Water Quality and Phytoplankton as Indicators of Pollution in a Tropical River

Abstract

Water plays major role in biodiversity conservation hence the need for its protection. The integrity of an aquatic ecosystem can be assessed through the physico-chemistry and phytoplankton structure. Some water quality and phytoplankton of Idumayo River were assessed from January 2015 to June 2016 using standard methods. Air temperature varied from 26.50 o C in the rainy season to 39.60 o C in the dry season. Water temperature was lower (25.50 o C) in the rainy season compared to the value in the dry season (34.40 o C). Mean conductivity (199.11µS/cm) and TDS (99.22 mg/L) were higher in the dry season when transparency (0.02 m) was least. The pH of the river was more alkaline (7.90) in the dry season when the flow rate was least. Mean dissolved oxygen (5.69 mg/L) was higher in the rainy season compared to dry season (3.78 mg/L). Mean silicate (4.07 mg/L) and nitrate (1.56 mg/L) concentrations were higher in the rainy season while iron (0.46 mg/L) and orthophosphate (0.71 mg/L) were higher in the dry season. Five phytoplankton divisions were identified with Bacillariophyta (1, 948 ind/L) having the highest mean abundance in the dry season. However, Chlorophyta was the most diverse (Hʹ = 4.55) and highest in species richness (MI = 0.92) in the dry season. The water quality and phytoplankton species composition of the river showed that allochthonous input from human activities has subjected the ecosystem to pollution pressure. Human activities around the river should be regulated to protect the ecosystem.

Full text

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Proceedings of 6th NSCB Biodiversity Conference; Uniuyo 2018 (83 – 89 pp)
83
Water Quality and Phytoplankton as Indicators of Pollution in a Tropical River
NWONUMARA, Godwin Nkwuda
Applied Biology Unit, Department of Biological Sciences,
Faculty of Science, Ebonyi State University, PMB 53, Abakaliki.
Correspondence: ngnkwuda@gmail.com; Phone: +234806 239 4068
Abstract: Water plays major role in biodiversity conservation hence the need for its protection. The integrity of
an aquatic ecosystem can be assessed through the physico-chemistry and phytoplankton structure. Some water
quality and phytoplankton of Idumayo River were assessed from January 2015 to June 2016 using standard
methods. Air temperature varied from 26.50 oC in the rainy season to 39.60 oC in the dry season. Water
temperature was lower (25.50 oC) in the rainy season compared to the value in the dry season (34.40 oC). Mean
conductivity (199.11µS/cm) and TDS (99.22 mg/L) were higher in the dry season when transparency (0.02 m)
was least. The pH of the river was more alkaline (7.90) in the dry season when the flow rate was least. Mean
dissolved oxygen (5.69 mg/L) was higher in the rainy season compared to dry season (3.78 mg/L). Mean silicate
(4.07 mg/L) and nitrate (1.56 mg/L) concentrations were higher in the rainy season while iron (0.46 mg/L) and
orthophosphate (0.71 mg/L) were higher in the dry season. Five phytoplankton divisions were identified with
Bacillariophyta (1, 948 ind/L) having the highest mean abundance in the dry season. However, Chlorophyta was
the most diverse (Hʹ = 4.55) and highest in species richness (MI = 0.92) in the dry season. The water quality and
phytoplankton species composition of the river showed that allochthonous input from human activities has
subjected the ecosystem to pollution pressure. Human activities around the river should be regulated to protect
the ecosystem.
Keywords: Ebonyi state, Idumayo River, phytoplankton, pollution indicators, water quality
INTRODUCTION
Human society relies on freshwater for domestic,
industrial, agricultural and other goods and services.
These needs have subjected the ecosystems which
include rivers, streams, lakes and ponds to increasing
contamination by a variety of mineral, agrochemicals
and organic pollutants due to higher frequency of
allochthonous input from anthropogenic activities.
