is of no immediate serious health consequences [33] [41] [52] [53].

The 2018 water samples indicate HRI values of cadmium for Ira-Ipaye, 9.94E+0 (adult), 10.95E+0 (children); Akesan, 7.06E+0 (adult), 7.77E+0 (children) respectively. Similarly, HRI values recorded for Ira-Ipaye, are: lead, 11.83E+0 (adult), 13.03E+0 (children); zinc 1.14E+0 (adult) and 1.25E+0 (children) respectively. Similar results for health risk for children and adults have been reported earlier for Lishui River and Shenjia watershed, China [15] [48]. These HRI values are >1.

HRI > 1 values portends adverse health effects [31] [33] [53]. These feed into the narrative expressed earlier by Bornman and co-workers on the rising levels of endocrine-disrupting heavy metal in Africa’s water ecosystems [25].

Consequent upon the findings of this study, there is a need for effective management of watersheds, through pollution source control and periodic water quality monitoring. These measures would assist in decontamination, improve functionality as well as protect the watersheds from irreversible degradation.

4. Conclusion

The occurrence of metal (Cd, Cu, Ni, Pb and Zn) in the water and sediment samples at elevated levels in the 2018 cycle is of pressing concern. This is an indication of the increasing trend of metal pollution of the watersheds. Health risk indices of cadmium and lead equally increased from HRI < 1 to HRI > 1, between 2016 and 2018. Cadmium and lead concentrations were found to be above the WHO guideline limits for drinking water. The most probable sources of metal contaminants were raw effluent discharges and municipal runoffs. The State and local government authorities are enjoined to protect these watersheds via pollution source control, by regulating the discharge of effluents into these water bodies. Unfortunately, some residents perceive these watersheds as “safe” places to empty their wastes.

Declarations

The authors have no conflict of interest or competing interests.

Acknowledgements

The authors are grateful to the Department of Chemistry, Lagos State University, Ojo for providing some of the facilities needed to complete this research. We equally appreciate the valuable assistance rendered by Dr. Wusu of Department of Mathematics, Lagos State University, Ojo and Dr. Olawale Raimi, University of Dundee, Scotland.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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