GIS-BASED ASSESSMENT AND MAPPING OF NOISE POLLUTION IN BARIGA AREA OF LAGOS STATE, NIGERIA

Abstract

Globally, environmental issues are assuming high proportions with antecedent impacts and consequences. Noise pollution is one of the most critical environmental challenges affecting human health. This study aims at utilising the spatial technology of GIS in assessment and mapping noise pollution in Bariga suburb of Lagos State, southwestern Nigeria. A total of 31 sampling locations were designed for the collection of data classified based on residential, educational, traffic and commercial land uses. The WHO standards were used as limits for the designated land uses. Noise recordings were conducted using three periods of the day – morning, noon and evening. GIS-based assessment using IDW spatial interpolation technique was used for the mapping of the recorded noise values across the study area. The lowest daily average of noise ranged between 67.2 dB(A) and 76.7 dB(A) across all the land uses. Lowest values were recorded in residential areas while higher values were detected in commercial and traffic areas. The computed noise index showed that all parts of the study area returned high index of above 55 dB(A) for comfortability and low annoyance response to noise when compared with WHO standard. Thus, it was recommended that strict design of noise index should be developed for the study area for safety and sustainable environmental development.

Full text

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 154
GIS-BASED ASSESSMENT AND MAPPING OF NOISE POLLUTION IN
BARIGA AREA OF LAGOS STATE, NIGERIA
Akintuyi, A. O*; Raji, S. A.** Adewuni, D*. and Wunude, E. O.*
*Department of Geography, Faculty of Social Sciences, University of Lagos
**Laboratory for Remote Sensing and Geographic Information System, Department of
Geography, Faculty of Social Sciences, University of Lagos.
Abstract
Globally, environmental issues are assuming high proportions with antecedent impacts
and consequences. Noise pollution is one of the most critical environmental challenges
affecting human health. This study aims at utilising the spatial technology of GIS in
assessment and mapping noise pollution in Bariga suburb of Lagos State, southwestern
Nigeria. A total of 31 sampling locations were designed for the collection of data classified
based on residential, educational, traffic and commercial land uses. The WHO standards
were used as limits for the designated land uses. Noise recordings were conducted using
three periods of the day – morning, noon and evening. GIS-based assessment using IDW
spatial interpolation technique was used for the mapping of the recorded noise values
across the study area. The lowest daily average of noise ranged between 67.2 dB(A) and
76.7 dB(A) across all the land uses. Lowest values were recorded in residential areas while
higher values were detected in commercial and traffic areas. The computed noise index
showed that all parts of the study area returned high index of above 55 dB(A) for
comfortability and low annoyance response to noise when compared with WHO standard.
Thus, it was recommended that strict design of noise index should be developed for the
study area for safety and sustainable environmental development.
Keywords: Noise Mapping, Noise mapping Index, GIS, Environmental Pollution, IDW
INTRODUCTION
Environmental pollution such as air, water,
hazardous waste and noise pollution has
always been of global concern affecting both
the public’s health (Banerjee et al, 2009) and
the planet’s fragile ecosystems (Butler, 2004;
Ugwuanyi et al, 2004). Environmental
pollution is assuming dangerous proportions
all through the globe and Nigeria as a country
is not immune to this problem. The
concentration of environmental pollution is
significantly increasing and causing serious
threat to the quality of the environment
(Wing and Kwong, 2006). Management of
environmental pollution is a challenge,
although there are many management
techniques, the problem of environmental
pollution remains relatively unchanged.

