Open access peer-reviewed chapter

Application of Diatom-Based Indices in River Quality Assessment: A Case Study of Lower Ogun River (Abeokuta, Southwestern Nigeria) Using Epilithic Diatoms

Written By

Adewole Michael Gbadebo, Benjamin Onozeyi Dimowo, Adewale Matthew Taiwo and Isaac Tunde Omoniyi

Submitted: 12 October 2018 Reviewed: 15 April 2019 Published: 30 May 2019

DOI: 10.5772/intechopen.86347

From the Edited Volume

Limnology - Some New Aspects of Inland Water Ecology

Edited by Didem Gökçe

Chapter metrics overview

1,248 Chapter Downloads

View Full Metrics

Abstract

Diatom indices have been extensively applied in the bioassessment of surface waters and wetlands in many countries except Nigeria. This pioneer study aimed at investigating the use of epilithic diatom-based indices in the assessment of the Ogun River quality. Water and epilithic diatom samples were collected fortnightly from four sampling stations for a period of four consecutive months (March–June 2015). Water samples were analysed for pH, temperature, electrical conductivity, total dissolved solids (TDS), dissolved oxygen, chemical oxygen demand (COD), nitrite, nitrate, ammonium, phosphate, sulphide, chloride, iron, manganese, silicate, total alkalinity, total hardness, total suspended solids (TSS), transparency and total organic carbon using standard methods. Epilithic diatom samples were collected by scraping the surfaces of rocks or stones using an 18 mm toothbrush and analysed following the standard methods. Data collected were subjected to descriptive (frequency, mean) and inferential statistics (diatom indices, Pearson correlation) using OMNIDIA and SPSS statistical packages. Results showed that the water quality of the Lower Ogun River ranged between bad and high qualities during the study period. The diatom indices (trophic diatom index (TDI), biological diatom index (IBD), generic salinity index (GSI1), generic trophic index (GTI), generic saprobity index (GTI)) were correlated with physical and chemical parameters, thereby indicating their effectiveness in water quality ranking.

Keywords

  • environmental management
  • applied ecology
  • ecosystem health
  • water quality assessment
  • surface water bioindicators

1. Introduction

Diatom-based indices are increasingly becoming important tools for the assessment of ecological conditions in lotic and lentic systems [1], and the ability to use diatoms to evaluate present and past conditions of water quality and environmental change in just about any aquatic environment has been recognized worldwide for many decades [2, 3, 4, 5, 6].

They are well favoured than other aquatic bioindicators in use during water quality assessment due to their cosmopolitan distribution and well-known ecological requirements. These features enable diatom indices developed in a geographic region to be used in other parts of the world [7]. This explains their wide usage in various water bodies in the world.

Some of the diatom indices in use presently include Descy’s index or DES [8], Sládecek’s index or SLA [9], Leclercq and Maquet’s index or LMI [10], the Watanabe index or WAT [11], the Commission of Economical Community Index or CEC [12], Schiefele and Schreiner’s index or SHE [13], Rott’s index or ROT [14], the generic diatom index or GDI [15], the Specific Pollution Sensitivity Index or SPI [16], the biological diatom index or IBD [17], the eutrophication/pollution index or EPI [18], the Artois-Picardie Diatom Index or APDI [19], the trophic diatom index or TDI [20], the Pampean Diatom Index or IDP [21] and the South African Diatom Index [22].

These indices according to Taylor et al. [23] function in the following manner: in a sample from a body of water with a particular level of determinant (e.g. salinity), diatom taxa with their optimum close to that level will be the most abundant. Therefore an estimate of the level of that determinant in the sample can be made from the average of the optima of all the taxa in that sample, each weighted by its abundance. A further refinement is the provision of an indicator value which is included to give greater weight to those taxa which are good indicators of particular environmental conditions. In practice, the first step to be completed when using diatom indices is the compilation of a list of taxa in a sample, together with their absolute abundance. The final index value is expressed as the mean of the optima of the taxa in the sample, weighted by the abundance of each taxon. The indicator value acts to further increase the influence of certain species [23, 24].

All these indices are based on the formula of Zelinka and Marvan [25] except the CEC, SHE, TDI and WAT indices [23]. The quality of running rivers has been successfully classified using epilithic diatom-based indices in Bulgaria [26].

Diatom indices have also been utilized in monitoring biotic integrity and trophic condition of aquatic ecosystems in select countries in sub-Saharan Africa [27] which include South Africa [28, 29, 30, 31, 32, 33, 34, 35], Kenya [36, 37, 38, 39], Zimbabwe [40, 41] and Zambia [42].

However, such studies have not been carried out on Nigerian water bodies. The Ogun River is a perennial water source which passes through areas of high population density [43, 44, 45]. In populated areas, river water quality is determined by human practices: deforestation, farming, industrial and domestic sewage discharge, which cause changes in colour, suspended solids, pH, temperature, nutrients and run-off characteristics [46].

Being that diatom-based biomonitoring programmes have been implemented with some success in South Africa, Kenya, Zimbabwe and Zambia [27], this study therefore investigated the use of epilithic diatom-based indices in water quality assessment of the Lower Ogun River.

Advertisement

2. Materials and methods

2.1. Description of the study area

Abeokuta is the capital and the largest city in Ogun State which is situated in the southwestern part of Nigeria [47]. Soils in Abeokuta have been characterized as being sandy, formed from sedimentary rocks, and can only support savannah vegetation. Vegetation is predominated by guinea and derived savannah [48]. The Ogun River (Figure 1) is one of the main rivers in the southwestern part of Nigeria with a total area of 22.4 km2 and a fairly large flow of about 393 m3 s−1 during the wet season [49]. It has coordinates of 3028″E and 8041”N from its source in Oyo State to 3025″E and 6035”N in Lagos where it enters the Lagos lagoon [43]. Mean annual rainfall ranges from 900 in the north to 2000 mm towards the south. The estimates of total annual potential evapotranspiration have been put between 1600 and 1900 mm [50]. The Ogun River water is used for agriculture, transportation, human consumption, various industrial activities and domestic purposes [43, 49]. 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 and domestic wastewater before finally discharging to Lagos lagoon [43, 45, 49].

Figure 1.

Map of Lower Ogun River, Abeokuta showing the sampling station.

Reports on the water quality of the Ogun River have been documented for over 30 years [51, 52]. Several studies have been reported on the water quality of the lower part of the Ogun River at Abeokuta. Among such studies (Table 1) include Ojekunle et al. [53], Adeosun et al. [54], Taiwo et al. [55], Olayinka et al. [56], Ikotun et al. [57], Awoyemi [58], Dimowo [44, 45], Osunkiyesi [59] and Adeogun et al. [60].

