Water quality assessment: Surface water sources used for drinking and irrigation in Zaria, Nigeria are a public health hazard

Abstract

We assessed the quality and pollution status of source surface waters in Zaria, Nigeria by monitoring the nature, cause and extent of pollution in Samaru stream, Kubanni River and Kubanni dam over a period of 10 months, between March and December 2002. A total of 228 water samples was collected from 12 sites and analysed for a total of ten physicochemical and one bacteriological quality indicators, using standard methods. Aesthetic water quality impairment parameters were also observed. The mean values of most water quality parameters were significantly higher (P < 0.05) in both the stream and river than in the dam. There was no significant correlation between faecal coliform counts (FCC) and water temperature (in the range 15–33°C); pH (5.77–7.32); and turbidity (1.4–567 NTU). The high FCC ranged from 2.0 × 101 to 1.6 × 106 MPN/100 ml and exceeded the WHO standards for drinking water and water used for fresh-produce irrigation, and correlated positively (P < 0.05) with conductivity (in the range 68–1,029 μS/cm); TDS (10.0–70.0 mg/l); TSS (10.0–70.0 mg/l); Cl (7.5–181 mg/l); PO
4−P (0.01–0.41 mg/l); NO
3−N (0.6–3.8 mg/l) and BOD5 (0.1–14.9 mg/l). The main pollution sources were municipal wastewater, stormwater runoffs, the ABU sewage treatment plant, abattoir effluents and irrigation farms treated with chemical fertilisers. We conclude that these water bodies are potentially hazardous to public health and that proper sewage treatment and river quality monitoring are needed to warn against hazards to public health.

Full text

Water quality assessment: surface water sources
used for drinking and irrigation in Zaria, Nigeria
are a public health hazard
Vincent N. Chigor &Veronica J. Umoh &
Charles A. Okuofu &Joseph B. Ameh &
Etinosa O. Igbinosa &Anthony I. Okoh
Received: 14 November 2010 /Accepted: 4 October 2011 /Published online: 21 October 2011
#Springer Science+Business Media B.V. 2011
Abstract We assessed the quality and pollution status
of source surface waters in Zaria, Nigeria by
monitoring the nature, cause and extent of pollution
in Samaru stream, Kubanni River and Kubanni dam
over a period of 10 months, between March and
December 2002. A total of 228 water samples was
collected from 12 sites and analysed for a total of ten
physicochemical and one bacteriological quality
indicators, using standard methods. Aesthetic water
quality impairment parameters were also observed.
The mean values of most water quality parameters
were significantly higher (P<0.05) in both the stream
and river than in the dam. There was no significant
correlation between faecal coliform counts (FCC) and
water temperature (in the range 15–33°C); pH (5.77–
7.32); and turbidity (1.4–567 NTU). The high FCC
ranged from 2.0×10
1
to 1.6×10
6
MPN/100 ml and
exceeded the WHO standards for drinking water and
water used for fresh-produce irrigation, and correlated
positively (P<0.05) with conductivity (in the range
68–1,029 μS/cm); TDS (10.0–70.0 mg/l); TSS
(10.0–70.0 mg/l); Cl (7.5–181 mg/l); PO
4
−
P
(0.01–0.41 mg/l); NO
3
−
N (0.6–3.8 mg/l) and BOD
5
(0.1–14.9 mg/l). The main pollution sources were
municipal wastewater, stormwater runoffs, the ABU
sewage treatment plant, abattoir effluents and irrigation
farms treated with chemical fertilisers. We conclude that
these water bodies are potentially hazardous to public
health and that proper sewage treatment and river
quality monitoring are needed to warn against hazards
to public health.
Keywords Water quality.Irrigation .Assessment
Introduction
The pollution of surface waters is a problem of global
concern contributing to high morbidity and mortality
rates from waterborne and food-borne diseases, such
as typhoid fever, cholera and diarrhoea (Pruss et al.
Environ Monit Assess (2012) 184:3389–3400
DOI 10.1007/s10661-011-2396-9
Following our initial reports, the sewage treatment plant was
renovated in 2004. We do not know how efficient the plant is
today. Furthermore, except for the university campus, the entire
area remains unsewered, and the population of the area has
continued to increase.
V. N. Chigor (*):E. O. Igbinosa:A. I. Okoh
Applied and Environmental Microbiology Research Group,
Department of Biochemistry and Microbiology,
University of Fort Hare,
Private Bag X1314,
Alice 5700, South Africa
e-mail: vnchigor@yahoo.com
V. J. Umoh :J. B. Ameh
Department of Microbiology, Ahmadu Bello University,
Funtua Road Samaru,
Zaria 810107, Kaduna State, Nigeria
C. A. Okuofu
Department of Water Resources and Environmental
Engineering, Ahmadu Bello University,
Funtua Road Samaru,
Zaria 810107, Kaduna State, Nigeria

2002; WHO 2009). Globally, it is estimated that 6–60
billion cases of gastrointestinal illness occur annually
(Payment and Riley 2002). Worldwide, 37% of people
not using improved source of drinking water live in sub-
Saharan Africa (WHO/UNICEF 2010). Such water
sources are vulnerable to contamination with faecal
pathogens, which pose serious public health risks
linked to excessive nutrient levels and the presence of
enteric pathogens in drinking and irrigation waters
(Shuval 1990; Hitzfeld et al. 2000; Pruss et al. 2002).