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 155
One of the serious issues of
environmental pollution is noise. Noise
entomologically is derived from the Latin
word ‘nausea’ denoting ‘undesirable
sound’ or sound that is loud, unpleasant or
unexpected’ (Chauhan et al, 2010). Noise
is an unwanted sound experience and
noise pollution in large urban areas is
regarded as a growing problem of
communities experiencing development in
built environment. Noise pollution is a
type of energy pollution in which audible
distracting sounds are projected from a
single or multiple sources leading to
obliteration of any natural process or
causes human harm. Studies have shown
that more than 20% of the world
population lives under unacceptable noise
levels and nearly 60% of the European
population is exposed to high noise levels
during the day (Silvia et al, 2003). Most
cities of Africa are also exposed to the
threats of urban noise particularly from
vehicles and related traffic activities
(Ugwuanyi et al, 2004). Saadu et al (1998)
affirmed that most metropolitan cities of
southwestern Nigeria are vulnerable to
high noise indices; thus residents are
susceptible to hearing and audibility
challenges owing to the intensity of the
urban transport activities.
Generally, high exposure to noise can
cause feelings of annoyance and irritation,
damage to auditory mechanisms, number
of health related effects like physiological
disorders, disturbances of daily activities
and performances, hypertensions and
ischematic heart diseases (Carter, 1996).
Space technological applications in form of
Geographic Information Systems (GIS)
have been employed to map, monitor and
model the dynamics of acoustic patterns
across the globe. Mapping of noise with
the utilization of GIS commenced in the
mid-90s in which two approaches were
employed for development of noise maps
– in-situ noise metre measurement and
prediction modelling tools (de Henk et al
2003; Mohammed et al 2005). GIS tools
have made it possible to generate noise
prediction maps with respect to location
of people and their sensitivity to noise (de
Henk et al, 2003).
In Lagos, all governmental efforts
aimed at curbing noise pollution have
yielded little or no impact. In many parts
of Lagos metropolis particularly the
traditionally residential area such as
Bariga, the issue of having a safe, secured
and friendly environment with respect to
noise is at the lowest informational ebb. In
addition, the sources of noise although
visible are not considered harmful to
human health. Thus, noise pollution maps
are not readily available, not even for any
sort of environmental monitoring or
academic purposes. It is on this background
that this study is set out to map the spatial
distribution of noise level and its impact on
the immediate environment in Bariga. The
associated objectives are; to understand the
different noise levels within the identified
land uses; to capture the quantum and
distribution of noise in the study area within
a given time of the day; and provide noise
mapping index for the purpose of
environmental decision-making.
MATERIALS AND METHODS
Study Area
Bariga is a suburban community located
within Somolu local government area of

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 156
Lagos state, southwestern Nigeria. It is
geographical defined within latitudes 60
31 20 and 60 33 30 North and
longitudes 30 22 0 and 30 24 0 East (Fig.
1). It is bounded in the east by the Lagos
lagoon, to the west by a Mushin Local
government and the Lagos Mainland and
Kosofe local government areas to the
south and north respectively. The climate
of the area fall within Koppen’s AF wet
equatorial forest climate. Throughout the
year, temperature hardly falls below 200 C
but averages about 220C. The average
annual rainfall is above 1830mm with
some local variations. Elevation around
Bariga varies from about 2 meters to 10
meters above sea level. The study area can
be categorized as residential with spots of
commercial and educational land uses. As
a key suburb of Lagos metropolis,
movement of people from home to
workplaces is the most essential source of
traffic and transportation related noise.
Infrastructures particularly road-based
have remained quasi-constant over the
years. Increase in the population of
dwellers has not been complemented with
improvement on road traffic and related
facilities. Thus, moving vehicles oftentimes
create the most noise.
Fig. 1: The Study Area in Context of Lagos Metropolis and Nigeria
Source: (Laboratory for Remote Sensing & Geographic Information System, UNILAG 2013)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 157
Data, Sources and Characteristics
Orthorectified IKONOS image of 2007
acquired from the archives of Laboratory
in Remote Sensing and GIS, Department of
Geography, University of Lagos was used
as baseline data as well as delineation of
roads. Extech 407730 sound level meter
was used in collection of acoustic level
data. A handheld Garmin 750 GPS was
used for geolocation data and geotagging
the noise values to the particular area of
data collection. The GPS was configured to
UTM Zone 31N based on WGS 1984 global
datum which matches the location of the
study area.
Noise level sampling and data
computation
The sampling approach used is local
administrative units coupled with land use
category. Each land use was identified at
the building footprint level i.e. based on
the use of the particular landed structure.
Thus, residential, educational, traffic and
commercial land uses were integrated into
the sampling. Table 1 details the land use
categories involved, the GPS coordinates
and locations of data collection. Sound
pressure values in decibels - dB(A) i.e.
decibels in A-weighted scale were
recorded for three periods of the day –
morning, afternoon and evening. The data
was collected between May 6th 2013 and
June 1st 2013, on Mondays, Wednesdays
and Saturdays, incorporating working days
of the week and weekends. The noise
assessment was conducted during
morning (7-9 am), afternoon (12-2pm) and
evening (5-7pm). Systematic noise indices
were developed for appropriate and
representative mapping as demonstrated
by Saadu et al (1998) and Banerjee et al,
(2009). These are;
 Leq-m, Leq-n and Leq-e: hourly A-weighted equivalent sound level for the morning,
noon and evening period;
 Lm-n-e: mean sound level for morning, noon and evening (MNE);
 NI: noise index, and
 Lmin and Lman: minimum and maximum noise level during the sampling period
An estimation of the continuous sound level denoted as Leq, was estimated from the
recorded acoustic pressure level, this was necessary owing to challenges encountered in
continuous data collection. This was computed using the equation;
󰇛󰇜 󰇟󰇛
󰇛󰇜

