MODELING THE EFFECT OF ATMOSPHERIC STABILITY, NITROGEN OXIDE AND CARBON MONOXIDE ON THE FORMATION ON OZONE: A CASE OF OGBA/EGBEMA/NDONI LOCAL GOVERNMENT AREA IN NIGERIA

http://www.iaeme.com/IJCIET/index.asp 111 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 3, May–June 2016, pp. 111–121, Article ID: IJCIET_07_03_011
Available online at
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=3
Journal Impact Factor (2016): 9.7820 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
MODELING THE EFFECT OF
ATMOSPHERIC STABILITY, NITROGEN
OXIDE AND CARBON MONOXIDE ON THE
FORMATION ON OZONE: A CASE OF
OGBA/EGBEMA/NDONI LOCAL
GOVERNMENT AREA IN NIGERIA
Ify L. Nwaogazie
Department of Civil and Environmental Engineering,
University of Port Harcourt, Rivers State, Nigeria
Abali Happy Wilson
Centre for Occupational Health, Safety & Environment,
Institute of Petroleum Studies, University of Port Harcourt, Nigeria
Terry Henshaw
Africa Centre of Excellence, University of Port Harcourt,
Rivers State, Nigeria
ABSTRACT
Modeling the effect of atmospheric stability, Nitrogen oxide and carbon
monoxide on ozone formation is presented. The observation of NO2, CO,
Ozone and meteorological parameters were carried out in 5 predefined
locations in Ogba/Egbema/Ndoni Local Government area in Nigeria. A model
which was dependent CO, NO and solar radiation was developed and it
attained a correlation coefficient of 0.6. Sensitivity analysis was carried out of
the independent variables of the developed model and NO2 showed no
significance to the formation of Ozone and a 0.5% coefficient of correlation in
the direct relationship to Ozone formation. Solar radiation showed
significance of 3.18 in the formation of Ozone and was ranked the second most
important parameter in the formation of Ozone. It also showed a 5%
coefficient of correlation in the direct relationship of Ozone formation. CO
showed the highest significance of 7.534 in the formation of Ozone and a
correlation coefficient of 50% in the direct relationship to Ozone formation.
The CO in this Work represents VOC’s because in the actual sense VOC’s
shows high correlation with CO and is chemically required in the formation of
Ozone. Results also shows that Ozone concentration as high as 0.38mg/m3
can
be formed on days of stable atmospheric conditions (low solar radiation).

Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw
Days of unstable atmospheric condition (high solar radiation) dependent on
the amount of recorded CO, Ozone concentration was as high as 0.68 mg/m3
.
Key words: Modeling, Ozone Formation, Carbon monoxide, Atmospheric
Stability, Rivers State Nigeria
Cite this Article: Nwoke H.U, Dike B.U, Okoro B.C, Nwite S.A, Modeling
The Effect of Atmospheric Stability, Nitrogen Oxide and Carbon Monoxide
On The Formation On Ozone: A Case of Ogba/Egbema/Ndoni Local
Government Area In Nigeria, International Journal of Civil Engineering and
Technology, 7(3), 2016, pp. 111–121.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=3
1. INTRODUCTION
The main driving force of emitted pollutants is the atmosphere and the meteorological
parameters that aid these movements are humidity, wind speed, solar radiation,
rainfall and temperature. Works of Henshaw et al. (2016), has considered solar
radiation the most significant meteorological parameter when considering uplifting of
pollutants and wind speed when considering horizontal dispersion of pollutants.
Pasquil in 1961 had proposed a classification tool that uses wind speed and solar
radiation to determine the atmospheric stability of the atmosphere. Till date this tool
has been used successively to determine the suitability of the atmosphere to
effectively disperse pollutants (Henshaw et al., 2015; Henshaw et al., 2016; Pasquil,
1961; Sucevic and Djurisic, 2012).
Ozone pollutant is a secondary pollutant formed from volatile organic compounds
and Nitrogen oxide in the presence of sunlight. The most significant of these stated
ingredients has not been discussed extensively in literature and it gets more confusing
to find the presence of unhealthy levels of ozone in areas with healthy amounts of
Nitrogen oxide. This has been explained as the ability of ozone to travel tens of
kilometers from point of its formation (World Bank Group, 1998)). Works of Erika et
al. (2010) has shown very high correlations between carbon monoxide and volatile
organic compounds and this simply means high amounts of CO represents high
presence of VOC’s.
Earlier works in the study area have recorded very high concentrations of
Nitrogen oxide and Carbon monoxide (Abali, 2015; Nwaogazie et al., 2016).
2. MATERIAL AND METHODS
2.1. Study area
Ogba/Egbema/Ndoni Local Government Area (LGA) is one of the 23 LGA of Rivers
State of Nigeria. It lies on Latitude 5.34167N and Longitude 6.65556 E. The area is
one of the highest flaring region, having a very high concentration of flaring activities
in the Niger Delta region of Nigeria (Anejionu et al.,2013). The Ogba–Egbema–
Ndoni Local Government Area is inhabited by the three tribes of Ogba, Egbema and
Ndoni people all sub-groups of the Igbo people. The Ndonis are a pure stock
of Ndokwa people of Delta State. They are great farmers and fishermen with a rich
cultural history. Figure 1 shows the map of Niger delta with the study area
represented as a white triangle. Figure 2 shows the observation sites and the flaring
locations in the study area (see to Nwaogazie et al., 2016 for more details on the study
area).

