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ORIGINAL ARTICLE
Int J Env Health Eng 2017,  6:3

Water quality in relation to plankton abundance and diversity in river Ogun, Abeokuta, Southwestern Nigeria


1 Department of Aquaculture and Fisheries Management, College of Environmental Resources Management, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
2 Department of Environmental Management and Toxicology, College of Environmental Resources Management, Federal University of Agriculture, Abeokuta, Nigeria

Date of Submission16-Jun-2013
Date of Acceptance09-May-2017
Date of Web Publication27-Sep-2017

Correspondence Address:
Benjamin Onozeyi Dimowo
Department of Environmental Management and Toxicology, College of Environmental Resources Management, Federal University of Agriculture, Abeokuta
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijehe.ijehe_31_13

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  Abstract 


Aims: Plankton abundance and diversity are governed by certain aquatic environmental factors which collectively determine the health of the aquatic ecosystem. This study was aimed at investigating the water quality of lower Ogun River, Abeokuta, in relation to plankton abundance and diversity. Materials and Methods: The dataset consists of physicochemical and plankton data collected for 7 months within the period of December 2011 and June 2012 in four stations. Spatial correlations were determined between physicochemical parameters, plankton abundance, and diversity. Physicochemical parameters that exhibited strong correlation with plankton abundance and diversity were used in the calculation of a water quality index (WQI) for the protection of aquatic life. Results: Results showed highly significant correlations (P ≤ 0.05) between plankton abundance, diversity, and the physicochemical parameters monitored during the study period excluding alkalinity and phosphates. The Canadian Council of Ministers of the Environment WQI showed that the river water quality in all the sampled stations (A, 63; B, 63; C, 56; and D, 64) was marginal in classification. Conclusion: It was concluded that River Ogun is polluted beyond doubt. Hence, corrective measures should be put in place so as to prevent total ecological collapse.

Keywords: Correlation, diversity, physicochemical, plankton abundance, river Ogun, water quality index


How to cite this article:
Odulate DO, Omoniyi IT, Alegbeleye WO, George FA, Dimowo BO. Water quality in relation to plankton abundance and diversity in river Ogun, Abeokuta, Southwestern Nigeria. Int J Env Health Eng 2017;6:3

How to cite this URL:
Odulate DO, Omoniyi IT, Alegbeleye WO, George FA, Dimowo BO. Water quality in relation to plankton abundance and diversity in river Ogun, Abeokuta, Southwestern Nigeria. Int J Env Health Eng [serial online] 2017 [cited 2017 Oct 20];6:3. Available from: http://www.ijehe.org/text.asp?2017/6/1/3/215733