 󰇠……………………………….…………….… (1)
where,
 is the noise level ith reading and N denotes total number of recorded samples. These
data were generated for the morning, noon and evening periods of the day as stated
above. The morning, noon and evening Leq equivalent were also used to compute the Lm-n-
e values, which is a 12-hour equivalent on a continuous basis, and 5 dBA is added to
evening time noise as a penalty mark. The Lm-n-e is expressed as:
󰇛󰇜    󰇡
󰇢󰇟󰇛
󰇜  󰇛
 󰇜  󰇛
󰇜󰇠 ……………….…… (2)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 158
The noise index is a measure of annoyance behaviour of humans exposed to
environmental noise, and this is estimated using the following mathematical expression;
󰇛󰇜 󰇛  󰇜 󰇛 󰇜……………………………………………….. (3)
where  (designated background noise) and  (highest noise) represent the percentile
noise levels exceeded for 10% and 90% of the sampling period respectively.
Table 1: Distribution of noise mapping locations differentiated on Land use classes with
respective GPS coordinates
Land use
category
ID
No
GPS Coordinates
Location name
Land use
category
ID
No
GPS Coordinates
Location name
X
Y
X
Y
Residential
2
542748
721627
Solanke Street
Traffic
8
543932
722258
Arobadade Street
3
541002
723679
Obanikoro Street
14
543715
722550
Ososa Avenue
4
542835
723992
Adeola Raji
Avenue
15
544161
722715
Odunsi Street
9
543711
722921
Ayoka Street
18
540955
724602
Olujobi Street
10
542067
723313
Ojelade Street
21
542477
722312
Olorunkemi
Street
11
542630
723603
Lanre Awolokun
Road
22
543360
722163
Ilaje Bus Stop
13
541544
723358
Bawala Street
24
542764
722544
Ladi-lak Bus Stop
17
542768
722952
Ayodele Street
27
540886
724064
Oyefeso Street
25
543721
724126
Oke-Owo Street
29
543700
721887
Ilaje Road
26
543522
721459
Oshinfolarin Street
30
542911
721196
Adeyinka Osijo
Street
Educational
23
543734
721133
Community Road
Commercial
7
543275
722448
Abule Okuta Road
31
543262
722288
Deji Aladejobi
Street
16
543485
723215
Gbagada Road
12
543272
721719
Council Obule
Street
19
542678
722659
Tapa Street
Traffic
1
543322
723703
Safuratu Sekoni
Street
20
543787
723606
Diya Street
5
541696
723862
Fola Jinadu Street
28
543434
722509
Bariga
Roundabout
6
542274
722597
Kusa Street
GIS interpolation technique for mapping
ArcGIS 10.1 software was used principally
for mapping the recorded noise levels
based on the computation itemised above.
These values were mapped as points data
relative to the specific location. To capture
the adjoining areas and to cover the
entirety of the study area, a contouring
method was used based on IDW (inverse
distance weighted) spatial interpolation
technique. Generally, interpolation
predicts cell values in a raster format using
a given albeit limited number of sample
data. It is a veritable tool for prediction of
unknown values for a given geographic
point data which in this study is noise
(Pamanikabud 1996, Anile et al. 2003,
Yilmaz et al. 2005). IDW however explicitly
implements the law of geography, which is
pivoted on the hypothesis that closer

