Ify L. Nwaogazie Department of Civil and Environmental Engineering, University of Port Harcourt, Rivers State, Nigeria

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 NO 2, 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 NO 2 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/m 3 can be formed on days of stable atmospheric conditions (low solar radiation). http://www.iaeme.com/ijciet/index.asp 111 editor@iaeme.com

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/m 3. 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). http://www.iaeme.com/ijciet/index.asp 112 editor@iaeme.com

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 Study Area 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 (NO 2, SO 2, 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. http://www.iaeme.com/ijciet/index.asp 113 editor@iaeme.com

Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw 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 NO 2, CO, O 3 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 NO 2, CO, O 3 and Solar radiation ± OBSERVATION POINT NO 2 CO Solar radiation ( W/m 2 ) O 3 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 1 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 1.1 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 2 0.39 http://www.iaeme.com/ijciet/index.asp 114 editor@iaeme.com

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 NO 2 CO Solar radiation ( OBSERVATION POINT W/m 2 ) O 3 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, NO 2, O 3 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 0.682 Total 46 0.724485 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.227467 0.053512 4.250753 0.0112 0.119549 0.335384 NO2 0.2111 0.2422 0.871547 0.388296-0.277 0.6996 CO 0.9476 0.1286 7.366582 3.81E-09 0.6882 0.012071 ( W/m2) -0.013 4.07E-05-3.18255 0.2712-0.021-4.7E-05 The multiple linear regression model is of the format as presented in Equation (1) Y = a + bx 1 + c X 2 + d X 3 +... + n X i... Equation (1) Where y = dependent variable; a, b, c, d, and n = site specific coefficients; and X 1, X 2, X 3 and X i = 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. O 3 = 0.227467 + 0.2111 (NO 2 ) + 0.9476 (CO) 0.013 (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. http://www.iaeme.com/ijciet/index.asp 115 editor@iaeme.com

Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw 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 O 3 SUMMARY OUTPUT Regression Statistics Multiple R 0.706688 R Square 0.499409 Adjusted R Square 0.488284 Standard Error 0.089774 Observations 47 ANOVA df SS MS F Regression 1 0.361814 0.361814 44.89366 Residual 45 0.362671 0.8059 Total 46 0.724485 Coefficients Standard Error t Stat P-value Intercept 0.248328 0.015702 15.8152 5.36E-20 CO 0.9274 0.1384 6.7273 2.84E-08 Table 4 Microsoft Excel Report of Regression comparison of NO 2, and O 3. SUMMARY OUTPUT Regression Statistics Multiple R 0.076061 R Square 0.5785 Adjusted R Square -0.01631 Standard Error 0.126517 Observations 47 ANOVA df SS MS F Significance F Regression 1 0.4191 0.4191 0.261856 0.61135 Residual 45 0.720294 0.0167 Total 46 0.724485 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.266276 0.08052 3.306981 0.1859 0.104102 0.428451 NO2 0.177 0.3459 0.511718 0.61135-0.52 0.8738 http://www.iaeme.com/ijciet/index.asp 116 editor@iaeme.com

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 Table 5 Microsoft Excel Report of Regression Comparison of O 3 and Solar Radiation SUMMARY OUTPUT Regression Statistics Multiple R 0.236773 R Square 0.056062 Adjusted R Square 0.035085 Standard Error 0.123277 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 0.109066-0.021 2.16E-05 S/N Parameter Table 6 Significance of Parameters to Ozone Formation Estimated t- statistic ±t-statistic @ 95% significance Remark Rank 1 CO 7.37 ±2.35 significant 1 50% 2 NO2 0.87 ±2.35 Not-significant - 0.5% 3 Solar radiation ± Source: Nwaogazie (2011) -3.18 ±2.35 significant 2 5.6% Correlation to Ozone Table 7 Observed Data from Obite with Estimated atmospheric stability Time NO 2 O 3 CO 0 C @ Ground Level 0 C @ 10 m High Solar Radiation (W/m 2 ) Wind Speed Atmospheric Stability 6: am 34 0.1 0 25 24.3 0 0 D 9: am 34 0.2 0 28 25.4 1049 0 A 12: pm 33 0.2 0 32 29.8 1033 1.6 A 2: pm 33 0.2 11 34 31.3 740 3.2 A 3: pm 32 0.5 29 37 31.8 560 1.6 A 4: pm 33 0.6 33 36 31.2 420 4.8 B 5: pm 32 0.5 14 29 29.1 220 6.4 C 6: pm 33 0.4 7.8 26 27.2 10.7 6.4 D 7: pm 35 0.3 2.6 25 26 0 1.6 F http://www.iaeme.com/ijciet/index.asp 117 editor@iaeme.com

concentration of O3/CO/NO2 concentration of O3/CO/NO2 Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw Table 8 Observed Data from Ebocha with Estimated atmospheric stability Time NO 2 O 3 CO 0 C @ Ground Level 0 C @ 10 m High Solar Radiation (W/m 2 ) Wind Speed Atmospheric Stability 6: am 20.25 0 0.25 25 23.6 0 0 E 9: am 20.39 0 0.26 27 26.6 1 0 D 9:30 am 19.1 4 0.36 25 26.3 0 1.6 D 12: pm 20.1 0.3 0.27 24 22.1 89 3.2 D 14: pm 20.2 0.2 0.21 24 23.1 73 1.6 D 15: pm 30.9 0 0.3 24 23.7 40 4.8 D 16: pm 19.7 0.7 0.32 24.5 24.1 1.1 6.4 D 17: pm 19.6 0 0.37 26 24.1 50.1 6.4 D 18: pm 19.58 0.1 0.26 24 23.7 6.2 6.4 D 19: pm 19.71 3.1 0.25 26 23.2 0 1.8 E 40 35 30 25 20 15 10 5 0 06:: 09:: 12:: 14:: 15:: 16:: 17:: 18:: 19:: 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 Time of observation O3 CO NO2 Figure 3 Comparison of NO 2, CO and O 3 from Obite observation point 35 30 25 20 15 10 5 0 06: 09: 09:30 12: 14: 15: 16: 17: 18: 19: 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 Time of Observation NO2 CO O3 Figure 4 Comparison of NO 2, CO and O 3 from Ebocha observation point http://www.iaeme.com/ijciet/index.asp 118 editor@iaeme.com

concentration of CO/NO2/O3 concentration of CO/NO2/O3 concentration of CO/NO2/O3 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 35 30 25 20 15 10 5 0 06: 09: 09:30 12: 14: 15: 16: 17: 18: 19: 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 Time of observation NO2 CO O3 Figure 5 Comparison of NO 2, CO and O 3 from Mgbede observation point 35 30 25 20 15 10 5 0 06: 09: 12: 14: 15: 16: 17: 18: 19: 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 Time of observation NO2 CO O3 Figure 6 Comparison of NO 2, CO and O 3 from Idu (Location 1) observation point 25 20 15 10 5 0 06: 09: 12: 14: 15: 16: 17: 18: 19: 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 Time of observation NO2 CO O3 Figure 7 Comparison of NO 2, 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 http://www.iaeme.com/ijciet/index.asp 119 editor@iaeme.com

Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw 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 NO 2 shows no significance at all. This is to say little amount of NO 2 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 NO 2 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/m 2 ), 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 (O 3 ) 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 http://www.iaeme.com/ijciet/index.asp 120 editor@iaeme.com

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 [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 Ozone upliftment in obstruction prone environment. International journal of civil 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 Engineering and Technology (IJCIET). Vol. 7(3). Pp 07-17. Available online at: 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. http://www.iaeme.com/ijciet/index.asp 121 editor@iaeme.com