Parameters Present study Ojekunle et al. [53] Adeosun et al. [54] Taiwo et al. [55] Olayinka et al. [56] Ikotun et al. [57] Awoyemi [58] Dimowo [44] Osunkiyesi [59] Adeogun et al. [60]
W-T (°C) 25.85–32.3 24.3–27.5 24–30.7 26.8–27 27.87–29.5 23.7–31.7 29–31 26.9–32.1 27–32 24.5–32
TRANS (cm) 14–88.75 NA 53–100 NA NA NA 98–173 20–70 NA NA
COND (μScm−1) 127–377 NA 140–190 103.7–105 412.67–514.67 NA 150–388 99–180.5 NA 725.19–3400
TDS (mgL−1) 63.5–189 690–7000 70–95 46–48 93.33–95 NA 75–194 48.8–90.8 438–448 346.05–757.03
NO3 (mgL−1) 0.02–47.5 35–205 0.235–5.445 0.4–0.9 1.85–2.13 0.66–3.91 20.49–63.42 0.6–113.4 NA 12.28–89.43
NO2 (mgL−1) 0.03–0.4 NA NA NA NA NA NA NA 0.65–0.69 NA
PO4 (mgL−1) 1.75–28 52–250 0.02–0.75 NA 2.68–3.26 0.19–2.0 0.035–0.583 0–0.1 NA 1.85–18.62
DO (mgL−1) 5.51–6.72 0.1–8.82 4.12–5.32 5.5–6.0 3.7–4.75 3.9–7.7 1.88–5.52 2.8–7.7 NA 0–11.27
COD (mgL−1) 4–678.5 350–2500 NA NA 88.33–111.67 NA NA NA NA 181.5–1374.91
pH 8.36–9.91 6.14–7.3 7.45–9.73 7.92–7.96 6.37–7.1 6.5–7.7 6.5–7.95 7.7–9.1 7.6–7.72 5.5–8.8
TSS (mgL−1) 0–49 NA NA 79–95 2.79–5.32 52.9–107.5 NA NA 446.00–448.09 822.93–1495.47
TA (mgL−1) 3.75–10 NA 4.5–14.5 0.1–0.1 NA NA NA 4.4–17.8 42.9–43.6 NA
TH (mgL−1) 2.6–7.15 NA NA 41–50 NA NA NA 45.5–105 36.1–38.1 NA
Cl (mgL−1) 25–25 380–1990 NA NA NA 29.3–104.5 NA NA 8.98–9.86 15.33–183.58
Fe+ (mgL−1) 0.05–1.53 NA NA 0.3–0.4 1.37–1.73 0.12–2.3 NA NA 13,800–16,100 NA
Mn+ (mgL−1) 0.03–0.66 NA NA NA NA 0–1.0 NA NA 289–466 NA

Table 1.

Reports on the water quality of the Lower Ogun river at Abeokuta.

W-T = water temperature; pH = hydrogen ion concentration; COND = electrical conductivity; TDS = total dissolved solids; TRANS = water transparency; Fe+ = iron; NO2 = nitrite; NO3 = nitrate; Mn+ = manganese; SiO3 = silicate; PO4 = phosphates; Cl = chloride; TA = total alkalinity; TH = total hardness; COD = chemical oxygen demand; TSS = total suspended solids; DO = dissolved oxygen

2.2. Water sampling and analysis procedure

Water samples were collected into well-labelled sample bottles fortnightly for the period of 4 months (March–June 2015) from four sampling stations along the river. Station A was located close to the Ogun State Water Corporation, Arakanga, Ibẹrẹkodo; Station B was located close to the FADAMA III supported ferry at Agọ Ika; Station C was located just below the bridge connecting to Lafenwa at Ẹnu Gada; and Station D was located just down the road of Pepsi bus stop, Quarry Road. The physical and chemical parameters determined included pH, water temperature, electrical conductivity and total dissolved solids which were determined in situ with the use of HANNA Hi 98129 multimeter, while dissolved oxygen, chemical oxygen demand, nitrite, nitrate, ammonium, phosphate, sulphide, chloride, iron, manganese, silicate, total alkalinity, hardness, total suspended solids and total organic carbon were determined in the laboratory using standard methods [61]. Water transparency was also measured in situ using a Secchi disc.

2.3. Epilithic diatom sampling and analysis procedure

Diatom samples were collected fortnightly from four sampling stations along the river for the period of 4 months (March–June 2015). The surfaces of the rocks or stones were scraped off using an 18 mm toothbrush. The brushed areas of the stones, as well as the toothbrush covered with algae, were flushed into a plastic bowl with water. The obtained brown or greenish suspension containing the diatoms was collected and preserved with neutral formaldehyde (4%) to prevent the silica cell walls from cracking. Due to the unavailability of rocks/stones at sampling stations at certain visits, other submerged substrates such as wood material were sampled. This method was adapted from the recommendations of Martin and Fernandez [62] and Kelly et al. [63]. Thereafter, in the laboratory, the samples were mounted on microscope slides by first shaking the samples vigorously and then pipetting a drop onto the slides with the use of a dropper. The identification of the diatoms was done to the lowest taxonomic category possible under the microscope using keys of identification such as [64, 65, 66, 67]. Then enumeration was carried out using the drop count method adapted from Dhargalkar and Ingole [68]. The abundance of organisms in each sample was extrapolated from the number of organisms per drop to the number of organisms per ml by multiplying the number of organisms per drop by 20 based on the tested premise that 20 drops of the sample make 1 ml.

2.4. Statistical analysis

Descriptive statistics in the form of frequency tables and range were used in the presentation of the data. Inferential statistics such as diatom indices, viz. biological diatom index (IBD), trophic diatom index, (TDI) and generic diatom indices (GDI) (saprobity index, trophic index and salinity index) were utilized in determining the water quality status of the Ogun River. IBD was calculated using OMNIDIA free version software [69], while TDI and GDI were calculated using MS Excel spreadsheets [70] following the method adapted from Kelly et al. [20] and Van Dam [71], respectively.

2.4.1. Correlation analysis

Bivariate correlation analysis was carried out using Pearson’s product moment coefficient of correlation in SPSS to check for the relationship between the physical and chemical parameters and diatom indices. The physical and chemical parameters (except pH) and diatom abundance data were log transformed before analysis in order to achieve normal distribution.

2.4.2. Ranking of water quality using diatom-based indicators

According to Taylor et al. [23], diatom-based indicators in all cases are calculated using the formula of Zelinka and Marvan [25] except for the Commission of Economical Community Index (CEC), Schiefele and Schreiner’s index (SHE), trophic diatom index (TDI) and Watanabe index (WAT index). They have the basic form [4] given:

index = j a = 1 a j s j v j j a = 1 a j v j E1

where aj is the abundance (proportion) of species j in sample; sj is the pollution sensitivity of species j; vj is the indicator value.

The performance of the indices depends on the values given to the constants s and v for each taxon, and the values of the index range from 1 to an upper limit equal to the highest value of s. Diatom indices differ in the number of species used and in the values of s and v which have been attributed after compiling the data from literature and from ordinations [4, 72]. For all of the above indices, except TDI (maximum value of 100), the maximum value of 5 (converted to 20 by the software package OMNIDIA) indicates a high quality or pristine water resource [23].

The diatom biotic indices, viz. IBD, TDI and GDI, were interpreted following the classifications in Tables 2, 3, 4 as adapted from Kelly et al. [20] and Van Dam [71], Eloranta and Soininen [73] and Delta Environmental [74].