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 159
things are more related than those farther
apart. For its prediction, IDW utilises the
given values surrounding the predicted
location. It predicts that each given point
has a local influence that shrinks with
space; thereby giving greater weights to
points closest to the prediction location,
based on distance decay effect. This
process leads to the procedure being
referred to as inverse distance weighted.
This technique was applied to measure the
spatial distribution and range of acoustics
in the area for the three periods of the
day.
DATA ANALYSIS AND RESULT
DISCUSSION
Overall noise level in Bariga
The background understanding of this
study is that different land uses generate
different levels of noise. This assertion was
centred on the WHO recommendations on
the impact of environmental noise on
humans. As shown in Table 2, there are
four different land uses identified in Bariga
with different noise values related to
sampling period of the day and the departure
from the global standards. The noise level
(Leq) for each land use was coalesced with the
average daily noise (Lm-n-e). Differences in
time coupled with intensity of anthropogenic
activities typify the quantum of noise
generated. For instance, in the morning
period, traffic areas generate the highest
noise level while the residential areas have
the least. In the afternoon, the same pattern
subsists. At the evening period, the noise
level increases skewing towards commercial
and educational areas. Similar patterns were
detected for daily average and Lm-n-e. The
daily average values are similitude of these
observations. Since there are no national
environmental noise standards, the WHO
guidance values were used as assessment
parameters to detect the extent of
exceedance.
Table 2: Summarised mean noise level (Leq and Lm-n-e) for each land use category
Land use Category
Sampling Period
and benchmark
limits
Residential
Commercial
Traffic
Education
Morning
67.2 ± 10.2
72.7 ± 9.1
76.7 ± 18.7
71.3 ± 2.1
Noon
66.2 ± 8.2
77.1 ± 12.0
77.8 ± 19.3
73.1 ± 6.6
Evening
71.6 ± 9.3
82.1 ± 13.8
82.1 ± 21.5
76.9 ± 12.9
Daily average
63.5
73.84
71.1
73.77
Lm-n-e
70.14
84.02
73.41
80.8
WHO Limit (Day)
55
70
70
55
WHO Limit (Night)
50
65
65
50
All noise level in dB(A) units

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 160
Land use and environmental noise levels
An assessment of the noise levels
measured with respect to different land
uses will provide a closer view of the
particular anthropogenic activity that is
exposed to noise and its impact on the
environment. Table 3 displays the average
noise levels (Leq) in residential areas as
well as the extent of departure from the
WHO standards. In all the sampled
location points, the detected noise levels
exceed the WHO standards for all the
periods of the day. All the day, Gbagada,
owing to proximity to Federal highway,
recorded the highest value. In the morning
it recorded 67.2 dB(A) with 12.2 dB(A)
exceedence value, noon 68.5 dB(A) with
exceedence value of 13.5 dB(A) and by the
evening time it returned 71.6 dB(A) with
excedeence of 21.6 dB(A). The placement
of sampling instrument could be a factor
for this recorded value. The daily average
is a similitude of the periodic Leq values.
However, the noise index values showed
that the proximity of Lanre Awolokun
Street and Ayoka Street to noisome
activities could have a particularly high
health effects on the residents. These
areas have 87.46 and 85.41 noise index
values respectively.
Table 3: Mean noise levels and noise index in residential areas
Sampling location number
2
3
4
9
10
11
13
17
25
26
Morning
61.9
61.1
57.0
66.8
60.6
67.2
58.8
62.3
67.2
59.4
Exceedence
factor
6.9
6.1
2
11.8
5.6
12.2
3.8
7.3
12.2
4.4
Noon
61.9
59.9
59.9
66.2
58.0
68.5
61.2
60.5
63.6
61.4
Exceedence
factor
6.9
4.9
4.9
11.2
3
13.5
6.2
5.5
8.6
6.4
Evening
65.9
65.9
65.4
70.7
65.0
71.6
62.3
63.4
67.6
64.6
Exceedence
factor
15.9
15.9
15.4
20.7
15
21.6
12.3
13.4
17.6
14.6
Daily average
63.24
62.30
60.77
67.89
61.20
69.09
60.78
62.05
66.13
61.80
Lm-n-e
71.25
70.25
68.6
76.21
69.06
77.49
68.62
69.98
74.34
69.71
NI
77.51
75.92
73.30
85.41
74.04
87.46
73.33
75.49
82.43
75.06
The detected Leq values in areas with high
traffic activity particularly in road
transport areas in Bariga are documented
in Table 4. Out of the 13 sample locations,
three locations returned values below the
set standards, an indication of low traffic
activity during specific periods of the day
in those areas. Ilaje Bus Stop (location 22)
recorded all-day high values (morning 76.7
dB(A), noon 77.8 dB(A), and evening 82.1
dB(A)). These values are directly
proportional to the intensity of human
activities observed in the area (informal
market and high vehicular movement). At
most times of the day, the market
activities often obstructs traffic, which
eventually leads to almost all-day traffic in
the day. Areas with lowest values such as