Nonpoint sources of such contamination include
domestic and wild animal defecation, malfunctioning
sewage and septic systems, stormwater drainage and
urban runoff, while point sources include as industrial
effluents and municipal wastewater treatment plants
(Kistemann et al. 2002; Albek 2003; Okoh et al.
2007; Igbinosa and Okoh 2009; Odjadjare and Okoh
2010). Furthermore, rapid population growth, land
development along river banks and in the catchment
areas, urbanization and industrialization place increased
demand on surface waters both as sources of water for
different uses and as carriers of treated and untreated
wastewaters (Suthar et al. 2010; Solaraj et al. 2010).
Yet, access to potable water and sanitation are
essential elements for the efficient functioning of
human settlements, and are integral to human health
and well-being (WHO 2008,2009).
Zaria is a highly populated region of Kaduna State,
Nigeria. As revealed by the Nigerian 2006 census, the
1991 Zaria population of over 600, 000 inhabitants
has almost doubled over a 15-year period (NPC and
Abuja 2006). Today’s estimate puts the total popula-
tion at over a million. In Zaria, as in most regions of
northern Nigeria, and as in many developing
countries with inadequate sanitation, drinking and
irrigation waters are sourced mainly from surface
waters, including lakes, dams, rivers and streams that
are vulnerable to faecal contamination (Shuval 1990;
Barbier and Thompson 1998; Byamukama et al.
2000; Agbogu et al. 2006; Chuo 2009). The use of
raw and treated wastewater for the irrigation of fresh
produce is also a common practice in these areas
(Shuval 1990; Umoh et al. 2001; Chigor et al. 2010a;
Okibe et al. 2010) and records at Ahmadu Bello
University Teaching Hospital, Zaria show that enteric
and waterborne infections are prevalent in Zaria
(unpublished data). Studies have also shown that the
hygienic quality of drinking water as delivered to
consumers in the area is unsatisfactory (Dada et al.
1990). It was therefore considered important to
evaluate the water quality of such water sources.
Some studies have investigated the impact of indus-
trial effluents and chemical pollutants on surface
waters in Zaria (Adakole 2000; Ekanem and Irekpita
2004; Adakole and Abolude 2009). However, after
nearly four decades since Smith (1975) and Okuofu
(1978) carried out some pioneering pollution studies,
little attention has been paid to microbiological
contamination of surface waters in the study area. In
this paper, we report the nature, sources and extent of
pollution in Samaru stream, Kubanni dam (also called
Ahmadu Bello University [ABU] dam) and Kubanni
River, in Zaria, and the public health risks attendant to
their use.
Materials and methods
The study area and sampling sites
Zaria region consists of Giwa, Sabon-Gari and Zaria
Local Government Areas in Kaduna State, Nigeria. It
lies within the Sudan Savanna and is located on
latitude 11°9′N and longitude 07°41′E. The
population of Zaria has increased from over 600,
000 (1991 census) to approximately one million
recorded during the 2006 census (NPC 2006). Zaria
is characterised by a tropical climate with two main
reasons; a rainy season of about 210 days (May to
October) and a dry/harmattan season (November to
April). The mean monthly temperature ranges from
13.8°C to 36.7°C and annual rainfall is over
1,090 mm. The three surface waters, which form a
continuum, are sources of raw water for potable water
production plants. Samaru stream discharges into the
Kubanni dam, which is the source of drinking water
for the university community. Similarly, Kubanni River
discharges into Zaria dam the source of raw water for
Zaria waterworks. While Samaru stream additionally
serves for recreation and herd watering, Kubanni River
is largely used by local farmers for the irrigation of
commercial crops including tomatoes, lettuce, cabbage,
onions, spinach and sugarcane. More than half of Zaria’s
working population derived their principal means of
livelihood from agriculture (Chuo 2009).
Twelve sampling sites were selected, three along
Samaru stream (S1, a recreational site and point source
of contamination from domestic sewage draining from
3390 Environ Monit Assess (2012) 184:3389–3400

Samaru village; S2, near student hostel septic tanks; S3,
a site where the stream meets the Kubanni River), four at
the dam (S4, a site of anthropogenic influence; S5, the
point of abstraction of raw water from the dam for
purification; S6, a site at a shallow zone where
herdsmen water their animals; and S7, located at the
point where another tributary empties into the dam), and
five sites along Kubanni River (S8, a site where
university sewage treatment effluent enters Kubanni
River); S9, S10 and S11 were at 300-m intervals
downstream from S8. The last site, S12 is located
10 m downstream of the point where the gutter that
drains Jama’a village and effluent from Zango abattoir
meets the river (Fig. 1).
Observation of aesthetic parameters and sampling
Aesthetic environmental quality impairment parameters,
including foaming, litter, coloured effluent and algal
blooms, were observed by the methods of House (1996)
and Williams and Simmons (1999).