Index score Water quality rank Trophic status
>17 High quality Oligotrophy
15–17 Good quality Oligo-mesotrophy
12–15 Moderate quality Mesotrophy
9–12 Poor quality Mesoeutrophy
<9 Bad quality Eutrophy

Table 2.

Water quality ranking with the use of IBD.

Source: Adapted from [73]

Table 3.

TDI water quality lookup chart.

Generic salinity index Generic trophic index Generic saprobity index Water quality classes Index score
Very clean Oligotrophic Oligosaprobous I >1
Clean Oligo-/mesotrophic β-Mesosaprobous II 1–0.96
Moderate Mesotrophic α-Mesosaprobous III 0.95–0.76
Polluted Eutrophic Meso-/polysaprobous IV 0.75–0.56
Very polluted Hypereutrophic Polysaprobous V <0.56

Table 4.

Interpretation of generic diatom indices.

Source: Based on [71, 74]

Advertisement

3. Results

The weekly and monthly spatial variation in the physical and chemical parameters and the weekly variation in the epilithic diatom abundance and epilithic diatom indices of the Ogun River at Abeokuta are available as supplementary files.

3.1. Epilithic diatom composition and variation

A total of 61 epilithic diatoms (Table 5) belonging to 12 orders and 3 classes were identified in the study sites. Caloneis bacillum (3600 cells mL−1) had the highest total count followed by Coscinodiscus rothii (2300 cells ml−1) and Campylodiscus clypeus (1780 cells mL−1).

Epilithic diatom species March 2015 April 2015 May 2015 June 2015 Total count
A B C D A B C D A B C D A B C D
Melosira varians 680 80 20 640 120 80 120 20 1760
Aulacoseira granulata 40 300 60 20 60 100 20 220 200 160 100 20 1300
Cyclotella meneghiniana 40 60 60 40 340 80 100 20 100 20 860
Cyclotella stelligera 240 40 240 520
Coscinodiscus rothii 80 240 120 100 700 220 60 100 160 340 180 2300
Stephanodiscus margarae 60 60 20 60 100 300
Stephanodiscus agassizensis 60 1160 40 180 100 60 100 1700
Cyclostephanos tholiformis 40 20 60
Fragilaria capucina 40 220 20 20 300
Synedra acus 20 20 140 60 40 280
Synedra nana 20 20 40
Synedra ulna 60 1140 40 80 160 40 80 140 220 40 80 40 40 120 2280
Diatoma vulgaris 100 40 20 160
Diatoma hiemale 40 40 100 20 40 240
Diatoma tenuis 80 60 40 140 80 100 100 60 20 60 740
Staurosira construens 60 60
Meridion circulare 40 40 20 100
Tetracyclus lacustris 20 40 220 60 340
Cymbella tumida 20 20 40 20 100
Gomphonema parvulum 20 20
Gomphonema acuminatum 20 20
Gomphonema truncatum 20 20 20 60
Asterionella formosa 60 40 20 120
Amphicampa erura 40 20 60
Eunotia serpentine 60 20 40 160 60 20 360
Eunotia formica 20 80 100
Eunotia bilunaris 40 20 60
Gyrosigma cf. scalproides 20 20 40
Gyrosigma attenuatum 140 240 20 60 20 40 40 560
Navicula viridula 40 60 100
Navicula radiosa 20 500 20 540
Navicula capitoradiata 140 200 340
Navicula cryptocephala 20 140 120 20 300
Navicula spp. 60 60
Navicula cf. margalithi 280 380 660
Pinnularia viridis 40 140 40 260 180 320 980
Pinnularia cf. interrupta 20 40 60 40 40 20 40 20 280
Frustulia vulgaris 20 40 40 120 20 60 80 420 800
Sellaphora pupula 20 20
Sellaphora seminulum 40 20 60
Sellaphora bacillum 20 20 60 100
Stauroneis cf. kriegerii 20 40 60
Caloneis bacillum 3600 3600
Craticula cuspidata 140 140
Achnanthes lanceolata 40 20 40 80 40 220
Planothidium lanceolatum 20 20
Achnanthes cf. inflate 20 20
Cocconeis placentula 40 40 40 120
Nitzschia cf. acicularis 20 20
Nitzschia cf. dissipata 20 20 40
Nitzschia palea 180 180 60 40 100 560
Nitzschia intermedia 500 500
Bacillaria paradoxa 40 40
Cylindrotheca gracilis 20 20 40
Cymatopleura solea 20 20
Campylodiscus clypeus 80 120 60 20 80 200 200 520 80 420 1780
Rhopalodia gibba 20 120 20 20 180
Epithemia adnata 20 20 60 100
Epithemia sorex 180 180
Denticula subtilis 40 60 20 120
Amphora veneta 20 40 20 40 20 40 180

Table 5.

Monthly spatial variation in the count of epilithic diatoms of the Ogun River in Abeokuta (cells mL−1).

The monthly spatial variation in epilithic diatom indices (Table 6) was in the following order: Trophic diatom index was found highest in March (69.26; Station C) and lowest in June (14.52; Station C). %Motile taxa was found highest in June (31.42; Station D) and lowest in April (1.10; Station B). Biological diatom index was found highest in June (15.10; Station D) and lowest in June (7.75; Station A). Generic salinity index (GSI1) was found highest in May (20.00; Station B). It was lowest in March, April (0.00; Station C) and June (0.00; Stations B, C, D). Generic trophic index was found highest in June (9.00; Station B) and lowest in June (0.00; Stations C and D). Generic saprobity index was found highest in March (4.00; Station A) and lowest in April (0.08; Station D).

Epilithic diatom indices March 2015 April 2015 May 2015 June 2015
A B C D A B C D A B C D A B C D
TDI 60.44 60.23 69.26 46.30 21.19 56.25 37.96 68.60 15.09 18.61 25.68 47.40 66.91 43.33 14.52 41.15
%Motile taxa 15.63 9.40 12.90 3.90 10.82 1.10 7.35 1.55 15.05 10.83 2.67 5.76 3.20 8.54 8.16 31.42
Interpretation P P P M G M G P H H G M P M H M
IBD 11.47 11.79 9.04 14.90 11.19 10.08 11.12 9.17 10.08 13.36 11.70 11.04 7.75 12.04 7.77 15.10
Interpretation P P P M P P P P P M P P B M B G
GSI1 0.25 1.67 0.00 1.33 8.50 3.00 0.00 0.80 9.50 20.00 12.00 17.00 11.00 0.00 0.00 0.00
Interpretation B H B H H H B M H H H H H B B B
GTI 0.24 1.15 0.25 0.44 3.09 0.60 1.20 0.30 3.60 2.86 2.88 5.67 3.33 9.00 0.00 0.00
Interpretation B H B B H P H B H H H H H H B B
GSI2 3.00 0.30 0.20 0.67 2.83 0.25 0.75 0.08 0.75 1.54 2.50 1.42 2.40 4.50 0.40 0.14
Interpretation H B B P H B P B P H H H H H B B

Table 6.

Monthly spatial variation in the epilithic diatom indices of Ogun River, Abeokuta.