Modeling The Effect of Atmospheric Stability, Nitrogen Oxide and Carbon Monoxide On
The Formation On Ozone: A Case of Ogba/Egbema/Ndoni Local Government Area In
Nigeria
Figure 1 Map of Niger Delta of Nigeria showing Ogba/Egbema/Ndoni Local Government
Area
Figure 2 Positions of observation points and flaring points in Ogba/Ndoni/Egbema LGA
Source: Nwaogazie et al.(2016)
2.2. Equipment used
The equipment Used for this work are as listed;
1. Davis Due weather station to measure weather parameters (mounted 10m high);
2. Garmin model 64s GPRS to identify location of study;
3. TES solar radiation monitor - hourly measurement of solar radiation levels;
4. An Aeroset 531s Particulate matter monitor - hourly measurement of particulates;
5. An Aeroqual 731 gas monitor – hourly pollutant levels monitoring; and
6. Gas sensors (NO2, SO2, Ozone and CO).
2.3. Procedure
Five observation sites were established, one in Obite, two in Idu, one in Mgbede and
one in Ebocha village. The observation point in Obite was behind the Obite gas plant,
that of Idu location one and two were close to Obagi flow stations and the Mgbede
and Ebocha locations were close to Ebocha oil center and Obrikom gas plant. The
weather station was mounted at Obite location and the gas/ particulate monitors were
mounted on different sites at observation periods. Readings were taken from 6am to
7pm for all locations. Obite observations were carried out on October 10, 2015.
Study
Area