Some of the pollutants such as fertilizers, herbicides
(Kreuger, 1998; Dorigo, Urita, - Milla, de Wit,
Brazile, Singh, and Schaepman, (2007)), are
frequently used in land preparation for agriculture.
They gain entrance into aquatic ecosystems through
terrestrial runoff, direct application and aerial spray
(Carter, 2000), frequently causing impairment of the
water quality for other uses and the production of the
ecosystem. However, the major degrading factors
arising from the stressors may include excessive
eutrophication due to nutrient (inorganic and organic)
matter loading, sewage from sewers, siltation due to
inadequate erosion control at agricultural lands,
logging and mining activities, acidification from
industrial effluents, atmospheric sources, acid mine
drainage, contamination by toxic or potentially toxic
metals such as mercury and other organic compounds
such as poly-chlorinated biphenyls (PCBs), pesticides
(Anna-lissa and Galina, 1999). Since water bodies
serve habitat to a variety of organisms, response to the
stressors may vary among the producers and
consumers. The phytoplankton community structure
might change in line with the nutrient status and other
regulating factors, which may affect the animal
community in either way or could lead general loss of
biodiversity.
The objective of this study was to determine the
pollution index of Idumayo River by assessing the
water quality and phytoplankton structure of the
ecosystem. This is because human activities such as
rice cultivation which employ the use of herbicides,
growing of vegetables with organic manure (animal
droppings) and car washing all take place with the
river. The study will provide vital information that can
help to identify the negative impacts of human
activities on such ecosystem. The information can
serve as tool for advocacy of policies to protect the
ecological system for sustainable utilization.
MATERIALS
Study Site
The study was carried out at Idumayo River in Onicha
Local Government Area of Ebonyi State. The sampled
location lie within 6o 02ʹ 13ʺN, 8o 00ʹ 28ʺE; 6o 02ʹ
12.8ʺN, 8o 00ʹ 28.4ʺE and 6o 02ʹ 13.1ʺN, 8o 00ʹ 28.4ʺE;
6o 02ʹ 12ʺN, 8o 00ʹ 28ʺE. The study sites lie within the
Guinea savannah region and experiences distinct wet
and dry season. Major activities within the site include
cultivation of rice, yam, cassava, potato, cocoyam as
well as vegetables in the dry season.

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METHODS
Sample Collection
Physicochemical Variables
Samples were collected monthly from the study site
for eighteen (18) months from January 2015 to June,
2016. This involved measurement of air and water
temperature, conductivity, total dissolved solid
(TDS), transparency and pH at the study site while
samples for dissolved oxygen was collected using
BOD bottles, fixed immediately using Winkler’s
reagent and determined in the laboratory using
titration method. Samples for nitrate, iron, silicate and
orthophosphate were collected using plastic and glass.
The samples were analysed in the laboratory using
spectrophotometric machine (Model: 721D)
according to the standard methods of Association of
Official and Analytical Chemistry (AOAC), 2003).
All the parameters were measured in triplicate.
Phytoplankton Sample
Phytoplankton sample was collected by throwing
plankton net randomly at the surface water (within
0.15 m depth). The collected sample was poured
immediately into a liter plastic container holding 0.4
liter of buffered formalin. Identification was in the
laboratory using the guides by Prescott (1978), Botes
(2003), and Nwankwo (2004) with the aid of Olympus
binocular microscope (Model: XSZ-107E) at 1000x
magnification.
Plankton Abundance
Quantitative assessment of phytoplankton abundance
was by counting individual of each species and
presented as number of individuals per litre (ind.L−1)
according to Ovie, (1993). Diversity indices: H′ = -∑
piInpi and Margalef’s Index (d); d= S-1/ In(N) were
used in estimating phytoplankton diversity and
species richness while equations based on geometrical
formulae Ruttner-Kolisko, (1977) and Hillebrand,
Durselen, Krischtel, Pollinger and Zohray (1999)
were used in calculating the biomass.