  Introduction Top


Rivers which are among the oldest water bodies in the world [1] are the zone of earth's highest biological diversity and also of most intense human activity.[2] Water from river sources is usually prone to pollution due to human activities around the resource. Major water quality problems stem from sewage pollution, the intensive agricultural use of fertilizers and pesticides, industrial wastes, saltwater intrusion, and soil erosion.[3] The dependency of plankton on water quality can be likened to the dependency of humans on air quality. Therefore, just as how humans will greatly be affected if the atmosphere is polluted with noxious gases, so also all aquatic life if their environment (which is water) is polluted with effluents. The quality of water is, therefore, germane to the survival and well-being of all aquatic life in the ecosystem. According to Lohdip,[4] water quality is a complex subject which involves physical, chemical, hydrological, and biological characteristics of water and their complex delicate relations. The plankton (phytoplankton and zooplankton) are the primary and secondary producers in an aquatic ecosystem and represent the biological parameters mostly measured when assessing water quality. As they (phytoplankton) are dependent on water quality parameters, especially nitrates and phosphates for optimum growth, reproduction, and survival, so also are all consumer aquatic life (zooplankton, aquatic invertebrates, fish larvae, etc.) dependent on them at least at an early stage for their nutrition. Any unfavorable change in physicochemical parameters could affect plankton yield, i.e. primary productivity and in turn bring about attendant problems (such as depletion of dissolved oxygen levels at night or supersaturation of dissolved oxygen in the day, food shortage, competition for available food, stress and death) to all consumer aquatic life. This possibility necessitates the continuous monitoring of water quality to protect aquatic life. According to Canadian Council of Ministers of the Environment (CCME),[5] the traditional practice of reporting water quality has been to produce reports describing trends and compliance with official guidelines or other objectives on a variable by variable basis. The advantage of this approach is that it provides a wealth of data and information; however, in many cases, managers and the general public have neither the inclination nor the training to study these reports in detail. One possible solution to this problem is to reduce the multivariate nature of water quality data by employing an index that will mathematically combine all water quality measures and provide a general and readily understood description of water. River Ogun is a free-to-all perennial river used without regulation for many domestic and industrial purposes and as a result is liable to pollution. Several studies have been carried out on the water quality of the river including (Diayi and Gbadebo,[6] who carried out pollution assessment of sediment metals from the middle region of the Ogun River; Ojekunle et al.,[7] who assessed the effect of commercial activities on the surface water quality of Ogun River; Onyema et al.,[8] who determined the composition, abundance, and temporal variation of phytoplankton and zooplankton at the lower part of Ogun river in Lagos in relation to water quality characteristics; Adeosun et al.,[9] who assessed the physical and chemical parameters of lower Ogun River, Akomoje, Ogun State, Nigeria; Alani et al.,[10] who carried out preliminary investigation of the state of pollution of Ogun River at Kara Abattoir, near Berger, Lagos; Iji and Adeogun,[11] who evaluated the cytotoxic effect of effluents at inducing chromosomal aberrations in Catfish [Clarias pachynema] from Ogun river; Taiwo et al.,[12] who assessed the impact of Lafenwa abattoir effluents on Ogun river course in Abeokuta, Nigeria; Olayinka et al.,[13] who assessed the physicochemical parameters, heavy metals, and bacteriological parameters of water samples of Ogun River near Lafenwa slaughterhouse in Abeokuta; Oketola et al.,[14] who assessed the water quality of River Ogun using multivariate statistical techniques; Oke et al.,[15] who mapped river water quality using inverse distance weighted interpolation in Ogun-Osun River Basin, Nigeria; Dimowo,[16] who assessed the physicochemical parameters of River Ogun in comparison with national and international standards; Olopade,[17] who assessed the water quality characteristics for aquaculture uses in Abeokuta North Local Government Area; Dimowo,[18] who reported the monthly spatial occurrence of phytoplankton and zooplankton in River Ogun, Abeokuta; Awoyemi,[19] who assessed the physical and chemical parameters of Ogun river [Opeji river], Opeji Village, Abeokuta, Ogun State, Nigeria; Osunkiyesi,[20] who carried out physicochemical analysis of water samples in Ogun River; Ikotun et al.,[21] who investigated the influence of human activities on the water quality of Ogun river in Nigeria; Murtala et al.,[22] who studied the bioaccumulation of heavy metals in fish [Hydrocynus forskahlii, Hyperopisus bebe occidentalis, and Clarias gariepinus] organs in downstream Ogun coastal water, Nigeria; Adeogun et al.,[23] who assessed the impact of abattoir and sawmill industry effluents on the physicochemical properties of upper Ogun River [Abeokuta], Nigeria; Aramide [24] and Owoade,[25] who carried out water and sediment quality assessment of River Ogun around the cattle market, Isheri along Lagos-Ibadan Express Road, Nigeria; Jaji et al.,[26] who assessed the water quality of River Ogun; Babalola,[27] who carried out pollution studies of Ogun River at Isheri along Lagos-Ibadan Express Road, Nigeria; Martins,[28] who studied the geochemical characteristics of a small drainage basin in Ogun river; and Adebisi,[29] who studied the physicochemical hydrology of Upper Ogun River, etc.). However, no study has specifically linked water quality to planktonic existence. This study is, therefore, aimed at: (i) investigating the spatial relationship between the physicochemical parameters, plankton abundance, and diversity of River Ogun and (ii) generating a water quality index (WQI) for appropriate communication of river water quality results in a nontechnical way to the public.


  Materials and Methods Top


Geography of the River

River Ogun [Figure 1] is one of the major rivers in Southwestern Nigeria with a total area of 22.4 km 2 and a fairly large flow of about 393 m 3/s during the wet season.[30] It has coordinates of 3°28“E and 8°41”N from its source in Oyo state to 3°25“E and 6°35”N in Lagos where it enters the Lagos lagoon.[31] A dry season from November to March and a wet season between April and October are the two seasons distinguishable in Ogun river basin. Mean annual rainfall ranges from 900 mm in the north to 2000 mm toward the south. The estimates of total annual potential evapotranspiration have been put between 1600 and 1900 mm.[32] The water is used for agriculture, transportation, human consumption, various industrial activities, and domestic purposes.[30],[31] It also serves as a raw material to the Ogun state water corporation which treats it before dispensing it to the public. Along its course, it constantly receives effluents from breweries, slaughterhouses, dyeing industries, tanneries, domestic wastewater,[30],[31] and locust bean processors before finally discharging to Lagos lagoon. A 100 km 2 area around River Ogun has an approximate population of 3,637,013 (0.03637 persons/m 2) and an average elevation of 336 m above the sea.[33]
Figure 1: Map of River Ogun showing the Sampling Stations. Source: Google Earth