Mgbede and Ebocha locations were observed on October 11, and Idu locations 1 and
2 observations were carried out on October 12.
3. RESULTS AND DISCUSSION
3.1. Results
Observation results of Nitrogen Oxide, Carbon monoxide, Ozone and Solar radiation
are collated as Table 1 and a linear relationship between NO2, CO, O3 and solar
radiation was carried out with the aid of the Microsoft Excel tool of 2011 model (See
Table 2).
Table 1 Field observations of NO2, CO, O3 and Solar radiation±
OBSERVATION POINT
NO2 CO Solar radiation (
W/m2
) O3
OBITE 34 0 0 0.1
33.74 0 1049 0.16
33.22 0 1033 0.16
33.44 11.3 740 0.22
32.46 28.6 560 0.52
33.44 32.7 420 0.64
31.58 13.9 220 0.54
32.55 7.8 10.7 0.4
20 2.6 0 0.32
EBOCHA 20.25 0 0 0.25
20.39 0 100 0.26
19.1 4 0 0.36
20.1 0.3 89 0.27
20.2 0.2 73 0.21
30.9 0 40 0.3
19.7 0.7 100.1 0.32
19.6 0 50.1 0.37
19.58 0.1 6.2 0.26
19.71 3.1 0 0.25
MGBEDE 20.38 0 0 0.23
20.39 0 126 0.21
19.84 4.6 0 0.33
20.1 0 35 0.23
20.14 0.5 74 0.3
33.25 0 45 0.36
20.16 0.6 98.5 0.29
20.12 0 57 0.28
20.13 0 24.2 0.25
20.17 3.7 0 0.26
IDU-1 20.03 1.9 0 0.25
21.13 0 470 0.02
20.27 0 1080 0.07
20.06 6.4 215 0.25
19.77 17.8 670 0.27
19.17 31 260 0.43
19.36 29.4 91 0.46
19.43 28.53 10.9 0.49
19.58 15.7 0 0.37
IDU-2 19.65 0 0 0.34
20.14 0 390 0.21
20.04 0 1020 0.23
20.02 3.2 200 0.39

Nigeria
OBSERVATION POINT
NO2 CO Solar radiation (
W/m2
) O3
19.7 10.3 590 0.41
19 12.6 210 0.61
19.93 9.8 82 0.31
19.3 8.7 9.2 0.36
19.6 4.2 0 0.28
±Source – Nwaogazie et al. (2016)
Table 2 Microsoft Excel Report of Regression comparison of CO, NO2, O3 and solar
radiation
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.771494
R Square 0.595203
Adjusted R
Square 0.566961
Standard Error 0.082585
Observations 47
ANOVA
df SS MS F
Significance
F
Regression 3 0.431215 0.143738 21.07532 1.5E-08
Residual 43 0.29327 0.00682
Total 46 0.724485
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 0.227467 0.053512 4.250753 0.000112 0.119549 0.335384
NO2 0.002111 0.002422 0.871547 0.388296 -0.00277 0.006996
CO 0.009476 0.001286 7.366582 3.81E-09 0.006882 0.012071
( W/m2) -0.00013 4.07E-05 -3.18255 0.002712 -0.00021 -4.7E-05
The multiple linear regression model is of the format as presented in Equation (1)
Y = a + bX1 + c X2+ d X3 + . . . + n
Xi………………………………………………………….... Equation (1)
Where y = dependent variable; a, b, c, d, and n = site specific coefficients; and X1,
X2, X3 and Xi = independent variables.
Extracting the site specific coefficient from Table 2, Equation (2) is presented as
the model for predicting Ozone from CO and NO concentrations with the prevailing
solar radiation.
O3 = 0.227467 + 0.002111 (NO2) + 0.009476 (CO) – 0.00013 (Solar radiation)
………………… Equation (2)
A sensitivity analysis on the degree of relationship of each independent variable in
Equation (2) on Ozone is as presented in Tables 3, 4 and 5 (reports from the Microsoft
excel regression tool). Extracting the significance of each parameter from Tables 2, 3,
4 and 5, Table 6 compares them with the standardized t-statistic at 95% significance
and ranks these parameters, accordingly.

Tables 7 and 8 present the atmospheric conditions at Obite and Ebocha during the
observation periods. Figures 3 - 5 show plots of CO and Ozone for the five
observation locations.
Table 3 Microsoft Excel Report of Regression comparison of CO and O3
SUMMARY OUTPUT
Multiple R 0.706688
R Square 0.499409
Adjusted R Square 0.488284
Observations 47
ANOVA
df SS MS F
Regression 1 0.361814 0.361814 44.89366
Residual 45 0.362671 0.008059
Total 46 0.724485
Coefficients Standard Error t Stat P-value
Intercept 0.248328 0.015702 15.8152 5.36E-20
CO 0.009274 0.001384 6.700273 2.84E-08
Table 4 Microsoft Excel Report of Regression comparison of NO2, and O3 .
SUMMARY OUTPUT
Multiple R 0.076061
R Square 0.005785
Adjusted R
Square -0.01631
Standard
Error 0.126517
Observations 47
ANOVA
df SS MS F
Significance
F
Regression 1 0.004191 0.004191 0.261856 0.61135
Residual 45 0.720294 0.016007
Total 46 0.724485
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 0.266276 0.08052 3.306981 0.001859 0.104102 0.428451
NO2 0.00177 0.003459 0.511718 0.61135 -0.0052 0.008738