TDI = trophic diatom index; %MT = %motile taxa; IBD = biological diatom index; GSI1 = generic salinity index; GTI = generic trophic index; GSI2 = generic saprobity index; H = high quality; G = good quality; M = moderate quality; P = poor quality; B = bad quality

3.2. Water quality parameters

The physical and chemical parameters (Table 1) assessed in this study had the following ranges: Water temperature was found highest in May (32.3°C; Station A) and lowest in June (25.85°C; Station D). Water transparency was highest in April (88.75 cm; Station B) and lowest in June (14 cm; Station D). Electrical conductivity was highest in March (377 μS cm−1; Station C) and lowest in March (127 μS cm−1; Station B). Total dissolved solids was highest in March (189 mg L−1; Station C) and lowest in March (63.5 mg L−1; Station B). Nitrate was highest in April (47.5 mg L−1; Station A) and lowest in May (0.02 mg L−1; Station A). Nitrite was highest in March (0.4 mg L−1; Station D) and lowest in June (0.03 mg L−1; Stations A, B and C). Phosphate was highest in April (28 mg L−1; Station C) and lowest in June (1.75 mg L−1; Station D). Dissolved oxygen was highest in June (6.72 mg L−1; Station D) and lowest in March (5.51 mg L−1; Station A). Chemical oxygen demand was found highest in June (678.5 mg L−1; Station D) and lowest in June (4 mg L−1; Station C). pH was highest in April (9.91; Station A) and lowest in March (8.36; Station B). Total suspended solids was highest in May (49 mg L−1; Station D) and lowest in May (0 mg L−1; Station A). Total alkalinity was highest in June (10 mg L−1; Station D) and lowest in March (3.75 mg L−1; Station C). Total hardness was highest in March (7.15 mg L−1; Station D) and lowest in March (2.6 mg L−1; Station A). Chloride levels were constant throughout the study period (25 mg L−1). Iron was found highest in April (1.53 mg L−1; Station C) and lowest in June (0.05 mg L−1; Station A). Manganese was highest in May (0.66 mg L−1; Station D) and lowest in May (0.03 mg L−1; Station B).

3.3. Relationship between physical and chemical parameters and epilithic diatom indices

Table 7 shows the Pearson correlation coefficients of physical and chemical parameters and epilithic diatom indices of the Ogun River at Abeokuta. Physical and chemical parameters and epilithic diatom indices exhibited the following relationship pattern: trophic diatom index was positively correlated with silicate (r = 0.508; p < 0.05). Biological diatom index was positively correlated with nitrite (r = 0.512; p < 0.05). Generic salinity index was negatively correlated with nitrate (r = −0.554; p < 0.05). Generic trophic index was positively correlated with generic saprobity index (r = 0.716; p < 0.01). Generic saprobity index was negatively correlated with iron (r = −0.515; p < 0.05), ammonium (r = −0.513; p < 0.05) and total hardness (r = −0.502; p < 0.05).

Parameters TDI %Motile taxa IBD GSI1 GTI GSI2
LogWT 0.068 −0.416 −0.120 0.187 −0.298 −0.251
pH −0.153 −0.295 −0.268 0.383 −0.003 0.161
LogCOND 0.055 0.341 −0.035 −0.301 −0.199 −0.073
LogTDS 0.081 0.372 0.003 −0.295 −0.196 −0.070
LogTRANS −0.101 −0.495 −0.239 0.322 −0.098 −0.014
LogFe 0.261 −0.168 −0.041 −0.040 −0.148 −0.515*
LogNO2 −0.205 −0.138 0.512* 0.362 −0.031 −0.146
LogNO3 0.443 −0.228 −0.090 −0.554* −0.488 −0.236
LogMn 0.404 −0.342 −0.233 0.025 0.132 −0.107
LogNH4 0.206 −0.466 −0.046 −0.345 −0.394 −0.513*
LogSO3 0.397 −0.376 −0.276 −0.206 −0.257 −0.333
LogSiO3 0.508* 0.058 0.167 −0.470 −0.379 −00.069
LogPO4 0.109 −0.322 0.033 0.013 −0.222 −0.240
LogCl .a .a .a .a .a .a
LogTA −0.280 0.079 −0.038 0.098 0.182 0.281
LogTOC −0.490 0.161 −0.015 0.229 0.363 0.221
LogTH −0.089 −0.042 0.244 0.121 0.060 −0.502*
LogCOD 0.406 0.185 0.279 0.273 −0.106 −0.217
LogTSS 0.253 −0.099 −0.095 0.361 0.099 0.013
LogDO −0.009 0.107 0.101 −0.133 −0.210 −0.400
TDI 1 −0.181 −0.187 −0.374 −0.206 −0.130
%Motile taxa −0.181 1 0.429 −0.189 −0.162 −0.090
IBD −0.187 0.429 1 0.034 0.033 0.067
GSI1 −0.374 −0.189 0.034 1 0.424 0.225
GTI −0.206 −0.162 0.033 0.424 1 0.716**
GSI2 −0.130 −0.090 0.067 0.225 0.716** 1

Table 7.

Pearson correlation coefficients of physical and chemical parameters and epilithic diatom indices of the Ogun River at Abeokuta.

Correlation is significant at the 0.05 level (two-tailed).


Correlation is significant at the 0.01 level (two-tailed).


LogWT = log water temperature; pH = hydrogen ion concentration; LogCOND = log electrical conductivity; LogTDS = log total dissolved solids; LogTRANS = log water transparency; LogFe = log iron; LogNO2 = log nitrite; LogNO3 = log nitrate; LogMn = log manganese; Log NH4 = log ammonium; LogSO3 = log sulphide; LogSiO3 = log silicate; LogPO4 = log phosphate; LogTA = log total alkalinity; LogTOC = log total organic carbon; LogTH = log total hardness; LogCOD = log chemical oxygen demand; LogTSS = log total suspended solids; LogDO = log dissolved oxygen; TDI = trophic diatom index; IBD = biological diatom index; GSI1 = generic salinity index; GTI = generic trophic index; GSI2 = generic saprobity index

Advertisement

4. Discussion

Diatoms have been shown through research to be used as alternative/supplementary means of water quality assessment due to the specific water quality tolerance each species portend [4, 75, 76]. They are sensitive and strongly respond to physicochemical and biological changes [77]. More so, the use of diatoms in water quality assessment is cheaper than routine chemical analyses and directly shows the impact of pollution on the aquatic biota [23].

The range of values got from this study on the physical and chemical parameters was comparable with those reported by previous studies except for pH which was more basic and total hardness which was relatively lower than the range of values previously reported [44, 45, 53, 54, 55, 56, 57, 58, 59, 60].

A total of 61 diatom species were identified in this study. Caloneis bacillum emerged with the highest total count (cells mL−1) followed by Coscinodiscus rothii and Campylodiscus clypeus. The dominance of Caloneis bacillum has also been reported in Lake Tanganyika by Cocquyt [78]. Caloneis bacillum has been reported by Ali et al. [79] to be present in Upper Dilimi River in Jos. Caloneis bacillum has also been reported to be dominant by Compere [80] at the fourth sampling site of the Red Sea Hills in north-eastern Sudan.