Data Analysis
Spatial and temporal variations in environmental
variables and phytoplankton data were tested
statistically using one-way analysis of variance
(ANOVA) and values were considered significant at
p < 0.05 levels. All the analysis was carried out using
Statistical package for social science (SPSS) software
version 20 and Paleontological statistics (PAST).
RESULTS
Physicochemical Variables
The results of the physicochemical variables of the
study site showed that air temperature varied from
26.50 oC in the rainy season to 39.60 oC in the dry
season (Table 1). Water temperature was lower (25.50
oC) in the rainy season compared to the dry season
(34.40 oC). Mean conductivity (199.11µS/cm) and
TDS (99.22 mg/L) were higher in the dry season.
However, transparency (0.02 m) was least in the dry
season. The pH of the river was more alkaline (7.90)
in the dry season when the flow rate was least. Mean
dissolved oxygen (5.69 mg/L) was higher in the rainy
season compared to dry season (3.78 mg/L). Mean
silicate (4.07 mg/L) and nitrate (1.56 mg/L)
concentrations were higher in the rainy season while
iron (0.46 mg/L) and orthophosphate (0.71 mg/L)
were higher in the dry season. ANOVA result showed
that air temperature, conductivity, TDS, flow rate,
dissolved oxygen and orthophosphate varied
significantly (p< 0.05) between seasons (Table 1).
Phytoplankton Species Composition
Five phytoplankton divisions including
Bacillariophyta (34 species), Chlorophyta (27
species), Cyanobacteria (12 species), Dinophyta (6
species) and Euglenophyta (1 species) were identified
from the study site (Table 2). Phytoplankton species
identified among the divisions that were indicators of
pollution include Biddulphia laevis, Fragilaria
species, Aulacoseira granulata, Navicula species,
Pleurosigma directum, Synedra nana, Surirella
splendida, Spirogyra africana, Microcystis
aeruginosa, Oscillatoria species, Anabaena species
and Euglena granulata, Ankistrodesmus fractus,
Coscinodiscus species, Nitzschia closterium and
Hemidiscus cuneiformis (Table 2). The most
dominant among the pollution indicators were
Pleurosigma directum (13, 360 ind/L), Synedra nana
(3, 520 ind/L), and Eugllena granulata (1, 430 ind/L)
(Table 2).
The seasonal mean abundance, diversity, species
richness and mean biomass of the five divisions were
compared (Table 3). All the divisions were higher in
mean abundance in the dry season. Bacillariophyta
was more diverse (Hʹ = 3.82) in the rainy season and
had higher species richness (d = 0.81) and biomass
(5.52 µg/L) values in the dry season. However,
Chlorophyta was higher in diversity (Hʹ = 4.55),
species richness (d = 0.92) and mean biomass (0.73
µg/L) in the dry season and was highest in diversity
and species richness among the five divisions
identified (Table 3). Only the abundance and biomass
contributions of the five phytoplankton divisions
identified varied significantly between seasons
(p<0.05)
.