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 161
Kusa Street, Fola Jinadu Street, Olujobi
Street, Ososa Street have lower traffic
activities during the day compared to Ilaje
Bus Stop. The noise index values
computed for the traffic areas are higher
within the high risk areas designated by
the WHO. In fact, there are two areas with
extremely high index values above 100
dB(A). Ilaje Bus Stop and Ladi-Lak Bus Stop
have noise index values of 104.1 dB(A) and
102 .5dB(A) respectively indicating a very
high index of annoyance response.
Table 4: Mean noise levels and noise index in traffic areas
Sampling location number
1
5
6
8
14
15
18
21
22
24
27
29
30
Morning
73.4
64.2
58.0
71.3
67.7
71.5
59.6
65.0
76.7
75.8
74.7
70.3
73.6
Exceedence
factor
3.4
-5.8
-12
1.3
-2.3
1.5
-10.4
-5
6.7
5.8
4.7
0.3
3.6
Noon
74.5
66.5
58.5
70.9
68.3
72.0
61.5
64.0
77.8
77.3
76.1
72.7
75.5
Exceedence
factor
4.5
-3.5
-11.5
0.9
-1.7
2
-8.5
-6
7.8
7.3
6.1
2.7
5.5
Evening
78.4
68.4
60.6
74.3
72.1
74.7
64.4
68.4
82.1
80.7
76.6
74.6
79.9
Exceedence
factor
13.4
3.4
-4.4
9.3
7.1
9.7
-0.6
3.4
17.1
15.7
11.6
9.6
14.9
Daily
average
75.45
66.36
59.05
72.17
69.36
72.73
61.82
65.80
78.88
77.94
75.81
72.53
76.34
Lm-n-e
84.22
74.58
66.76
80.75
77.77
81.35
69.73
73.98
87.85
86.86
84.61
81.13
85.17
NI
98.26
82.81
70.38
92.69
87.91
93.64
75.09
81.86
104.10
102.50
98.88
93.30
99.78
Leq values and related statistics for the
commercial and educational areas are
documented in Table 5. Bariga is
fundamentally a residential area with few
commercial land uses. The identified areas
are mere clusters of low-income shopping
centres and traditional markets, thus 5
sampling points were identified. In the
morning period, Diya Street recorded the
highest value of 75.8 dB(A) with
exceedence value of 5.8 dB(A). This was
followed by Gbagada Road with 75.4 dB(A)
and exceedence value of 5.4 dB(A). Tapa
Street had the least value of 63.6 dB(A)
which is below the WHO standards. By
noon, the Leq values increased drastically
with Gbagada Road recording the highest
value of 78.70 dB(A). At the evening
period, higher values were recorded with
Gbagada Road having the highest value of
82.14 dB(A) and exceedence value of
17.17 dB(A). The average Leq values
showed that Gbagada Road recorded
highest value of 90.72 dB(A) with
respective high noise index of 103.87
dB(A) which is higher than the mean index.
Educational areas are anticipated to have
lower sound levels since tranquillity is
required in such areas. Three of such areas
were captured in this study as shown in
Table 5. All the educational areas returned
high Leq values with Community Road
having the highest average value.
Similarly, the noise index values are also
high with the lowest value of 84.42 dB(A)
and highest value of 94.19 dB(A).