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Dataset

Secondary data were acquired from Dimowo.[18] The dataset consists of physicochemical, plankton abundance and diversity data collected from four stations for a period of 7 months (December 2011–June 2012). The plankton species encountered during the study period have been published elsewhere. The methods of sample collection and identification are in Dimowo.[18]

Statistical analysis

For the purpose of this study, inferential statistics such as correlation analysis were employed. MS EXCEL and SPSS version 17.0 statistical packages were used for the analysis. For the calculation of the WQI, the CCME WQI was employed following CCME.[5] It is given by:

CCME WQI



where F1 represents scope and refers to the percentage of variables that do not meet their objectives at least once during the time period under consideration. It is calculated as follows:



F2 represents frequency and refers to the percentage of individual tests that do not meet objectives. It is calculated as follows:



F3 represents amplitude and refers to the amount by which failed test values do not meet their objectives. It is calculated in three steps:

  1. Calculation of excursion: Excursion is the number of times by which an individual concentration is greater than (or less than, when the objective is a minimum) the objective. This is calculated as follows:


  2. When the test value must not exceed the objective:



    For the cases in which the test value must not fall below the objective:



  3. Summation of normative excursions: Normative sum of excursions (nse) is the collective amount by which individual tests are out of compliance. This is calculated as follows:




  4. Calculation of amplitude: It is calculated by an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100. This is calculated as follows:




The divisor 1.732 normalizes the resultant values to a range between 0 and 100, where 0 represents the “worst” water quality and 100 represents the “best” water quality.[5]

For the purpose of this study, physicochemical parameters such as total alkalinity and phosphates were excluded from the calculation due to the fact that they failed to exhibit strong correlation with plankton abundance and diversity.


  Results Top


Mean values of the physicochemical and biological properties of the river are presented in [Table 1]. The CCME WQI of the various sampling stations is shown in [Figure 2].
Figure 2: The water quality index of River Ogun at the various sampling stations

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Table 1: Mean values of the physicochemical and biological properties of River Ogun, Abeokuta, over the study period (December 2011-June 2012)

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Spatial correlation between physicochemical parameters, plankton abundance and diversity

The correlation of the physicochemical parameters with plankton abundance and diversity of River Ogun [Table 2], [Table 3], [Table 4], [Table 5] revealed that:
Table 2: Pearson correlation coefficient of physicochemical parameters, plankton abundance, and diversity at Station A in River Ogun (December 2011-June 2012)

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Table 3: Pearson correlation coefficient of physicochemical parameters, plankton abundance, and diversity at Station B in River Ogun (December 2011-June 2012)

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Table 4: Pearson correlation coefficient of physicochemical parameters, plankton abundance, and diversity at Station C in River Ogun (December 2011-June 2012)

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Table 5: Pearson correlation coefficient of physicochemical parameters, plankton abundance, and diversity at Station D in River Ogun (December 2011-June 2012)

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In Station A, conductivity was negatively correlated with water temperature (r = −0.876, P ≤ 0.01) and positively correlated with total dissolved solids (TDS) (r = 0.996, P ≤ 0.01), zooplankton abundance (r = 0.842, P ≤ 0.01), and zooplankton species diversity (r = 0.829, P ≤ 0.05). TDS was negatively correlated with water temperature (r = −0.862, P ≤ 0.01) and positively correlated with zooplankton abundance (r = 0.844, P ≤ 0.01) and zooplankton species diversity (r = 0.836, P ≤ 0.01). Hardness was positively correlated with alkalinity (r = 0.94, P ≤ 0.01). Nitrates were negatively correlated with transparency (r = −0.823, P ≤ 0.05) and positively correlated with phosphates (r = 0.704, P ≤ 0.05), phytoplankton abundance (r = 0.814, P ≤ 0.05), and phytoplankton species diversity (r = 0.782, P ≤ 0.05). Zooplankton abundance was negatively correlated with water temperature (r = −0.826, P ≤ 0.05) and positively correlated with zooplankton species diversity. Phytoplankton abundance was negatively correlated to transparency (r = −0.891, P ≤ 0.01) and positively correlated to phytoplankton species diversity (r = 0.987, P ≤ 0.01). Zooplankton species diversity was negatively correlated with water temperature (r = −0.74, P ≤ 0.05) and positively correlated with hydrogen ion concentration (r = 0.814, P ≤ 0.05). Phytoplankton species diversity was negatively correlated with transparency (r = −0.873, P ≤ 0.01).