In a 10-month monitoring period (March to
December, 2002), 228 water samples were taken from
the 12 sampling sites and analysed to determine a
total of ten physicochemical parameters, as well as
one bacteriological parameter, faecal coliform counts
(FCC), according to standard methods (APHA 1992).
All samples were collected between 0830 and 1030
hours, at a depth of about 15 cm in the direction of flow
using sterile, wide-mouthed, screw-capped 250 ml
bottles, for bacteriological analysis and 2-L plastic
cans, for water for physicochemical analyses.
Samples were immediately transported on ice to
the laboratory and analysed within 4 h of
collection. Due to lack of access, it was not
possible to collect samples from sites S3, S4,
S10 and S11 in March and from S7 in March and
April.
Bacteriological analysis
The most probable number (MPN) technique was
used to determine the FCC of the water samples
(APHA 1992). This involved the presumptive test using
MacConkey broth (Bioteck; Suffolk, UK) with Durham
tube, confirmatory test using brilliant green lactose
broth and completed test using eosin methylene blue
(EMB) agar (LAB M; Lancashire, UK). The tubes and
Fig. 1 The study area and sampling sites
Environ Monit Assess (2012) 184:3389–3400 3391

plates were incubated at 44.5°C for 24–48 h. Gas and
turbidity in the tubes as well as metallic sheen or pink
with dark centre colonies on EMB agar indicated
positive responses. All isolates that produced gas at
44.5°C, stained Gram-negative and were non-spore
forming and rod-shaped were regarded as faecal coli-
forms and the counts were calculated from a standard
probability table (APHA 1992).
Physicochemical analyses
Temperature, pH, turbidity and conductivity were
measured on site. The pH was determined using
pre-calibrated portable pH meter (Norylab; PM8).
Conductivity was measured at 25°C directly in
microsiemens per centimeter using a digital conductiv-
ity meter (NoryLab; LM8), while turbidity was mea-
sured with a digital turbidimeter (Hach Chemicals;
2100P). The total suspended solids (TSS) and total
dissolved solids (TDS) were separated by filtering the
water through a 0.45 μm filter paper and determined
according to standard procedures (APHA 1992).
Chloride was determined by the argentometric
(Mohr’s)method, while nitrate (NO
3
−
N) and phosphate
(PO
4
−
P) were determined by using a Cecil spectro-
photometer to measure the intensities of colour
developed following the phenoldisulphonic acid and
the phosphomolybdate methods, respectively. Five-
day biochemical oxygen demand (BOD
5
) was deter-
mined titrimetrically after incubation in tightly stop-
pered BOD bottles in the dark at 20°C (APHA 1992).
Statistical analysis
Student’sttest was used to compare the means of
physicochemical parameters for the two seasons and
one-way analysis of variance and Duncan’s multiple
range tests were used to compare the means of
parameters for the different water bodies and the 12
sites. The coefficient of correlation between FCC and
the physicochemical parameters was calculated by the
Pearson correlations test. Statistical significance was
set at P<0.05.
Results and discussion
Both humans and animals were observed to defecate
and urinate in the open, especially in Samaru Stream
and on its banks. A recent WHO report, reveals that the
proportion of the world’s population that practises
defecation in the open is high. The report also pointed
out that though the great majority of people defecating
in the open live in rural areas, due to rapid increases in
urban populations, a growing number of these people
also defecate in the open (WHO/UNICEF 2010). The
outcome is that faecal matter is washed into surface
waters after a rainstorm, as is the case in the study
area. But for the university campus, the entire study
area is unsewered. Pit latrines and septic tanks
prevail. Domestic sewage could be seen discharged
directly from residential houses and failing septic
tanks into drains and thus into the Samaru stream.
The most polluted stretch of the stream (between
sampling sites S1 and S2) emitted bad odours that
restricted their use, especially for recreation. A similar
finding was made elsewhere by House (1996) and the
visual and odorous characteristics of the environment
tend to be those that have the greatest impact upon the
public’s assessment of water quality. Heavy litter,
rubbish (bottles, cans, plastics, polythene wastes,
etc.), sewage-associated and runoff-derived wastes
and coloured effluents not only impaired the quality
of Samaru stream but further complicate the long-
established reservoir siltation problem of the dam
(Yusuf 2009) into which the stream drains. The
disappearance of odours following heavy storm
events, and their absence at ABU dam, may be
attributable not to reduced pollution, but to dilution
due to high volumes of inflow water.
The presence of large solid particles, natural debris
and farm-derived wastes along the banks and within
the stretches of Kubanni River was due to extensive
irrigation farming. Fishing and cattle watering were
common at ABU dam. At Kubanni River, irrigation
farming was extensive, occurring not only along the
banks but also on the expanses of land adjoining the
banks. Particularly in hot periods, swimmers were seen
at certain sections of the river. Dumps of farm-derived
wastes and massive growth of floating weeds charac-
terised Kubanni River. Human faeces and cattle dung
were found on the slopes of the river valley. Industrial
effluents were channelled into the river. The floating
weeds, water hyacinths and mats of dead vegetation,
occurring especially in the stretch between S9 and S12,
restricted the use of Kubanni River by swimmers.