Nigeria
Table 5 Microsoft Excel Report of Regression Comparison of O3 and Solar Radiation
SUMMARY OUTPUT
Multiple R 0.236773
R Square 0.056062
Adjusted R Square 0.035085
Observations 47
ANOVA
df SS MS F
Significance
F
Regression 1 0.040616 0.040616 2.672602 0.109066
Residual 45 0.683869 0.015197
Total 46 0.724485
Coefficients Standard Error t Stat P-value Lower 95%
Upper
95%
Intercept 0.326688 0.021854 14.94842 4.56E-19 0.282671 0.370705
W/M -9.3E-05 5.7E-05 -1.63481 0.109066 -0.00021 2.16E-05
Table 6 Significance of Parameters to Ozone Formation
S/N Parameter
Estimated t-
statistic
±t-statistic @
95% significance
Remark Rank
Correlation to
Ozone
1 CO 7.37 ±2.35 significant 1 50%
2 NO2 0.87 ±2.35 Not-significant - 0.5%
3 Solar
radiation
-3.18 ±2.35 significant 2 5.6%
±
Source: Nwaogazie (2011)
Table 7 Observed Data from Obite with Estimated atmospheric stability
Time NO2 O3 CO
0
C @
Ground
Level
0
C
@ 10 m
High
Solar
Radiation
(W/m2
)
Wind
Speed
Atmospheric
Stability
6:00 am 34 0.1 0 25 24.3 0 0 D
9:00 am 34 0.2 0 28 25.4 1049 0 A
12:00 pm 33 0.2 0 32 29.8 1033 1.6 A
2:00 pm 33 0.2 11 34 31.3 740 3.2 A
3:00 pm 32 0.5 29 37 31.8 560 1.6 A
4:00 pm 33 0.6 33 36 31.2 420 4.8 B
5:00 pm 32 0.5 14 29 29.1 220 6.4 C
6:00 pm 33 0.4 7.8 26 27.2 10.7 6.4 D
7:00 pm 35 0.3 2.6 25 26 0 1.6 F

Table 8 Observed Data from Ebocha with Estimated atmospheric stability
Time NO2 O3 CO
0
C @
Ground
Level
0
C
@ 10
m
High
Solar
Radiation
(W/m2
)
Wind
Speed
Atmospheric
Stability
6:00 am 20.25 0 0.25 25 23.6 0 0 E
9:00 am 20.39 0 0.26 27 26.6 100 0 D
9:30 am 19.1 4 0.36 25 26.3 0 1.6 D
12:00 pm 20.1 0.3 0.27 24 22.1 89 3.2 D
14:00 pm 20.2 0.2 0.21 24 23.1 73 1.6 D
15:00 pm 30.9 0 0.3 24 23.7 40 4.8 D
16:00 pm 19.7 0.7 0.32 24.5 24.1 100.1 6.4 D
17:00 pm 19.6 0 0.37 26 24.1 50.1 6.4 D
18:00 pm 19.58 0.1 0.26 24 23.7 6.2 6.4 D
19:00 pm 19.71 3.1 0.25 26 23.2 0 1.8 E
Figure 3 Comparison of NO2, CO and O 3 from Obite observation point
Figure 4 Comparison of NO2, CO and O 3 from Ebocha observation point
06:00:
00
09:00:
00
12:00:
00
14:00:
00
15:00:
00
16:00:
00
17:00:
00
18:00:
00
19:00:
00
O3 0.1 0.16 0.16 0.22 0.52 0.64 0.54 0.4 0.32
CO 0 0 0 11.3 28.6 32.7 13.9 7.8 2.6
NO2 34 33.74 33.22 33.44 32.46 33.44 31.58 32.55 34.8
0
5
10
15
20
25
30
35
40
concentrationofO3/CO/NO2
Time of observation
O3
CO
NO2
06:00 09:00 09:30 12:00 14:00 15:00 16:00 17:00 18:00 19:00
NO2 20.25 20.39 19.1 20.1 20.2 30.9 19.7 19.6 19.58 19.71
CO 0 0 4 0.3 0.2 0 0.7 0 0.1 3.1
O3 0.25 0.26 0.36 0.27 0.21 0.3 0.32 0.37 0.26 0.25
0
5
10
15
20
25
30
35
concentrationofO3/CO/NO2
Time of Observation
NO2
CO
O3