Following the ecological indicator values reported by Van dam et al. [71], Caloneis bacillum occurs mainly in water bodies but is sometimes found in wet environments. It is a nitrogen-autotrophic taxa, tolerating very small amounts of organically bound nitrogen with fairly high oxygen requirements (>75% saturation). It is rarely found in large numbers in rivers [81]. Caloneis bacillum is alkaliphilous mainly occurring in fresh brackish waters with pH > 7, chloride levels <500 mg L−1 and salinity <0.9%. Caloneis bacillum is meso-eutraphentic and β-mesosaprobous, thereby falling under water quality class II [71]. It has also been reported to be ubiquitous [82].

The dominance of Caloneis bacillum in this study was therefore indicative of moderate pollution.

All the diatom indices (TDI, IBD, GSI1, GSI2, GTI) differed in their ranking of the water quality of the Lower Ogun River at Abeokuta. However, the generic diatom indices (GSI1, GSI2, GTI) were quite similar in their water quality ranking. This misalliance is explained by the global nature of indices, which try to evaluate the general state of water quality and not only the trophic degree [83, 84].

The trophic diatom index (TDI) showed that during the study period, the river water was in most cases poor and moderate (there was a tie in frequency of occurrence) in terms of quality. However, the biological diatom index (IBD) showed that the river water was in most cases moderate in terms of quality.

The generic salinity index (GSI1) showed that the river water was in most cases high in terms of quality during the study period. This salinity classification was calculated based on the tolerance of diatoms to salinity.

The generic trophic index (GTI) ranked the river water during the study period as being in most cases high in terms of quality. This trophic classification was calculated based on the tolerance of diatoms to the trophic state of the aquatic ecosystem. According to Naumann [85] as cited by Van dam et al. [71], variations in trophic state are usually as a result of variations in concentration of inorganic nitrogen and phosphorus compounds. There are however various concepts regarding trophic state. For this reason, water quality assessment based on trophic state was rather qualitative.

The generic saprobity index (GSI2) showed that the river water was in most cases high in terms of quality during the study period. The saprobity classification was calculated based on the indicator properties of diatoms to the presence of biodegradable organic matter and oxygen concentrations in the aquatic ecosystem [71].

Diatom species react distinctly to varying physical and chemical parameters. They are sensitive to change in nutrient concentrations, supply rates and silica/phosphate ratios. Each taxon has a specific optimum and tolerance for nutrients such as phosphate and nitrogen, and this is usually quantifiable [4].

The following deductions were made from the relationship between physical and chemical parameters and epilithic diatom indices: As trophic diatom index (TDI) scores increased, the concentration of silicates increased. This shows that the increases and decreases in concentration of silicates in the river water supported the concomitant increase and decrease in TDI scores. This result corroborated Reynolds [86] as cited by Gbadebo et al. [87] who observed that silica plays an important role in the ecology of aquatic systems as it is an essential element for diatom existence comprising 26–69% of its cellular dry weight. This study did not observe correlations between TDI and pH as observed by Tan et al. [88] in South-East Queensland River, Australia. This study did not also observe correlations between TDI and total phosphates as observed by Vilbaste [89].

It was observed that biological diatom index (IBD) increased as the concentration of nitrites increased. This shows that nitrite influenced the diatoms of the aquatic ecosystem which was evidenced in the IBD scores. Nitrites are one of the nutrients that favour the growth of diatoms. This result was supported by Kalyoncu and Şerbetci [1] who reported significant correlations between IBD, dissolved oxygen, temperature, conductivity, ammoniacal nitrogen, nitrite nitrogen and phosphate phosphorus. This result was also corroborated by Vilbaste [89] who reported significant correlations of IBD with water temperature, pH, total phosphate, nitrite nitrogen and ammoniacal nitrogen. This study however did not observe correlations between IBD and pH as observed by Tan et al. [88] in Upper Han River, China.

The lack of significant relationship between TDI, IBD, electrical conductivity and total dissolved solids was supported by the observation of Eassa [90] who reported that TDI showed no significant correlation with any physicochemical parameters and/or percentages of eutrophic species. This however did not corroborate Solak et al. [91] who reported negative correlations between TDI, electrical conductivity and total dissolved solids.

The relative abundance of motile diatom taxa in this study did not exhibit significant relationship with the physical and chemical parameters.

The relationship between the generic salinity index (GSI1) scores and the concentration of nitrates in the river water signified that increases in nitrate contributed to reduction in GSI1 scores, whereas no significant relationship was observed between generic trophic index (GTI) scores and physical and chemical parameters except with generic saprobity index (GSI2) scores. Also, generic saprobity index (GSI2) scores decreased as iron, ammonium and total hardness increased but increased with increasing GTI scores.

These results show that there was a close relationship between physical and chemical parameters and diatom-based indices. This is agreed with Bere et al. [40] who applied the indices to urban streams in Zimbabwe. The lack of significant correlation observed between electrical conductivity and the diatom indices in this study was not in agreement with the work of Stancheva et al. [26] who reported high negative correlation.

Advertisement

5. Conclusion

The diatom indices except the relative abundance of motile taxa were moderately correlated with physical and chemical parameters indicating their effectiveness in water quality ranking.

The water quality of the Ogun River during the study period as elucidated from the diatom indices ranged between bad and high qualities. Trophic diatom index (TDI) served as an indicator of silicates, biological diatom index (IBD) was an indicator of nitrites, generic salinity index (GSI1) was an indicator of nitrates and generic saprobity index (GSI2) was an indicator of iron, ammonium and total hardness. It can be concluded that trophic diatom index, biological diatom index, generic salinity index and generic saprobity index can be utilized in water quality assessment.

Limitations on the use of diatom indices in Nigeria include insufficient information on the autecology of diatom species in Nigeria. It is therefore recommended that the ecology of diatoms in Nigeria should be studied in detail in order to provide information on taxonomy, nomenclature, autecology, sensitivities and tolerance levels of diatoms to pollution in Nigerian waters. Also, diatom keys, identification guides and diatom-based indices specific to water bodies in Nigeria should be developed just as is done in other regions of the world.

Advertisement

Acknowledgments

We acknowledge the Departments of Aquaculture/Fisheries Management and Environmental Management/Toxicology at the Federal University of Agriculture, Abeokuta, Nigeria, for letting us use the research facilities. We are deeply grateful to Prof. (Mrs) M.O. Kadiri of the University of Benin, Nigeria, and Dr. J.C. Taylor of the Northwest University, South Africa, for their support in terms of research materials. We also acknowledge the many unnamed persons that rendered support.

Advertisement

Conflict of interest

The authors declare no conflicts of interest.