In Station B, conductivity was negatively correlated with water temperature (r = −0.725, P ≤ 0.05) and positively correlated with TDS (r = 0.998, P ≤ 0.01), hardness (r = 0.726, P ≤ 0.05), hydrogen ion concentration (r = 0.723, P ≤ 0.05), and phytoplankton abundance (r = 0.681, P ≤ 0.05). TDS was negatively correlated with water temperature (r = −0.733, P ≤ 0.05) and positively correlated with hardness (r = 0.704, P ≤ 0.05) and hydrogen ion concentration (r = 0.732, P ≤ 0.05). Hydrogen ion concentration was negatively correlated with water temperature (r = −0.798, P ≤ 0.05) and air temperature (r = −0.9, P ≤ 0.01). Nitrates were negatively correlated with transparency (r = −0.837, P ≤ 0.01) and positively correlated with phosphates (r = 0.715, P ≤ 0.05). Air temperature was positively correlated with water temperature (r = 0.876, P ≤ 0.01). Phytoplankton abundance was positively correlated with hardness (r = 0.754, P ≤ 0.05) and phytoplankton species diversity (r = 0.807, P ≤ 0.05). Phytoplankton species diversity was positively correlated with hardness (r = 0.672, P ≤ 0.05).

In Station C, TDS was positively correlated with conductivity (r = 0.999, P ≤ 0.01), hydrogen ion concentration (r = 0.745, P ≤ 0.05), zooplankton abundance (r = 0.917, P ≤ 0.01), phytoplankton abundance (r = 0.8, P ≤ 0.05), and zooplankton species diversity (r = 0.85, P ≤ 0.01). Transparency was negatively correlated with dissolved oxygen (r = −0.757, P ≤ 0.05) and hydrogen ion concentration (r = −0.706, P ≤ 0.05). Hardness was positively correlated with dissolved oxygen (r = 0.773, P ≤ 0.05). Hydrogen ion concentration was positively correlated with conductivity (r = 0.73, P ≤ 0.05), zooplankton abundance (r = 0.833, P ≤ 0.01), phytoplankton abundance (r = 0.812, P ≤ 0.05), and zooplankton species diversity (r = 0.932, P ≤ 0.01) and negatively correlated with air temperature (r = −0.807, P ≤ 0.05). Phosphates were positively correlated with nitrates (r = 0.723, P ≤ 0.05). Zooplankton abundance was positively correlated with conductivity (r = 0.907, P ≤ 0.01), phytoplankton abundance (r = 0.708, P ≤ 0.05), and zooplankton species diversity (r = 0.882, P ≤ 0.01) and negatively correlated with air temperature (r = −0.817, P ≤ 0.05). Phytoplankton abundance was positively correlated with conductivity (r = 0.803, P ≤ 0.05) and zooplankton species diversity (r = 0.924, P ≤ 0.01) and negatively correlated with air temperature (r = −0.68, P ≤ 0.05). Zooplankton species diversity was positively correlated with conductivity (r = 0.841, P ≤ 0.01) and negatively correlated with air temperature (r = −0.868, P ≤ 0.01).

In Station D, TDS was positively correlated with conductivity (r = 1, P ≤ 0.01) and hydrogen ion concentration (r = 0.972, P ≤ 0.01). Transparency was negatively correlated with dissolved oxygen (r = −0.778, P ≤ 0.05) and nitrates (r = −0.833, P ≤ 0.05). Hydrogen ion concentration was negatively correlated with water temperature (r = −0.698, P ≤ 0.05) and air temperature (r = −0.708, P ≤ 0.05) and positively correlated with conductivity (r = 0.969, P ≤ 0.01) and zooplankton abundance (r = 0.723, P ≤ 0.05). Nitrates were positively correlated with dissolved oxygen (r = 0.77, P ≤ 0.05) and phytoplankton species diversity (r = 0.72, P ≤ 0.05). Phosphates were positively correlated with hardness (r = 0.954, P ≤ 0.05). Air temperature was positively correlated with water temperature (r = 0.71, P ≤ 0.05) and negatively correlated with zooplankton abundance (r = −0.972, P ≤ 0.01), zooplankton species diversity (r = −0.828, P ≤ 0.05), and phytoplankton species diversity (r = −0.699, P ≤ 0.05). Phytoplankton abundance was negatively correlated with water temperature (r = −0.777, P ≤ 0.05) and positively correlated with phytoplankton species diversity (r = 0.773, P ≤ 0.05). Zooplankton species diversity was positively correlated with zooplankton abundance (r = 0.785, P ≤ 0.05). Phytoplankton species diversity was positively correlated with dissolved oxygen (r = 0.794, P ≤ 0.05) and zooplankton abundance (r = 0.75, P ≤ 0.05).