Table 1compares the mean values of the various
water quality parameters (WQPs) measured at the 12
3392 Environ Monit Assess (2012) 184:3389–3400

Table 1 Comparison of mean values of water quality parameters for all the sampling sites
Surface
water
Site Variables (mean±standard deviation)
WT (°C) pH EC (μS/cm) Turbidity (NTU) TDS (mg/l) TSS (mg/l) Chloride
(mg/l)
PO
4
−
P
(mg/l)
NO
3
−
N
(mg/l)
BOD
5
(mg/l)
FCC (MPN/
100 ml)
Samaru
stream
S1 24.80
a
±1.33 6.76
ab
±0.07 674.80
a
±65.29 54.77
cd
±11.45 45.00
a
±5.63 33.00
a
±5.97 95.77
a
±7.42 0.32
a
±0.04 2.56
a
±0.30 8.60
a
±1.29 7:8105a 1:7105
S2 24.60
a
±1.37 6.70
ab
±0.17 495.40
b
±40.54 36.03
cd
±11.43 31.00
bc
±3.15 32.00
ab
±6.63 81.80
a
±12.61 0.13
cd
±0.3 2.23
ab
±0.33 6.05
b
±0.63 1:2105a 3:7104
S3 25.95
a
±1.38 6.63
ab
±0.06 202.63
cd
±35.07 295.41
a
±99.50 15.00
d
±1.67 23.00
bcd
±4.23 36.81b
c
±8.25 0.23
bc
±0.04 1.71
bc
±0.24 1.35
de
±0.20 3:2103b 1:6103
ABU
dam
S4 25.50
a
±1.48 6.54
b
±0.10 218.37
cd
±42.37 217.67
ab
±59.15 29.00
bc
±7.95 20.50
bcd
±4.74 35.17
bc
±6.59 0.15
cd
±0.03 2.01
abc
±0.28 1.66
de
±0.26 7:8103b 3:7103
S5 26.10
a
±1.46 6.64
ab
±0.04 126.51
d
±10.31 57.51
cd
±21.93 21.00
cd
±4.33 15.00
cd
±2.24 18.24
c
±2.45 0.09
d
±0.02 1.49
c
±0.22 0.68
e
±0.12 4:9101c 2:5101
S6 25.65
a
±1.47 6.67
ab
0.05 135.34
b
±9.70 96.65
bcd
±35.64 18.00
cd
±2.49 26.00
ab
±5.42 23.08
c
±3.49 0.14
cd
±0.02 1.64
bc
±0.24 1.53
de
±0.26 3:3103b 2:1103
S7 26.08
a
±1.55 6.70
ab
±0.04 128.44
d
±11.72 186.88
abc
±58.15 23.00
bcd
±3.00 12.00
d
±2.00 27.24
bc
±4.42 0.26
ab
±0.03 1.73
bc
±0.24 1.04
de
±0.21 1:2103b 8:8102
Kubanni
River
S8 25.10
a
±1.42 6.68
ab
±0.05 266.70
c
±29.27 19.98
d
±4.36 29.00
bc
±3.15 16.00
cd
±2.21 29.99
bc
±4.16 0.22
abc
±0.04 1.92
abc
±0.17 3.66
c
±1.03 2:9104b 1:8104
S9 25.60
a
±1.50 6.84
a
±0.05 236.51
c
±13.44 78.31b
cd
±35.74 24.00
bcd
±1.63 13.00
cd
±1.53 27.89
bc
3.98 0.14
cd
±0.03 1.65
cd
±0.23 0.93
de
±0.23 4:5103b 3:4103
S10 25.75
a
±1.47 6.68
ab
±0.80 187.92
cd
±11.42 131.93
cd
±51.77 23.50
bcd
±3.17 19.00
bcd
±3.15 21.29
c
±1.99 0.14
cd
±0.04 1.82
abc
±0.21 0.96
de
±0.24 1:2103b 4:0102
S11 25.90
a
±1.49 6.69
ab
±0.07 186.19
cd
±15.81 169.31
abcd
±64.97 19.50
cd
±2.41 21.00
bcd
±2.77 22.17
c
±3.11 0.18
bcd
±0.04 1.87
abc
±0.15 0.96
de
±0.27 1:7103b 1:1103
S12 26.20
a
±1.32 6.76
ab
±0.07 243.08
c
±20.37 130.65
bcd
±49.41 35.00
ab
±6.37 25.00
ab
±4.77 43.74
b
±6.34 0.13
cd
±0.03 2.06
abc
±0.23 2.66
c
±0.48 3:0104b 1:5104
Range 15–33 5.77–7.32 68–1029 1.4–567 10–70 10–70 7.5–181 0.01–0.41 0.60–3.80 0.1–14.9 2.0×10
1
–1.6× 10
6
For each parameter, means with the different letters (superscripts) are significantly different (P<0.05), using Duncan’s multiple range test
FCC faecal coliform count, WT water temperature, EC electrical conductivity, NTU nephelometric turbidity units, TDS total dissolved solids, TSS total suspended solids, PO
4
−
P
phosphate–phosphorus, NO
3
−
Nnitrate–nitrogen, BOD
5
5-day biochemical oxygen demand
Environ Monit Assess (2012) 184:3389–3400 3393

different sampling sites. The Pearson correlation coef-
ficients shown in a half matrix (Table 2) are the results
of statistical analyses for possible relationships between
faecal coliform counts and the ten physicochemical
parameters monitored. Table 3compares the mean
values of the WQPs assessed for the three water
bodies.