Nigeria
Figure 5 Comparison of NO2, CO and O 3 from Mgbede observation point
Figure 6 Comparison of NO2, CO and O 3 from Idu (Location 1) observation point
Figure 7 Comparison of NO2, CO and O 3 from Idu (Location 2) observation point.
3.2. Discussion
The model for predicting Ozone from CO, NO and solar radiation has been presented
and it attains a correlation coefficient of 0.6. This factor can be improved on when
more data are collated from the study area. The formation of ozone pollutant can be
very confusing, as literature has it that it is capable of moving more than 3 km away
06:00 09:00 09:30 12:00 14:00 15:00 16:00 17:00 18:00 19:00
NO2 20.38 20.39 19.84 20.1 20.14 33.25 20.16 20.12 20.13 20.17
CO 0 0 4.6 0 0.5 0 0.6 0 0 3.7
O3 0.23 0.21 0.33 0.23 0.3 0.36 0.29 0.28 0.25 0.26
0
5
10
15
20
25
30
35
concentrationofCO/NO2/O3
Time of observation
NO2
CO
O3
06:00 09:00 12:00 14:00 15:00 16:00 17:00 18:00 19:00
NO2 20.03 21.13 20.27 20.06 19.77 19.17 19.36 19.43 19.58
CO 1.9 0 0 6.4 17.8 31 29.4 28.53 15.7
O3 0.25 0.02 0.07 0.25 0.27 0.43 0.46 0.49 0.37
0
5
10
15
20
25
30
35
Time of observation
NO2
CO
O3
06:00 09:00 12:00 14:00 15:00 16:00 17:00 18:00 19:00
NO2 19.65 20.14 20.04 20.02 19.7 19 19.93 19.3 19.6
CO 0 0 0 3.2 10.3 12.6 9.8 8.7 4.2
O3 0.34 0.21 0.23 0.39 0.41 0.61 0.31 0.36 0.28
0
5
10
15
20
25
Time of observation
NO2
CO
O3