References

  1. 1. Kalyoncu H, Şerbetci B. Applicability of diatom-based water quality assessment indices in Dari stream, Isparta-Turkey. International Journal of Environmental, Ecological, Geological and Mining Engineering. 2013;7(6):191-199
  2. 2. Chessman BC. Diatom flora of an Australian river system: Spatial patterns and environmental relationships. Freshwater Biology. 1986;16:805-819
  3. 3. Whitmore TJ. Florida diatom assemblages as indicators of trophic state and pH. Limnology and Oceanography. 1989;34(5):882-895
  4. 4. Harding WR, Archibald CGM, Taylor JC. The relevance of diatoms for water quality assessment in South Africa: A position paper. Water SA. 2005;31(1):41-46
  5. 5. Ács É, Borics G, Fehér G, Kiss KT, Reskóné NM, Stenger-Kovác CS, et al. Implementation of the European water framework directive to assessment the water quality of Hungarian running waters with diatoms. In: Van Dam, Cadee, editors. Proceedings of the Joint Meeting of the Dutch-Flemish Society of Diatomists (NVKD) and 3rd Central European Diatom Meeting (CE-DiatoM) 26-29 March 2009. Diatomededelingen 33. 2009. pp. 29-32
  6. 6. Gottschalk S, Kahlert M. Littoral diatoms as indicators for water quality in Swedish lakes. In: Van Dam, Cadee, editors. Proceedings of the Joint Meeting of the Dutch-Flemish Society of Diatomists (NVKD) and 3rd Central European Diatom Meeting (CE-DiatoM) 26-29 March. 2009. Diatomededelingen 33. 2009. pp. 58-58
  7. 7. Alvarez-Blanco I, Cejudo-Figueiras C, Blanco S, Hernández N, Bécares E. Autoecology of epilithic diatoms in the Duero rivers basin. In: Van Dam, Cadee, editors. Proceedings of the Joint Meeting of the Dutch-Flemish Society of Diatomists (NVKD) and 3rd Central European Diatom Meeting (CE-DiatoM) 26-29 March 2009. Diatomededelingen 33. 2009. pp. 33-33
  8. 8. Descy JP. A new approach to water quality estimation using diatoms. Nova Hedwigia. 1979;64:305-323
  9. 9. Slàdecek V. Diatoms as indicators of organic pollution. Acta Hydrochimica et Hydrobiologica. 1986;14(5):555-566
  10. 10. Leclerq L, Maquet B. Two new chemical and diatomic indices of running water quality, Application to Samson and its tributaries (Belgian Meuse basin), Comparison with other chemical, biocenotic and diatomic indices, Royal Institute of Natural Sciences of Belgium. Work documents, Belgium. 1987;38:1-113
  11. 11. Watanabe T, Asai K, Houki A. Numerical simulation of organic pollution in flowing waters. Encyclopedia of Environmental Control Technology. 1990;4:251-281
  12. 12. Descy JP, Coste M. A test of methods for assessing water quality based on diatoms. Verhandlungen des Internationalen Verein Limnologie. 1991;24:2112-2116
  13. 13. Schiefele S, Schreiner C. The use of diatoms for monitoring nutrient enrichment, acidification and impact of salt in rivers in Germany and Austria. In: Witton BA, Rott E, Friedrich G, editors. Use of Algae for Monitoring Rivers. Universität Innsbruck: Institut für Botanik; 1991. pp. 103-110
  14. 14. Rott E. Methodological aspects and perspectives in the use of periphyton for monitoring and protecting rivers. In: Witton BA, Rott E, Friedrich G, editors. Use of Algae for Monitoring Rivers. Universität Innsbruck: Institut für Botanik; 1991. pp. 9-16
  15. 15. Coste M, Ayphassorho. Étude de la qualité Des Eaux du Bassin Artois-Picardie à l’aide Des communautés de diatomées Benthiques (Application Des Indices Diatomiques). Rapport CEMAGREF. Bordeaux, Douai: Agence de l’Eau Artois-Picardie; 1991
  16. 16. CEMAGREF. Etude Des méthodes Biologiques Quantitatives d’appréciation de la qualité Des Eaux. Pierre-Bénite: Rapport Division Qualité des Eaux Lyon–Agence Financière de Bassin Rhône-Méditerranée-Corse; 1982
  17. 17. Lenoir A, Coste M. Development of a practical diatom index of overall water quality applicable to the French National Water Board net work. In: Whitton BA, Rott E, editors. Use of Algae for Monitoring Rivers II. Universität Innsbruck: Institut für Botanik; 1996. pp. 29-43
  18. 18. Dell’Uomo A. Assessment of water quality of an Apennine river as a pilot study. In: Whitton BA, Rott E, editors. Use of Algae for Monitoring Rivers II. Universität Innsbruck: Institut für Botanik; 1996. pp. 65-73
  19. 19. Prygiel J, Lévêque L, Iserentant R. Un nouvel in dice diatomique pratique pour l’évaluation de la qualité des eaux en réseau de surveillance. Revue des Sciences de l'Eau. 1996;1:97-113
  20. 20. Kelly MG, Adams C, Graves AC, Jamieson J, Krokowski J, Lycett EB, et al. The trophic diatom index: A user’s manual, revised edition, R&D technical report, E2/TR2. Environment Agency. 2001. 135pp
  21. 21. Gómez N, Licursi M. The Pampean diatom index (IDP) for assessment of rivers and streams in Argentina. Aquatic Ecology. 2001;35:173-181
  22. 22. Harding WR, Taylor JC. The South African Diatom Index (SADI): A preliminary index for indicating water quality in rivers and streams in Southern Africa. In: Report to the Water Research Commission. 2011. 113pp
  23. 23. Taylor JC, Harding WR, Archibald CGM, Rensburg L. Diatoms as indicators of water quality in the Jukskei-Crocodile river system in 1956 and 1957, a re-analysis of diatom count data generated by BJ Cholnoky. Water SA. 2005;31(2):237-246
  24. 24. Kelly MG. Use of community-based indices to monitor eutrophication in European rivers. Environmental Conservation. 1998;25:22-29
  25. 25. Zelinka M, Marvan P. Zur Präzisierung der biologischen Klassifikation der Reinheit fliessender Gewässer. Archiv für Hydrobiologie. 1961;57:389-407
  26. 26. Stancheva R, Ivanov P, Mancheva A. Application of epilithic diatoms in water quality assessment of the rivers vit and Osum, Bulgaria. In: Van Dam, Cadee, editors. Proceedings of the Joint Meeting of the Dutch-Flemish Society of Diatomists (NVKD) and 3rd Central European Diatom Meeting (CE-DiatoM) 26-29 March. 2009. Diatomededelingen 33. 2009. pp. 114-118
  27. 27. Dalu T, Froneman PW. Diatom-based water quality monitoring in southern Africa: Challenges and future prospects. Water SA. 2016;42(4):551-559
  28. 28. Schoeman FR. Diatoms as indicators of water quality in the upper Hennops River. Journal of the Limnological Society of Southern Africa. 1979;5:73-78
  29. 29. Schoeman FR, Haworth EY. Diatom as indicator of pollution. In: Proceedings of the 8th International Diatom Symposium, Koeltz Scientific Books, Koenigstein. 1986. pp. 757-766