Higher water temperatures were observed for
the three water bodies during the rainy season
months of May through to October than during the
dry season months of March, April, November and
December. There was no apparent correlation
between water temperature and faecal coliform
counts (Table 2). There was, however, a positive
correlation between water temperature and each of pH
(r=0.30), turbidity (r=0.36), TDS (r=0.18), phos-
phate (r=0.19) and nitrate (r=0.44). Water tempera-
ture reflected changes corresponding to the seasons,
with significantly (P<0.05) higher values recorded
during the rainy season (Table 3). Since there was no
apparent correlation between faecal coliform counts
and water temperature, the significantly lower con-
centrations of faecal coliforms encountered at S5,
which recorded the highest temperatures (Table 1),
could be due to the water being held for an
appreciable time in the abstraction pipe. The
significant correlation between water temperature
and pH, turbidity, TDS, as well as nitrate agrees
with the knowledge that temperature affects other
properties of water by speeding up chemical
reactions and reducing the solubility of gases
(Jonnalagadda and Mhere 2001).
No significant correlation was discernible between
pH (range 5.77–7.32) and faecal coliform counts or
the other physicochemical parameters except for
water temperature (r=0.30). However, significantly
lower pH values were recorded during the dry season
(Table 3). Although not definitive, the pH of an
aquatic system is an important indicator of water
quality and the extent of pollution in the watershed
area. Unpolluted waters normally show a pH of about
7 and 8 (WHO 2008). The unexpectedly low mean
pH value (6.54) observed at S4 (Table 1) could be
attributed to possible, but unconfirmed, acidic dis-
charges at a fishermen’s shed. The general drop in pH
values in the dry season is not surprising. It could be
explained by the reduced water volume and higher
CO
2
concentrations.
The range of values recorded for electrical con-
ductivity recorded was relatively wide, from 68 to
1,029 μS/cm. Table 3shows that Samaru stream
manifested the highest conductance throughout the
study period, whereas Kubanni dam had the smallest
values. There was a significant positive correlation
between conductance and each of FCC (r=0.55),
TDS (r=0.46), TSS (r=0.39), phosphate (r=0.27),
nitrate (r=0.49) and BOD (r=0.67). The strongest
correlation (r=0.84) was between conductance and
chloride concentration. Electrical conductivity is a
reflection of the status of inorganic pollution and is a
Table 2 Correlation matrix of water quality parameters (Pearson correlation coefficients (r) of 228 samples per parameter)
Parameters FCC WT pH EC Turbidity TDS TSS Cl
−
PO
4
−
PNO
3
−
N BOD
5
FCC 1.00
WT 0.02 1.00
PH 0.05 0.30 1.00
EC 0.55 0.04 0.15 1.00
Turbidity −0.13 0.36 0.05 −0.13 1.00
TDS 0.31 0.18 0.02 0.46 −0.02 1.00
TSS 0.27 0.11 0.15 0.39 0.26 0.22 1.00
Cl
−
0.51 0.02 0.12 0.84 −0.04 0.47 0.50 1.00
PO
4
−
P0.30 0.19 0.03 0.27 0.28 0.05 0.11 0.21 1.00
NO
3
−
N0.25 0.44 0.14 0.49 0.03 0.41 0.31 0.54 0.22 1.00
BOD
5
0.51 0.10 0.10 0.67 −0.20 0.36 0.30 0.63 0.23 0.30 1.00
All values in bold print are significant (P<0.05)
FCC faecal coliform counts, WT water temperature, EC electrical conductivity, TDS total dissolved solids, TSS total suspended solids,
Cl
−
chloride, PO
4
−
Pphosphate–phosphorus, NO
3
−
Nnitrate–nitrogen, BOD
5
5-day biochemical oxygen demand
3394 Environ Monit Assess (2012) 184:3389–3400

Table 3 Comparison of mean values of water quality parameters for two seasons at the three water bodies
Water quality parameter Variables (mean±standard deviation)
Samaru stream Kubanni dam Kubanni river
Dry season Rainy season Dry season Rainy season Dry season Rainy season
WT (°C) 21.88
b
±1.28 27.28
a
±0.52 22.52
b
±1.86 28.04
a
±0.56 22.68
b
±1.06 27.73
a
±0.49
pH 6.50
b
±0.10 6.83
a
±0.06 6.54
b
±0.04 6.70
a
±0.04 6.63
b
±0.06 6.81
a
±0.03
EC (μS/cm) 466.93
a
±79.62 451.40
a
±55.42 155.98
a
±23.03 149.62
a
±14.94 221.27
a
±10.23 225.95
a
±14.21
Turbidity (NTU) 56.26
a
±15.50 177.05
a
±62.52 77.24
b
±24.57 181.3
a
±35.54 29.44
b
±4.60 157.10
a
±31.96
TDS (mg/l) 35.83
a
±5.96 26.69
a
±3.32 20.63
a
±4.33 24.17
a
±2.94 25.00
a
±2.69 27.00
a
±2.36
TSS (mg/l) 24.17
a
±4.84 32.78
a
±4.34 16.56
a
±2.63 19.58
a
±3.10 16.50
a
±1.96 20.33
a
±2.01
Chloride (mg/l) 71.27
a
±9.67 71.59
a
±10.23 27.73
a
±4.75 24.74
a
±2.46 29.13
a
±3.97 28.94
a
±2.40
PO
4
−
P (mg/l) 0.17
a
±0.04 0.26
a
±0.03 0.15
a
±0.03 0.17
a
±0.02 0.14
a
±0.02 0.17
a
±0.02
NO
3
−
N (mg/l) 1.93
a
±0.31 1.84
a
±0.09 1.67
a
±0.22 1.75
a
±0.15 1.70
b
±0.18 2.23
a
±0.21
BOD
5