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85
Table 1: Seasonal Variation in Physicochemical variables of Idumayo River
Table 2: Phytoplankton Species Composition, Abundance and Biomass
Phytoplankton Divisions /
Species
Abundance
(ind/L)
Biomass
(µg/L)
References
Bacillariophyta
21, 170
58.340
Achnanthes lanceolata
20
0.001
(Brébisson ex Kützing) Grunow in Van Heurck
1880
Anomoeoneis sphaerophora
10
0.041
Pfitzer, 1871
Aulacoseira granulata*
110
0.140
(Ehrenberg) Simonsen, 1979
Biddulphia laevis*
10
0.002
Ehrenberg, 1843
Caloneis bacillum
100
0.210
(Grunow) Cleve 1894
Chlorobotrys regularis
10
< 0.001
(West) Bohlin 1901
Cocconeis pediculus
70
0.0007
Ehrenberg 1838
Coscinodiscus centralis*
10
0.004
Ehrenberg 1839
Coscinodiscus granii*
10
0.004
L.F.Gough 1905
Coscinodiscus stellaris*
30
0.003
Roper 1858
Denticula elegans
10
0.002
Kützing 1844
Diatomella balfouriana
60
0.007
Greville 1855
Diceras phaseolus
10
0.002
Fott, 1936
Fragilaria capucina*
820
0.049
Desmazières, 1830
Gomphoneis herculeana
10
0.031
(Ehrenberg) Cleve, 1894
Gryosigma acuminatum
500
0.585
Rabenhorst, 1853
Gyrosigma balticum
860
4.257
(Ehrenberg) Rabenhorst, 1853
Hemidiscus cuneiformis*
50
8.240
Wallich, 1860
Navicula digitoradiata*
280
0.962
(W.Gregory) Ralfs, 1861
Navicula kotschyii*
350
0.034
Grunow, 1860
Nitzschia closterium*
10
0.014
(Ehrenberg) W.Smith, 1853
Nitzschia sigma
50
0.071
(Kützing) W. Smith, 1853
Opephora martyi
590
3.127
Héribaud-Joseph, 1902
Peronia fibula
30
0.004
Ross(1956)
Pleurosigma capense
10
0.029
Petit, 1876
Pleurosigma directum*
13,360
38.076
Grunow,1880
Pseudo-nitzschia australis
10
0.010
Frenguelli, 1939
Rhizosolenia hebatata
160
0.086
Grunow
Rhoicosphenia curvata
50
0.010
(Kützing) Grunow, 1860
Rainy Season
Dry Season
Parameters
Mean±SE
Minimum
Maximum
Mean±SE
Minimum
Maximum
Air Temperature(ᵒC)
30.49a±1.10
26.50
35.60
32.98b±1.35
26.20
39.60
Water Temperature (ᵒC)
29.33c ±1.07
25.50
35.50
30.17c ±0.92
26.40
34.40
Conductivity (µS/cm)
74.88d±10.31
50.00
126.00
199.11e±35.58
82.00
443.00
TDS (mg/L)
35.88f±5.09
18.00
62.00
99.22g±17.27
54.00
220.00
Transparency (m)
0.13h±0.08
0.08
0.15
0.08h±0.02
0.02
0.14
pH
6.80i ±0.05
6.60
7.00
7.18i ±0.16
6.50
7.90
Flow rate (m/s)
0.04 j ± 0.01
0.00
0.06
0.01k±0.003
0.00
0.03
DO (mg/L)
5.69 l± 0.68
3.80
8.50
3.78m±0.27
2.60
5.20
Silicate (mg/L)
4.07 n± 1.09
0.59
8.58
2.88n ±0.60
0.43
5.10
NO3- (mg/L)
1.56p± 0.36
0.27
3.17
1.13p ±0.15
0.25
1.85
Fe2+ (mg/L)
0.29o ± 0.08
0.06
0.81
0.46o±0.12
0.09
1.01
Orthophosphate PO4-
(mg/L)
0.55q± 0.14
0.14
1.23
0.71r±0.16
0.08
1.35

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Skeletonema costatum
10
0.004
(Greville) Cleve 1873
Stauroneis parvula
20
0.012
(Grunow) Cleve 1894
Surirella ovalis*
10
0.009
Brebisson 1838
Synedra nana*
3,520
2.310
F.Meister 1912
Tabellaria fenestrate
10
0.003
Kützing 1844
Chlorophyta
6, 630
17.259
Ankistrodesmus fractus*
50
0.006
(West &G.S.West) Collins 1912
Chlorococcum infusionum
10
0.002
(Schrank) Meneghini 1842
Closteriopsis longissima
10
0.022
(Lemmermann) Lemmermann 1899
Closterium kuetzingii
10
0.029
Brébisson 1856
Closterium leibleinii
90
0.259
Kützing ex Ralfs 1848
Closterium lunula
10
0.525
Ehrenberg &Hemprich ex Ralfs 1848
Closterium parvulum