  30. 30. Pieterse AJH, Van Zyl JM. Observations on the relation between phytoplankton diversity and environmental factors in the Vaal River at Balkfontein, South Africa. Hydrobiologia. 1988;169:199-207
  31. 31. De La Rey PA, Taylor JC, Laas A, Van Rensburg L, Vosloo A. Determining the possible application value of diatoms as indicators of general water quality: A comparison with SASS 5. Water SA. 2004;30:325-332
  32. 32. De La Rey PA, Roux H, Van Rensburg L, Vosloo A. On the use of diatom-based biological monitoring. Part 2: A comparison of the response of SASS 5 and diatom indices to water quality and habitat variation. Water SA. 2008;34:61-70
  33. 33. Harding WR, Archibald CGM, Taylor JC, Mundree S. The South African diatom collection: An appraisal and overview of needs and opportunities. In: WRC Report No. TT 242/04. Pretoria: Water Research Commission; 2004
  34. 34. Taylor JC, Janse Van Vuuren MS, Pieterse AJH. The application and testing of diatom-based indices in the Vaal and Wilge rivers, South Africa. Water SA. 2007;33:51-60
  35. 35. Taylor JC, Prygiel J, Vosloo A, De La Rey PA, Van Rensburg L. Can diatom-based pollution indices be used for biomonitoring in South Africa? A case study of the Crocodile West and Marico water management area. Hydrobiologia. 2007;592:455-464
  36. 36. Ndiritu GG, Gichuki NN, Kaur P, Triest L. Characterization of environmental gradients using physico-chemical measurements and diatom densities in Nairobi River, Kenya. Aquatic Ecosystem Health & Management. 2003;6:343-354
  37. 37. Ndaruga AM, Ndiritu GG, Gichuki NN, Wamicha WN. Impact of water quality on macroinvertebrates assemblages along a tropical stream in Kenya. African Journal of Ecology. 2004;42:208-216
  38. 38. Ndiritu GG, Gichuki NN, Triest L. Distribution of epilithic diatoms in response to environmental conditions in an urban tropical stream, Central Kenya. Biodiversity and Conservation. 2006;15:3267-3293
  39. 39. Triest L, Lung’ayia H, Ndiritu G, Beyene A. Epilithic diatoms as indicators in tropical African rivers (Lake Victoria catchment). Hydrobiologia. 2012;695:343-360
  40. 40. Bere T, Mangadze T, Mwedzi T. The application and testing of diatom-based indices of stream water quality in Chinhoyi town, Zimbabwe. Water SA. 2014;40(3):503-512
  41. 41. Bere T, Mangadze T. Diatom communities in streams draining urban areas: Community structure in relation to environmental variables. Tropical Ecology. 2014;55:271-281
  42. 42. Lang P, Taylor JC, Bertolli L, Lowe S, Dallas H, Kennedy MP, et al. Proposed procedure for the sampling, preparation and analysis of benthic diatoms from Zambian rivers: A bioassessment and decision support tool applicable to freshwater ecoregions in tropical southern Africa. In: Southern African River assessment scheme (SAFRASS). AFS/2009/219013. ACP Science and Technology Programme, Cape Town. 2012
  43. 43. Ayoade AA, Sowunmi AA, Nwachukwu HI. Gill asymmetry in Labeo ogunensis from Ogun River, Southwest Nigeria. Revista de Biología Tropical. 2004;52(1):171-175
  44. 44. Dimowo BO. An assessment of the physico-chemical parameters and plankton composition of Ogun River, Southwest Nigeria. In: BSc. Report. Abeokuta, Ogun State, Nigeria: Federal University of Agriculture; 2012. 97 pp
  45. 45. Dimowo BO. Monthly spatial occurrence of phytoplankton and zooplankton in River Ogun, Abeokuta, Ogun State, Southwest Nigeria. International Journal of Fisheries and Aquaculture. 2013;5(8):193-203
  46. 46. Silva-Benavides A. The use of water chemistry and benthic diatom communities for qualification of a polluted tropical river in Costa Rica. Revista De Biologia Tropical. 1995;44(2):395-416
  47. 47. National Bureau of Statistics (NBS). State Information (Online). www.nigerianstat.gov.ng/information/details/Ogun (25 Jan. 2015); 2012
  48. 48. Online Nigeria, 2016. Physical setting of Ogun state (online). Available from: http://links.onlinenigeria.com/ogunadv.asp?Blurb=337 [20 March 2016]
  49. 49. Oketola AA, Osibanjo O, Ejelonu BC, Oladimeji YB, Damazio OA. Water quality assessment of river Ogun around the cattle market of Isheri. Nigerian Journal of Applied Science. 2006;6:511-517
  50. 50. Bhattacharya AK, Bolaji GA. Fluid flow interactions in Ogun River, Nigeria. International Journal of Research and Reviews in Applied Sciences. 2010;2(2):173-180
  51. 51. Adebisi AA. The physico-chemical hydrology of tropical seasonal river—Upper Ogun River. Hydrobiology. 1981;79:157-165
  52. 52. Martins O. The Ogun River: Geochemical characteristics of a small drainage basin. Hydrological Processes. 1987;64:475-482
  53. 53. Ojekunle ZO, Ufoegbune GC, Oyebamiji FF, Sangowusi RO, Taiwo AM, Ojekunle VO. Assessment of the effect of commercial activities on the surface water quality of Ogun River, Nigeria. Merit Research Journal of Environmental Science and Toxicology. 2014;2:196-204
  54. 54. Adeosun FI, Akin-Obasola BJ, Oyekanmi FB, Kayode JO. Physical and chemical parameters of lower Ogun River Akomoje, Ogun State, Nigeria. Fisheries and Aquaculture Journal. 2014;5:88-98
  55. 55. Taiwo AG, Adewunmi AR, Oseni OA, Ajayi JO, Lanre-Iyanda YA. Physico-chemical and microbial analysis of the impact of abattoir effluents on Ogun River course. International Journal of ChemTech Research. 2014;6(5):3083-3090
  56. 56. Olayinka OO, Adedeji OH, Oladeru IB. Water quality and bacteriological assessment of slaughterhouse effluent on urban river in Nigeria. Journal of Applied Sciences in Environmental Sanitation. 2013;8(4):277-286
  57. 57. Ikotun OO, Olafusi OS, Quadri HA, Bolarinwa OA. Influence of human activities on the water quality of Ogun River in Nigeria. Civil and Environmental Research. 2012;2:36-48
  58. 58. Awoyemi AO. Physical and chemical parameters of Ogun river (Opeji river), Opeji Village, Abeokuta, Ogun State, Nigeria. In: BSc. Report. Abeokuta, Ogun State, Nigeria: Federal University of Agriculture; 2012
  59. 59. Osunkiyesi AA. Physico-chemical analysis of Ogun River (water samples) within two locations (Akin Olugbade and Lafenwa) in Abeokuta, Ogun State, Nigeria. IOSR Journal of Applied Chemistry. 2012;1(4):24-27