(mg/l) 6.41
a
±1.36 4.62
a
±0.81 1.06
a
±0.23 1.34
a
±0.15 1.62
a
±0.51 1.89
a
±0.33
FCC (MPN/100 ml) 2:4105a 1:11053:4105a 1:21055:6103a 2:61031:4103a 7:31022:8104a 1:11043:5103b 1:4103
For each parameter, means with the different letters (superscripts) are significantly different (p<0.05), using Student’sttest
FCC faecal coliform counts, WT water temperature, EC electrical conductivity, NTU nephelometric turbidity units, TDS total dissolved solid, TSS total suspended solids, PO
4
−
P
phosphate–phosphorus, NO
3
−
Nnitrate–nitrogen, BOD
5
5-day biochemical oxygen demand
Environ Monit Assess (2012) 184:3389–3400 3395

measure of TDS and ionized species in water (WHO
2008). The significant positive correlation between
conductance and TDS (r=0.46), chloride (r=0.84) as
well as nitrate (r=0.49) agrees with the above
relationship. The significantly higher conductance
values recorded at sampling site S1 (Table 1), relative
to other sites, were apparently due to the pollutants
from municipal wastewater and runoffs from Samaru.
A similar pattern was observed for turbidity at the
three water bodies, and the range was wide for all the
watercourses: Samaru stream (2.6–799 NTU),
Kubanni dam (1.4–567 NTU) and Kubanni River
(10.2–507 NTU). The highest values were recorded
during the rainy season months. No apparent correla-
tion was established between turbidity and faecal
coliform counts. This could be attributable to the fact
that turbidity is caused not only by the presence in
water of microorganisms, but also of particulate
matter, such as clay, silt and colloidal particles
(WHO 2008). This is reflected by the significantly
higher mean turbidity value observed at S3. Sampling
site S3 was influenced by water from upper Kubanni
River which is heavily affected by soil particles from
the heavy erosion of its catchments.
There was a positive correlation between turbidity
and water temperature (r=0.36), TSS (r=0.26) and
phosphate (r=0.28). The observed turbidity levels
were well above 5.00 NTU, the upper limit for
drinking water. Turbidity was most affected by storm
events, resulting in the highest values being recorded
during the rainy season. Variations recorded within
the season could be attributable to possible settling
that occurred, depending on the time lapse between
storm event and sampling. The significantly higher
turbidity values encounteredintherainyseason
agrees with the finding of Kistemann et al. (2002)
who studied, the microbial load of water reservoir
tributaries in Germany, during extreme rainfall and
runoff, and observed a remarkable increase in
turbidity and microbial loads following rainfall and
runoff events.
A positive correlation, shown in Table 2,
existed between TDS values and those of other
parameters: faecal coliform counts (r=0.31), water
temperature (r=0.18), conductance (r=0.46), BOD
5
(r=0.36), as well as its constituents including nitrate
(r=0.41) and chloride (r=0.47). The level of TDS
reflects the pollutant burden of the water. High levels
of dissolved and suspended solids in water systems
increase the biological and chemical oxygen demand
(Jonnalagadda and Mhere 2001). Significantly higher
TDS values were recorded in the non-rainy periods
when water flow was low. This reflects with the
finding of Albek (2003), who demonstrated that TDS
correlates with water flow.
The concentrations TSS showed significant posi-
tive correlation with those of FCC (r=0.27) and
conductance (r=0.39) (Table 2). For all three water
bodies, levels of TSS peaked in July, and
decreased as the months progressed. The TSS
values were generally higher during the rainy
season, and particularly after storm events. The
significantly higher mean TSS value observed at
Samaru stream compared to ABU Dam and
Kubanni River could be attributable to runoff
waters from the heavily littered Samaru village.
Throughout the study period, distinctly lower
chloride levels were recorded at Kubanni dam and
river compared to Samaru stream (Table 1). Both the
stream and dam showed a similar trend, with the
chloride concentration decreasing as the months
advanced to November, after sharp increases in April.
In all, the chloride level had a wide range, 7.5–
181 mg/l, and showed a significant positive correla-
tion with FCC (r=0.51), conductance (r=0.84), TDS
(r=0.47), BOD
5
(r=0.63). Chloride concentration is
higher in wastewater than in raw water because
sodium chloride, a common component of the human
and diet, passes unchanged through the digestive
system (WHO 2008). In line with this, Samaru
stream, which is heavily influenced by domestic
wastewater, recorded a significantly higher mean
value than Kubanni dam and river.