from its point of formation (Erika, 2016; Henshaw, 2016). In spite of that,it is very
important to know the most sensitive pollutant(s) or meteorological parameter(s) to
the formation of Ozone. This work has shown CO as the most significant pollutant
sensitive to the formation of Ozone (see Table 6). This is very odd looking at it from
the angle that CO is not one of the primary pollutant responsible for the formation of
Ozone. This field observation did not put into consideration observation of VOC’s but
works of Erika et al. (2010) has shown strong positive correlations between CO and
VOC’s. It is then acceptable to say that very high concentrations of CO represent very
high concentrations of VOC’s. From Table 6 solar radiation is the next sensitive
parameter to the formation of Ozone and NO2 shows no significance at all. This is to
say little amount of NO2 is required for the formation of Ozone and higher volumes
outside this required amount is needless to the reaction. This is further confirmed
from Table 6 which shows that NO2 has only 0.5% correlation with Ozone formation.
This is very low when compared to CO which has up to 50% correlation with Ozone
formation
It is seen from Tables 7 & 8 that even in very stable atmospheric conditions (from
class D downwards) which indicates low solar radiation (< 150 W/m2
), Ozone can be
formed up to 0.35mg/m concentration. This is to say that the parameter that regulates
the concentration of Ozone is basically VOC’s (CO in our case). An interesting trend
is noted in all the Figures (Figs. 3-5), that is, high values of Ozone (O3) corresponding
to high values of Carbon monoxide and this goes to reinforce the high significance
noted in the t-statistic (see Table 6). This is also a confirmation to the fact that VOC’s
concentration is the main parameter that regulates the amount of Ozone recorded in
any environment.
4. CONCLUSION
The following conclusion can be drawn from this work:
 CO showed 50% correlation with Ozone formation, NO showed 0.5% correlation and
solar radiation showed 5%;
 Observations show high Ozone concentration to occur at points of high CO
concentration;
 Ozone can be formed up to concentration of 0.38mg/m with very stable atmospheric
conditions (low solar radiation); and
 Only VOC’s and solar radiation are significant to the formation of Ozone. This is to
say very small healthy levels of NO concentration can form very unhealthy high
concentration of Ozone.
REFERENCES
[1] Abali, H. (2015): Assessment of some pollutants from gas flaring in
Ogba/Egbema/Ndoni Local Government Area of Rivers State, M.Sc Thesis
work of the University of Port Harcourt, Nigeria.
[2] Henshaw, T., Nwaogazie, I.L. and Weli, V. (2015): Modeling surface solar
radiation using a cloud depth factor. International journal of recent scientific
research. Vol. 6(10):6562-6569. Available at: http://www.ijser.org
[3] Henshaw, T., Nwaogazie, I.L. and Weli, V (2016): Model prediction of pollution
standard index (PSI) for carbon monoxide: A tool for environmental impact
assessment. British Journal of Applied Science & Technology. Vol. 15(3). Pp 1-
13. Available at: www.sciencedomain.org

Nigeria
[4] Sucevic, N. and Djurisic, Z. (2012): Influence of atmospheric stability variation
on uncertainties of wind farm production estimation. European wind Energy
conference and exhibition.
[5] World Bank Group (1998)
[6] Henshaw, T., Nwaogazie, Ify L. and Weli V. (2016): A predictive model for
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engineering and technology (IJCIET). Vol. 7(1) pp 337-357. Available online at:
http://www.iame.com/IJCIET/issue.asp?JType-IJCIET&VType=7&IType=1
[7] Anejionu, C.D, Blackburn, A and Duncan, W. (2013): Remote Mapping of Gas
Flares in the Niger Delta of Nigeria with MODIS imagery. Lancaster University,
Lancaster Environment Centre, United Kingdom.
[8] Nwaogazie, Ify L. (2011): “Probability and statistics for science and engineering
practice”. University of Port Harcourt press, Port Harcourt, Nigeria.
[9] Nwagazie, Ify L., Abali, H., and Henshaw, T. (2016): Assessment of Standard
Pollutants In A Gas Flaring Region: A Case Of Ogba/Egbema/Ndoni Local
Government Area In Rivers State of Nigeria. International Journal of Civil
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http://www.iame.com/IJCIET/issue.asp?JType-IJCIET&VType=7&IType=3
[10] Pasquill, F. (1961): The estimation of the dispersion of windbore material.
Meteorological magazine. Vol. 90, pp.33-49.
[11] Erika, V.S., Paul, S.M., and Christian, P. (2010): Global comparison of VOC
and CO observations in urban areas. Atmospheric Environment. 44, pp. 5053–
5064.

MODELING THE EFFECT OF ATMOSPHERIC STABILITY, NITROGEN OXIDE AND CARBON MONOXIDE ON THE FORMATION ON OZONE: A CASE OF OGBA/EGBEMA/NDONI LOCAL GOVERNMENT AREA IN NIGERIA

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MODELING THE EFFECT OF ATMOSPHERIC STABILITY, NITROGEN OXIDE AND CARBON MONOXIDE ON THE FORMATION ON OZONE: A CASE OF OGBA/EGBEMA/NDONI LOCAL GOVERNMENT AREA IN NIGERIA