var,maius
20
0.042
(Schmidle) Willi Krieger 1935
Cosmarium margaritatum
40
0.116
(P.Lundell) J.Roy&Bisset 1886
Cosmarium panamense
4,520
9.490
Prescott 1936
Docidium undulatum
10
0.030
Bailey 1851
E v ctochaete endophytum
600
0.900
(M. Mobius) Wille 1909
Genicularia elegans
10
0.005
West & G.S. West 1903
Hormidiopsis ellipsoideum
10
0.007
Prescott 1944
Micrasterias foliacea
100
0.063
Bailey ex Ralfs 1848
Micrasterias radiata
100
1.950
West and West 1905
Netrium digitus
50
0.630
Rabenhorst 1856
Nephrocytium limneticum
70
0.012
Smith 1933
Palmellopsis gelatinosa
10
< 0.001
Korshikov 1953
Pediastrum simplex
10
< 0.001
Meyen 1829
Planktosphaeria gelatinosa
10
0.001
Smith 1918
Pleurotaenium trabecula
30
0.390
Nägeli 1849
Polytoma obtusum
10
0.001
Pascher 1927
Schizomeris leibleinii
90
0.252
Kützing 1843
Spirogyra Africana*
520
2.180
(F. E. Fritsch) Czurda, 1932
Spondylosium planum
30
0.270
(Wolle) West and G.S.West 1912
Stylosphaeridium stipitatum
10
0.013
(Bachmann) Geitler and Gimesi, 1925
Ulothrix aequalis
200
0.064
Kützing 1845
Cyanophyta
5, 300
7.403
Anabaena subcylindricum*
10
0.013
Borge 1921
Aphanizomenon flos-aquae
1, 190
1.010
Ralfs ex Bornet and Flahault 1886
Aphanocapsa grevillei
10
0.007
(Berkeley) Rabenhorst 1865
Chamaesiphon incrustans
200
0.053
Grunow in Rabenhorst 1865
Dactylococcopsis acicularis
10
0.004
Lemmermann 1900
Gloeocapsa punctate
10
0.035
Nägeli 1849
Hydrocoleum oligotrichum
10
< 0.001
A.Braun 1865
Microcystis aeruginosa
70*
6.090
(Kützing) Kützing 1846
Oscillatoria rubescens
10*
0.043
De Candolle ex Gomont 1892
Raphidiopsis curvata
10
< 0.001
Fritsch and Rich 1930
Rivularia species
40
0.123
Bornet and Flahault 1886
Schizothrix tinctoria
5000
0.025
Gomont ex Gomont 1892
Dinophyta
250
0.733
Ceratium dens
20
0.087
Ostenfeld and J.Schmidt 1901
Dinophysis acuminate
10
0.018
Claparède&Lachmann 1859
Preperidinium meunieri
80
0.062
(Pavillard) Elbrächter 1993
Prorocentrum micans
80
0.330
Ehrenberg 1834
Protoperidinium excentricum
20
0.052
(Paulsen) Balech 1974
Protoperidinium subinerme
40
0.184
(Paulsen) Loeblich III 1969

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Euglenophyta
1, 430
2.367
Euglena granulata*
1, 430
2.367
G.A.Klebs) F.Schmitz 1884
NB: Species with asterisks are pollution indicator.
DISCUSSION
Physicochemical variables
Temporal variation in air and water temperature
observed could be attributed to seasonal changes in
weather condition since the variable was directly
linked to season. Temperature was an important
ecological factor as it directly affected the behaviour
and productivity of organisms and dissolution of gases
in water (Dixit and Tawari, 2007). Although there was
high variability in water temperature of lotic systems
due to the flow condition, especially in the rainy
season when flow velocity was high, productivity in
the dry season could be enhanced by increased water
temperature and higher residence time.
Higher conductivity and TDS was observed in the dry
season and could be due to higher concentrations of
dissolved ions in the water bodies at that period. This
could be linked to higher water temperature recorded
in the dry season which Dixit and Tawari, (2007)
suggested that it enhances the solubility of salt.