The chloride levels in unpolluted waters are often
below 10 mg/l (Tebbut 1992), but mean concentra-
tions observed in this study ranged from 18.2 to
95.8 mg/l. The strong positive correlation (r= 0.84)
between chloride and electrical conductivity reflects
the fact that chloride increases the electrical conduc-
tivity of water, and thus its corrosivity (WHO 2008).
The significant correlation between chloride and TDS
reflects the fact that chloride is one of the principal
anionic constituents of dissolved solids.
The highest mean value for phosphate (0.41 mg/l)
was observed in March at Samaru stream and the
smallest value (0.08 mg/l) at ABU Dam in the rainy
month of August. There was a significant positive
correlation between phosphate–phosphorus values
3396 Environ Monit Assess (2012) 184:3389–3400

and FCC (r=0.30), nitrate–nitrogen (r=0.22) and
biochemical oxygen demand (r=0.23). Concentra-
tions of total phosphate in the water samples exceeded
the standard limit (0.1 mg/l) of the US Public Health
Standards (Solaraj et al. 2010) at 11 of the 12 sites. It
has been documented (Ekholm and Krogenus 1998)
that municipal wastewater contains substantial
amount of phosphorus contributed by human urine
and detergents. This may account for the significantly
higher phosphate concentrations observed at Samaru
stream, which is heavily affected by municipal
sewage. The highest value was recorded in March
when the water in the stream was largely wastewater.
High concentrations of phosphates and nitrates
increase the growth of vegetation in water systems
and elevate oxygen demand (McEldowney et al.
1993). However, besides its contribution to eutrophi-
cation and toxic algal blooms, phosphate does not
have notable adverse health effects (WHO 2008).
There was a significant correlation (r=0.30) between
PO
4
−
P and FCC, and reports (Sathasivan et al. 1997;
Lehtola et al. 1999) have shown that there are regions
of the world where it is predominantly phosphorus
that determines microbial growth in drinking water.
The positive correlation between phosphate and
BOD
5
showed that enrichment with nutrients leads
to eutrophication in aquatic systems and inversely
affects the dissolved oxygen content of water.
Excess nitrogen is also an important contributor
to the problem of aquatic eutrophication. Nitrate
levels were generally higher in Kubanni River and
Samaru stream than at the dam, and significantly
higher (P<0.05) in Kubanni River during the rainy
than in the dry season (Table 3). Samaru stream
receives much organic matter from municipal waste-
water discharging into it. Higher nitrate levels in
Kubanni River are attributable to water from farm-
lands containing nitrate residues from manure and
fertiliser used in agriculture along the banks of the
river, and to organic loadings from the ABU sewage
treatment works and the Zango abattoir.
The level of algal growth observed in Kubanni
River should not be overlooked. Blooms of various
planktonic species are directly or indirectly hazardous
to human and animal health (Hitzfeld et al. 2000;
Carmichael et al. 2001; Hunter 2003). A recent report
(Chia et al. 2009) showed that Kubanni River had
microcystin concentrations higher than the acceptable
limit (1 μg/l) for potable water. Moreover, infants can
die of methaemoglobinaemia when they imbibe water
containing nitrate and that the mortality risk is high
when the nitrate concentration exceeds 10 mg/l of
nitrate–nitrogen (WHO 2008). This risk is multiplied
if the water contains pathogens and the child suffers
diarrhoea (Knobeloch et al. 2000). However, the
observed nitrate levels (range, 0.60–3.80 mg/l) did
not exceed the standard limits, and therefore, it
appears that the waters represent no nitrate-mediated
public health hazard.
Table 2reveals a significant positive correlation
between nitrate values and BOD
5
(r=0.30). Mea-
surement of BOD has long been the basic means
of determining the degree of organic pollution in
aquatic systems, and a river is said to be
unpolluted if its water has a BOD
5
of 2 mg/l or
less (Hobson and Poole 1988). The mean BOD
5
values generally decreased at the onset of the rainy
season in May and as the season progressed, a steady
increase occurred from July, with values peaking in
October. As also shown in Table 2,therewasa
significant correlation between BOD
5
levels and faecal
coliform counts (r=0.51) as well as other parameters,
including conductance (r=0.67), TDS (r=0.36), TSS
(r=0.30), chloride (r=0.63), phosphate (r=0.23) and
nitrate (r=0.30).
The BOD
5
values for the three water bodies were
significantly different, indicating that the stream and the
river compared to the dam were of significantly inferior
quality during both the rainy and dry seasons.The lower
BOD
5
values (<5.00 mg/l) recorded at Kubanni dam
reflect a lower burden of organic pollution. Due to the
high volume of water, the extent of pollution therein
may not appear severe. The pollution potential gains
significance when the dam is considered to be a
source of drinking water. Significantly higher mean
BOD
5
values were recorded at S1 (8.60±1.29 mg/l),
S2 (6.06±0.63 mg/l), S8 (3.66± 1.03 mg/l) and S12
(2.66±0.48 mg/l) compared to the other sites (Table 2)
throughout the study period. This shows that the chief
sources of faecal pollution in the study sites were:
municipal sewage and runoffs from Samaru village
and Dan Fodio Hall failing septic tank, in Samaru
stream, as well as ABU sewage treatment plant, the
abattoir effluent and runoffs from Jama’a, in Kubanni
River.