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 162
Table 5: Mean noise levels and noise index in commercial and educational areas
Sampling location number and land use
Commercial area
Educational area
7
16
19
20
28
23
31
12
Morning
72.7
75.4
63.6
75.8
72.3
69.20
71.30
71.40
Exceedence factor
2.7
5.4
-6.4
5.8
2.3
14.2
16.3
16.4
Noon
73.10
78.70
65.10
77.10
72.00
73.10
69.30
66.50
Exceedence factor
3.1
8.7
-4.9
7.1
2
18.1
14.3
11.5
Evening
76.31
82.14
68.27
79.93
75.1
76.87
72.57
64.01
Exceedence factor
11.31
17.14
3.27
14.93
10.1
26.87
22.57
14.01
Daily average
74.04
78.75
65.66
77.61
73.15
73.06
71.06
67.30
Lm-n-e
85.74
90.72
76.84
89.52
84.8
82.95
77.74
78.38
NI
95.87
103.87
81.62
101.94
94.35
94.19
90.80
84.42
Spatial distribution of noise in Bariga
The tabulated details of noise distribution of
Bariga create a need to further understand
the place-based spatial distribution of noise.
Fig. 2 is a cartographic representation of the
Leq of the morning, noon and evening period
of the day based on the location of
connected communities in Bariga. Across the
study area, the mapped values indicate high
noise values above the WHO standards
except in few areas where low values were
detected.
(a)
(b)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 163
Fig. 2: The distribution of Noise (Leq) values in Bariga for the three periods of the day:
morning, afternoon, and evening.
With the observed trend of high noise
values, the morning recorded the least Leq
values. The mapped values range between
64.5 dB(A) to 87.6 dB(A) depicting lower
and higher limits for the period.
Residential communities such as Pedro,
Owotutu, and parts of Gbagada recorded
the lower limits. Areas with higher Leq
values include Bariga, Akoka, Abule Okuta,
Obanikoro and Apelehin. These could be
related to high transportation and
commercial activities that is gradually
building up in these areas. Usually, noise in
these areas are triggered by early-risers-
to-work attitude of the residents mostly
white-collar job workers and market men
and women who travel to wholesale
markets in parts of Lagos to buy goods
that will be resold to residents in Bariga.
The intensity of the afternoon noise is a
little higher than the morning period but
well distributed compared to the morning
period which is observable within the
traffic areas. This was observed in the
same areas with high Leq in the morning.
Apelehin, Abule Okuta, Bariga and Akoka
owing to high transportation activities
have high Leq values. Thus, they are
susceptible to noise disturbances in the
noon period of the day despite having a
high residential cum educational land use
structure. The observed crude values of Leq
in the evening period depict a form of
spatial skewness towards the traffic areas.
It can be affirmed that Leq values points to
high environmental noise in the entire
Bariga settlement triggered by high
transportation activity combined with
intense local commercial activities.
(c)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 164
Fig. 3: The index of Noise (a) Lmin and (b) Lmax in Bariga
In order to weigh the temporal assessment of
environmental noise in Bariga, it is essential
to examine the detected limits of noise. The
overall minimum (Lmin) and maximum (Lmax)
limits of noise detectable in Bariga were
captured and depicted in Fig. 3. The lower
limits (Lmin) shows the baseline minimum
noise levels detected in the area while the
upper limits depicts the converse. The Lmin
range 63.35 dB(A) to 71.31 dB(A) captured in
neighbourhoods such as Pedro, Owotutu,
Gbagada, Ibu-Owo and parts of Aiyetoro
indicate with values that correlates with
WHO standards for outdoor residential areas.
However, values beyond the 70 dB(A) marks
indicate exceedence. This value is recorded
within the Lmax areas. Although, the Lmin value
indicates probability, it is a function of
environmental variables particularly
aforementioned human activities. Lmax values
indicate that noise levels in the entire Bariga
suburb may not be higher than 114 dB(A). It
is therefore to ensure that activities
generating noise incidence in Bariga should
be put to check.
Periodical Noise index of Bariga
The observed noise pattern suggests that
Bariga is highly sensitive to economic
activities such as commercial and
transportation. The observed pattern has
led to designation of some communities as
being environmentally-unsafe due their
proximity to noise-generating activities.
Periodic noise index of Bariga as depicted
in Fig. 4 considers specific periods of the
day and the human reaction in form of
response i.e. displeasure. The index values
represent the extent of human displeasure
to increasing noise values for each period
of the day and the general noise index for
the study area. The high index value for
the morning period is a function of the
observed human activities that generate
noise. 87 dB(A) and above depict high
annoyance in areas such as Bariga, Akoka,
Abule Okuta, Apelehin and parts of
Obanikoro. Lower values are roughly
distributed during this period of the day.
(a)
(b)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 165
Fig. 4: Periodic noise indices of Bariga
In the noontime, the noise index reduces
with respect to human activities. However,
high index is possible in areas
aforementioned although with reduced
intensity compared to the morning period.
The highest noise indices were recorded in
the evening period. Obanikoro, Apelehin,
Abule Okuta, Akoka, parts of Akoka,
Aiyetoro, and Bariga all recorded high
values indicating the need to check the
human activities stimulating noise in these
areas. The cumulative noise index for the
area showed that incidence of noise
pollution is high with likely consequences
that will adversely affect the residents of
the study area. The least exposure index of
70.39–79.24 dB(A) is well distributed
within communities with low traffic
activity and high residential occupation.
This area could be zoned as comfortable
with low noise risk values. Other higher
values up to 104.07 dB(A) point to high
(b)
(a)
(c)
(d)