Nwonumara, Okogwu, Ani, and Nwinyimagu (2016)
recorded higher conductivity at Idumayo River in the
dry season in their previous study at the same site. The
authors attributed this to higher concentrations of
dissolved ions in the river then. According to Koning
and Ross, (1999) long dry period, low flow condition
and high temperature can contribute to high
conductivity and these features were typical of the
observations made at the study sites in the dry season.
The conductivity values recorded in the dry season
was above that of unpolluted river according to
Koning and Ross, (1999), which was an indication
that the river is under pollution pressure.
Low transparency in the dry season could be due to
higher frequency of human activities in the rivers and
re-suspension as well as easy distribution of inorganic
particles in the water columns (MacIntyre and
Melack, 1984). Since the water level was below 1
meter in the dry season, a little tilt of the water could
result to the suspension of dissolved soil particle
which might significantly affect the transparency.
This might be a limiting factor to phytoplankton
productivity in the euphotic zone where other factors
such as nutrient availability was limiting.
Flow rate was generally higher in the rainy season due
to increased volume of water in the rivers than in the
dry season. High flow rate can increase dissolved
solid load which limit light penetration for
phytoplankton production (Gosselain, Descy, and
Everbecq 1994) in the rainy season. It can also reduce
phytoplankton residence time (Marshall and Alden,
1997), thereby affecting the productivity of the groups
with larger cell size and slower growth rate (Reynolds,
1984). However, low flow condition can enhance
productivity by allowing time for phytoplankton to
multiply so that the gain in numbers in unit time
exceeds the loss in number.
Nutrient concentration (phosphate) was
comparatively higher in the dry season than rainy
season. This could be due to the washing off and
accumulation of inorganic phosphate from fertilizer
and the product of the microbial degradation of
glyphosate herbicide used on the riparian farmland
into the river bed. Higher level of microbial activities
enhanced by higher environmental temperature may
lead to the release of phosphate locked up in sediment
thereby increasing the concentration in the dry season.
Benslama and Boulahrouf, (2013) reported that
increase in environmental temperature as observed in
the dry season in this study enhances microbial
activities which release nutrients that are locked up
underneath the earth. Furthermore, higher silicate and
nitrate concentrations in the rainy season could be due
to the dissolution of soil silicate by the erosive action
of flowing water and input from nitrogenous fertilizer
used in rice farms. Hence, farming activities coupled
with increase in decayed plant materials in the river
may be responsible for the high nutrient
concentrations recorded during the study.
Phytoplankton Species Composition
The phytoplankton divisions identified from the study
site during the sampled period were similar to those
reported by other researchers including Ekwu and
Sikoki, (2006); Agouru and Audu, (2012); Ewa,
Iwara, Alade, and Adeyemi (2013); Okogwu and
Ugwumba, (2013) have recorded in tropical rivers.
This indicated similarity in biogeographical province
among phytoplankton of tropical rivers.
Bacillariophyta was the highest in total phytoplankton
abundance and biomass. Hence, mean phytoplankton
abundance that was higher in the dry season could be
due to higher nutrient level, more intense solar
radiation and low flow rate which increased the
residence time of phytoplankton to utilize available
nutrients and optimal light intensity for growth and
development. Okogwu and Ugwumba, (2013)
recorded peak phytoplankton abundance in the dry
season and attributed it to increased temperature, solar
radiation and water residence time while Ewa et al.
(2013) linked it to increased solar radiation. These
factors according to Soares, Huszar, and Roland
(2007); and Perbiche-Neves, Ferreira and Nogueira

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88
(2011), support algae development in rivers while
high flow rate and low temperature as observed at the
study site during rainy season could affect
phytoplankton productivity and abundance. This may
contribute to the lower phytoplankton abundance
recorded in the rainy season. Chlorophyta had the
highest diversity and species richness in both
seasons.This could be due to higher predation by
zooplankton which reduced the abundance of some
species, reducing competition to allow for the
development and growth of more species. Species
such as Ankistrodesmus and Pediastrum are suitable
food for zooplankton and are among those identified
during the study and high predation on these species
may have favoured the diversity.