The presence of faecal coliforms is an indicator of
the bacteriological quality of water. Table 1reveals
that high FC counts were recorded at S1 (affected by
Environ Monit Assess (2012) 184:3389–3400 3397

municipal sewage and urban runoff) and S2, whereas
a lower mean value was obtained at S3, which lies
downstream of the other two sites. While the
significant decrease in faecal coliform counts and
BOD (and thus pollution) at S3 could be attributable
to the self-purification process of rivers, the observed
lack of a significant difference in faecal coliform
levels at S1 and S2 could be explained by the effect of
the failing septic tank serving student hostels at
the university, which discharged into the stream at
a point about 10 m upstream of S2. Also, the
significantly higher faecal coliform counts recorded
atS8(comparedtositesS9,S10andS11,all
located in the Kubanni River) reflects the effect of
the malfunctioning wastewater treatment plant that
was discharging raw sewage into the river at a
point upstream of S8. As the river flowed further
away from that point source of pollution, the FCC
decreased as expected. The significantly higher
counts again encountered at S12 were attributable
to effluents from Zango abattoir and runoff from
Jama’a. At the dam (S4–S7), the significantly
lower (P<0.05) FCC recorded at S5 could be due to
prolonged storage in the abstraction pipe and to
dying-off or possible transformation of bacterial
cells into viable but non-culturable states (Wang
and Doyle 1998).
A uniform decline in FCC was encountered in
December in all the water sources, which coincided
with the usual drop in population and anthropogenic
activities associated with migration of urban/semi
urban dwellers during the Christmas and New Year
holiday period. Student’sttest revealed that Kubanni
River had significantly higher (P<0.05) FCC (MPN/
100 ml) in the dry season than during the rainy season
(Table 3). This can be explained by the combined
effect of STP effluent and reduced water volume that
usually associated with stretches of river downstream
of dams. Dams disturb the natural flow regime of
rivers, giving rise to reduced water volume and river
flow downstream of the dam (Rowntree and Dollar
2008). This reduction in river water volume was
usually more pronounced in non-rainy periods.
The correlations (Table 2) between the physico-
chemical and bacteriological parameters agree with
previous findings made in India (Pathak et al. 1993)
and validate the use of bacterial density as the
basic criterion for the quality assessment of
freshwater sources. It must be noted, however,
that recent studies have shown that bacteria cannot
be used as indicators of viral particles in water
(Jurzik et al. 2010).
Faecal coliforms were present in all the water
samples, and none of the water samples met interna-
tional standards for human consumption. Faecal
coliform count should be zero (per 100 ml) of sample
in all drinking water supplies, piped or unpiped,
treated or untreated, and <1,000 faecal coliforms/
100 ml for unrestricted irrigation (Bouwer et al. 1999;
WHO 2008). Therefore, these waters are not suitable
for human consumption and the irrigation of vegeta-
ble and salad crops without treatment. This raises
concern over the health of the consumers of such
crops irrigated and washed with Kubanni River water,
and that of fishermen and villagers along the course
who were observed to take water, at points they
consider clean, for drinking and domestic purposes
from these sources. Waterborne and food-borne
diseases, such as typhoid fever, cholera and diarrhoea
are prevalent in the area. We have previously reported
a high prevalence of Escherichia coli O157 in the
Kubanni River, which is used for commercial fresh-
produce irrigation (Chigor et al. 2010a), and the
isolation of multidrug-resistant strains of the same
pathogen from children with diarrhoea in Zaria
(Chigor et al. 2010b).
Conclusion
The results presented here demonstrate that Samaru
stream, ABU Dam and Kubanni River were
continually being polluted by both point and
diffuse sources of faeces, particularly by domestic
sewage, storm runoffs, failing septic tanks and an
inefficient sewage treatment plant. Based on the
significant differences in faecal coliform counts
and BOD
5
, Samaru stream and Kubanni dam were
the most and least polluted, respectively. The quality
of the waters generally exceeded microbiological
infection risk limits proposed in water quality guide-
lines with respect to their use for domestic purposes,
recreation and fresh-produce irrigation. Chemical
fertilisers significantly affected the nitrate level in
the Kubanni River. We therefore recommend that
farming and the use of inorganic fertilisers should be
discouraged or restricted around these waters. Proper
sewage treatment and river quality monitoring are
3398 Environ Monit Assess (2012) 184:3389–3400

needed to warn against hazards to public health and
vulnerable water resources.
Acknowledgements The authors thank Professor J.U. Umoh of
the Department of Veterinary Microbiology and Public Health,
Ahmadu Bello University for providing some microbiological
media and H.C. Alika of the Public Health Laboratory, Depart-
ment of Water Resources and Environmental Engineering, ABU,
for the technical assistance. We are also grateful to the Govan
Mbeki Research and Development Centre (GMRDC) of the
University of Fort Hare, Alice, South Africa, forthe facilities used
in the course of preparing this manuscript.
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