Water quality of River Ogun in relation to Plankton

The WQI [Figure 2] for the various stations ranged from 56 in Station C and 64 in Station D. Following the water quality ranking of CCME [Table 6],[5] all the stations of River Ogun were marginal in classification.
Table 6: Canadian Council of Ministers of the Environment Water Quality Ranking

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  Discussion Top


Shinde et al.[34] inferred from their research that positive correlation means that one parameter increases as other parameters also increase while a negative correlation means that as one parameter increases, the other parameters decrease. In this study, transparency was negatively correlated with dissolved oxygen at Stations C and D. This shows that as the transparency values got higher, the dissolved oxygen concentration of the water got lower. Nitrates were negatively correlated with transparency at Stations A, B, and D. This shows that higher concentration of nitrates was associated with lower transparency values. This result was also reported by Shinde et al.[34] Nitrates were also positively correlated with phosphates at Stations A, B, and C. This could be attributed to runoff from fertilizer and pesticide laden farms along the river. This result was also reported by Shinde et al.[34] Water temperature was negatively correlated with conductivity and TDS in Stations A and B. This shows that as the temperature of water got higher, the conductivity and TDS values got lower. This result was also reported by Shinde et al.[34] Water temperature was also positively correlated with air temperature and negatively correlated with hydrogen ion concentration (pH) at Stations B and D. This shows that water temperature increased as air temperature increased while water temperature increased as pH decreased. This result of positive correlation between air temperature and water temperature was also reported by Kareem et al.[35] pH was positively correlated with conductivity and TDS in Stations B, C, and D. This shows that as the pH got higher, the conductivity and TDS values also got higher. This statement is further reinforced by Waterose Environmental,[36] who suggested that the higher the pH measurement, then the higher the conductivity measurement which concurs with the principle that increased concentrations of ions would increase conductivity. Phytoplankton abundance was positively correlated with conductivity at Stations B and C. This result was also observed by Abowei et al.[37] Phytoplankton abundance was also positively correlated with phytoplankton species diversity at Stations A, B, and D. This shows that as the species diversity of phytoplankton got higher, the abundance also got higher. Phytoplankton species diversity was positively correlated with nitrates. This shows that as nitrates concentration increased, the diversity of phytoplankton species also increased. Zooplankton abundance was positively correlated with conductivity and TDS at Stations A and C, positively correlated with hydrogen ion concentration (pH), and negatively correlated with air temperature at Stations C and D. The result of positive correlation between zooplankton abundance and conductivity was also reported by Abowei et al.,[37] in Sombreiro River. Zooplankton abundance was also positively correlated with zooplankton species diversity at Stations A, C, and D. This shows that as the species diversity of zooplankton got higher, the abundance also got higher. Zooplankton species diversity was positively correlated with conductivity, TDS, and pH at Stations A and C and negatively correlated with air temperature at Stations C and D. More so, TDS was positively correlated with conductivity at all the sampling stations. This shows that conductivity and TDS are much related and complement each other. This statement can be further reinforced by Kareem et al.,[35] who observed the same result.


  Conclusion Top


This study showed that River Ogun is polluted beyond doubt. Hence, corrective measures should be put in place so as to prevent total ecological collapse. It is, therefore, recommended that public enlightenment programs and campaigns should be organized to educate the populace on the ills of dumping untreated wastes and effluents into the waterbody so as to change their personal behaviors and attitudes.

Acknowledgment

We appreciate and thank Mrs. Binyotubor of the National Institute for Freshwater Fisheries Research, New Bussa, Niger state, Nigeria, for her technical support. Mr. Sunday Okunade is appreciated for accepting to be the bearer of the project materials from Niger. We appreciate Mr. Moses O. Dimowo, for providing the multimeter test kit used for this project and Mrs. Dimowo for the funding. We also appreciate and thank Mr. Thomas O. Dimowo for the statistical analysis. May God uplift and be with you all.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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