  60. 60. Adeogun AO, Chukwuka AV, Ibor OR. Impact of Abattoir wastes and saw-mill effluents on water quality of Upper Ogun River (Abeokuta). American Journal of Environmental Services. 2011;7(6):525-530
  61. 61. Merck. Spectroquant Nova 60 Operating manual. Darmstadt, Germany: Merck KGaA; 2014. 297 pp
  62. 62. Martin G, Fernandez M. Diatoms as indicators of water quality and ecological status: Sampling, analysis and some ecological remarks. In: Voudouris, editor. Ecological Water Quality–Water Treatment and Reuse. United Kingdom: InTech; 2012. pp. 183-204
  63. 63. Kelly MG, Penny CJ, Whitton BA. Comparative performance of benthic diatom indices used to assess river water quality. Hydrobiologia. 1995;302:179-188
  64. 64. Edmondson WT, editor. Freshwater Biology. New York: John Wiley Sons Inc; 1959. ISBN: 0-471-23298-X
  65. 65. Gell PA, Sonneman JA, Reid MA, Illman MA, Sincock AJ. An illustrated key to common diatom genera from Southern Australia. In: Identification Guide No. 26. Cooperative Research Centre for Freshwater Ecology; 1999. 63 pp
  66. 66. Biggs BJF, Kilroy C. Stream Periphyton Monitoring Manual. New Zealand: NIWA–New Zealand Ministry for the Environment; 2000. 246 pp
  67. 67. Janse van vuuren S, Taylor J, Van Ginkel C, Gerber A. Easy identification of the most common freshwater algae: A guide for the identification of microscopic algae in South African freshwaters. IBM Corporation. 2006;2011
  68. 68. Dhargalkar VK, Ingole BS. Measurement of Biomass; Phytoplankton Identification Manual. NIWA–New Zealand Ministry for the Environment; 2004. pp. 25-26
  69. 69. Lecointe C, Coste M, Prygiel J. OMNIDIA: Software for taxonomy, calculation of diatom indices and inventories management. Hydrobiologia. 1993;269(270):509-513
  70. 70. Microsoft Corporation. Microsoft Excel, Version 2007, Microsoft Office Suite, DVD; 2006
  71. 71. Van Dam H, Mertens A, Sinkeldam J. A coded checklist and ecological indicator values of freshwater diatoms from the Netherlands. Netherlands Journal of Aquatic Ecology. 1994;28(1):117-133
  72. 72. Prygiel J, Coste M. The assessment of water quality in the Artois-Picardie water basin (France) by the use of diatom indices. Hydrobiologia. 1993;269(279):343-349
  73. 73. Eloranta P, Soininen J. Ecological studies of some Finnish rivers evaluated using benthic diatom communities. Journal of Applied Phycology. 2002;14:1-7
  74. 74. Delta Environmental. Test method: Generic diatom indices. 2007. Available from: http://www.deltaenvironmental.com.au/management/Lab_methods/Generic_indices.htm [28 Nov. 2015]
  75. 75. Lobo EA, Callegaro VLM, Hermany G, Bes D, Wetzel CA, Olivera MA. Use of epilithic diatoms as bioindicators from lotic systems in southern Brazil, with special emphasis on eutrophication. Acta Limnologica Brasieliensis. 2004;16(1):25-40
  76. 76. Dela-cruz J, Pritchard T, Gordon G, Ajani P. The use of periphytic diatoms as a means of assessing impacts of point source inorganic nutrient pollution in South-Eastern Australia. Freshwater Biology. 2006;51:951-972
  77. 77. Suphan S, Peerapornpisal Y, Underwood GC. Benthic diatoms of Mekong River and its tributaries in northern and North-Eastern Thailand and their applications to water quality monitoring. Maejo International Journal of Science and Technology. 2012;6:28-46
  78. 78. Cocquyt C. Diatoms form a hot spring in Lake Tanganyika. Nova Hedwigia. 1999;68(3):425-439
  79. 79. Ali AD, Ezra AG, Abdul SD. Species composition and distribution of freshwater diatoms from Upper Dilimi River, Jos, Nigeria. IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS). 2015;10(5):53-60
  80. 80. Compere P. Some algae for the Red Sea Hills in north-eastern Sudan. In: Dumont HJ, El Moghraby AI, Desougi LA, editors. Limnology and Marine Biology in the South Sudan, Vol. 21. Switzerland: Developments in Hydrobiology; 2012. 374 pp
  81. 81. Kelly MG, Bennion H, Cox EJ, Goldsmith B, Jamieson J, Juggins S, et al. Common Freshwater Diatoms of Britain and Ireland: An Interactive Key (Online). Bristol: Environment Agency; 2005. Available from: http://craticula.ncl.ac.uk/EADiatomKey/html/taxon13810180.html [20 March 2016]
  82. 82. Guiry MD, Guiry GM. Caloneis Bacillum (Grunow) Cleve 1894. Algae Base World-Wide Electronic Publication. Galway: National University of Ireland; 2016. Available from: http://www.algaebase.org/search/species/detail/?species_id=y1b89c1c56b056c40 [30 April 2016]
  83. 83. Prygiel J, Coste M, Bukowska J. Review of the major diatom-based techniques for the quality assessment of rivers—State of the art in Europe. In: Prygiel J, Whitton BA, Bukowska J, editors. Use of Algae for Monitoring Rivers III. Douai: Agence de l’Eau Artois-Picardie; 1999. pp. 138-144
  84. 84. Gomà J, Ortiz R, Cambra J, Ector L. Water quality evaluation in Catalonian Mediterranean rivers using epilithic diatoms as bioindicators. VIE MILIEU. 2004;54(2-3):81-90
  85. 85. Naumann, E. 1921. Einige Grundlinien der regionalen Limnologie. Lunds University: Arsskr. N.F., 17(8)
  86. 86. Reynolds CS. The Ecology of Freshwater Phytoplankton. United Kingdom: Cambridge University Press; 1984. 384 pp
  87. 87. Gbadebo AM, Taiwo AM, Adeola AJ. Assessment of dissolved silica content of groundwater from southwestern Nigeria. Journal of Human Ecology. 2013;43(3):273-279
  88. 88. Tan X, Zhang Q, Burford MA, Sheldon F, Bunn SE. Benthic diatom based indices for water quality assessment in two subtropical streams front. Microbiology. 2017;8:601. DOI: 10.3389/fmicb.2017.00601
  89. 89. Vilbaste S. Application of diatom indices in the evaluation of the water quality in Estonian streams. Proceedings of the Estonian Academy of Sciences, Biology, Ecology. 2003;53(1):37-51
  90. 90. Eassa AM. The use of diatom indices for the assessment of Shatt AL–Arab river water quality. Journal of Basrah Researches. 2012;38(1):114-124
  91. 91. Solak CN, Ács É, Dayioglu H. The application of diatom indices in the Felent Creek (Porsuk-Kütahya). In: Van Dam, Cadee, editors. Proceedings of the Joint Meeting of the Dutch-Flemish Society of Diatomists (NVKD) and 3rd Central European Diatom Meeting (CE-DiatoM) 26-29 March, 2009. Diatomededelingen 33. 2009. pp. 107-109

Written By

Adewole Michael Gbadebo, Benjamin Onozeyi Dimowo, Adewale Matthew Taiwo and Isaac Tunde Omoniyi

Submitted: 12 October 2018 Reviewed: 15 April 2019 Published: 30 May 2019