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 166
values of annoyance vulnerabilities. These
indices are noticeable in residential and
educational areas such as Akoka, Abule
Okuta, Ilaje, Apelehin and parts of
Mafowoku and Gbagada. This showed that
the entire area has high susceptibility to
noise-driven health challenges. Therefore,
the occurrence of uncontrollable noise-
driven annoyance, partial deafness and other
possible noise-related health challenges
might be rife in these areas.
CONCLUSION
The study has examined the distribution of
noise in Bariga suburb of Lagos metropolis,
via monitoring and GIS-based mapping for
decision making purposes. Review of
literature showed that there are no
localised standards and regulations on
noise as an environmental pollution that
requires scientific measurement and
appropriate data collection as well as
regulation-assessment mechanisms. As a
result of this gap, the World Health
Organisation (WHO) standards were used
as limits for noise assessment. The method
adopted for data collection examined the
possible contribution of specific land uses
– residential, educational, commercial,
and traffic to noise generation. The results
all points to high noise values and
consequent high impact on human health,
learning and environmental safety of all
residents of the area.
According to the result of the study, it is
essential to check the impact of increasing
transportation and marketing activities in the
study area with respect to noise. Areas such
as Apelehin, Obanikoro, Ilaje, Akoka, Bariga,
Gbagada, require consistent checks on the
variety to human activities such as transport
and commercial activities. Road
transportation regulations should be defined
in such a way that it will include concerns for
human health as regards to the dangers and
hazards of it. Marketing activities should be
re-organised in an environmentally-friendly
manner. The use of loud speakers should be
discouraged in already noisy environment
such as markets. Motorists should devoid
from unnecessary usage of horns as this
contributes enormously to traffic-stimulated
noise in places such as Ilaje, Bariga and parts
of Gbagada. Road signs indicating silence for
educational areas should be considered for
the safety of pupils and teachers. In addition,
Ministry of Environment at the Federal and
State level should collaborate to conduct a
metropolitan noise assessment study. Such
study will produce a comprehensive noise
pollution and regulations standard for
sustainable environmental development in
Lagos with respect to the identified land uses.

Sokoto Journal of the Social Sciences Vol. 4: No.1, June, 2014 167
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