Mean phytoplankton biomass was higher in the dry
season compared to the rainy season and could be
attributed to higher water temperature, increased solar
radiation and water residence time.These factors
according to Bukaveckas, MacDonald,
Aufdenkampe, Chick, Havel, Schultz, Angradi,
Bolgrien, Jicha and Taylor (2011) enhance efficient
light and nutrient utilization and reduce algae wash-
out thereby increasing biomass accumulation.
However, decline in phytoplankton biomass in the
rainy season could be due to higher flow rate, lower
nutrient concentrations and lower water temperature,
which was also reported by Okogwu and Ugwumba,
(2013) in their study at Cross River.
The presence of Ankistrodesmus fractus, Aulacoseira
granulata, Biddulphia laevis, Coscinodiscus specie,
Fragilaria species, Navicula species, Pleurosigma
directum, Synedra nana, Surirella splendida,
Spirogyra africana, Euglena granulata, Microcystis
aereginosa, Oscillatoria species and Anabaena
species in the river during the study indicated that it is
under pollution pressure. The proliferation of these
species could be due to high nutrient concentrations
of the river especially in the dry season when it was
enhanced by low flow condition, higher rate of
evaporation and low water level. Edward and
Ugwumba, (2013) and Onyema, (2013) reported some
of the species as indicators of pollution in their study
at Egbe reservoir and Iyagbe lagoon.
Table 3: Seasonal Mean Abundance, Diversity,
Species richness and Mean Biomass (µg/L) of
Phytoplankton Divisions from the study site
Phytoplan
kton
Division
Diversity
indices and
Biomass
Rainy
season
Dry season
Bacillariophyta
Mean
Abundance
(ind/L)
241.00 a
1,948.00b
Number of
species
13.00
33.00
Shannon-
Weiner
diversity index,
Hʹ
3.82a
2.76a
Margalef’s
index, d
0.79b
0.81b
Mean Biomass
(µg/L)
0.05 b
5.52 c
Chlorophyta
Mean
Abundance
220.00 c
587.00d
Number of
species
18.00
18.00
Shannon-
Weiner
diversity index,
Hʹ
4.17c
4.55 c
Margalef’s
index, d
0.80d
0.92 d
Mean Biomass
(µg/L)
0.60 d
1.27 e
Cyanobacteria
Mean
Abundance
8.00 f
652.00 g
Number of
species
5.00
9.00
Shannon-
Weiner
diversity index,
Hʹ
2.00g
1.53g
Margalef’s
index, d
0.49g
0.46 g
Mean Biomass
(µg/L)
0.01 g
0.73 h
Dinophyta
Mean
Abundance
0.00i
25.00 j
Number of
species
0.00
6.00
Shannon-
Weiner
diversity index,
Hʹ
0.00k
2.33l
Margalef’s
index, d
0.00m
0.62n
Mean Biomass
(µg/L)
0.00o
0.07p
Euglenophyta
Mean
Abundance
30.00q
119.00r
Number of
species
1.00
1.00
Shannon-
Weiner
diversity index,
Hʹ
nc
nc
Margalef’s
index, d
nc
nc
Mean Biomass
(µg/L)
0.01s
0.02t

Nwonumara
Proceedings of 6th NSCB Biodiversity Conference; Uniuyo 2018 (83 – 89 pp)
89
Note: Values with the same superscript on the same
row are not significant, “nc” means not calculated.
CONCLUSION
The effects of anthropogenic stress of aquatic
ecosystem can be identified through water quality
assessment and the phytoplankton structure. Some
physico-chemical (conductivity) variable was above
that of unpolluted sites while some phytoplankton
species that are indicators of pollution were
recorded. Hence, low diversity of most
phytoplankton divisions recorded posits that the
river is on the verge of being polluted. Hence,
anthropogenic activities around the river should be
regulated to ensure its protection and conservation.
This might contribute to national development
through provision of good water for domestic use
and food.
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