A PREDICTIVE MODEL FOR OZONE UPLIFTING IN OBSTRUCTION PRONE ENVIRONMENT

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International Journal of Civil Engineering and Technology (IJCIET)

Volume 7, Issue 1, Jan-Feb 2016, pp. 337-357, Article ID: IJCIET_07_01_028

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ISSN Print: 0976-6308 and ISSN Online: 0976-6316

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A PREDICTIVE MODEL FOR OZONE

UPLIFTING IN OBSTRUCTION PRONE

ENVIRONMENT

Terry Henshaw

Africa Center of Excellence, University of Port Harcourt, Rivers state, Nigeria

Ify L. Nwaogazie

Department of Civil and Environmental Engineering,

University of Port Harcourt, Rivers State, Nigeria

Vincent Weli

Department of Geography and Environmental Science,

University of Port Harcourt, Rivers State, Nigeria

ABSTRACT

A model for predicting uplifting of ozone gas in obstruction prone areas is

developed. The model is dependent on ground level temperature, four metre

height temperature, wind speed and solar radiationand the obstruction used in

this research is an existing four metre fence wall. With data points established

both inside and outside the fence wall, and four metre height above the ground

level of the inside and outside positions, data were collected for five days at

two hour intervals. The Buckingham’s π-method of dimensional analysis was

adopted to develop this model and collated field measurements were used to

calibrate the model through regression. Results show that the model

developed for Ozone uplifting attained a correlation coefficient of

0.996.Verification of the model showed a correlation coefficient of 0.905 and

a mean square error (MSE) of 0.0007 between the predicted and observed

ozone concentration. Detailed statistical sensitivity analysis carried out

showed temperature as the most important meteorological parameter and

solar radiation as the least important in case of pollutant uplifting.

Verification of the modified model without the solar radiation term showed a

correlation coefficient of 0.905 and a MSE of 0.0007 between the predicted

and observed ozone concentrations and this confirmed solar radiation as the

least important meteorological parameter in obstruction environment.NO2,

SO2 and TSP showed poor correlation coefficients of 0.03, 0.45 and 6.2,

respectively when uplifting models were calibrated and verified for them, they

also showed statistically that ground level temperature is the most significant

Terry Henshaw, Ify L. Nwaogazie and Vincent Weli


meteorological parameter for pollutant uplifting in obstruction prone

environment.

Key words: Uplifting, Obstruction, Ozone, Air, Pollutant, Predictive model

Cite this Article: Terry Henshaw, Ify L. Nwaogazie and Vincent Weli, A

Predictive Model For Ozone Uplifting In Obstruction Prone Environment,

International Journal of Civil Engineering and Technology, 7(1), 2016, pp.

337-357.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=1

1. INTRODUCTION

Air pollution models have generally failed to accurately predict concentration of

pollutants (Lolymer, 2011). Most of these failures have been attributed to

environmental conditions for which these models were not developed to handle.

Literature has recorded different models which have been developed for different

environmental configuration. Some of these models have shown improvement when

compared to Gaussian models (Henricheen, 1986; Stull, 1988).

Most of the existing air pollution models can be classified as follows :(i)Those

that have incorporated wind speed and vertical eddy diffusivity as a power function of

vertical height (Seinfeld, 1986; Lin and Hildemann, 1996); (ii) Those that have

incorporated wind speed as a function of height and eddy diffusivity as a function of

downwind distance (Sharan and modani, 2006); (iii) Models that have incorporated

wind speed as a function of vertical height and vertical eddy diffusivity as a function

of both vertical height and downwind distance from the source (Sharan and Kumar,

2009); and (iv) Models that have incorporated low wind and unbounded region

(Anikender and Goyal, 2013).Obstructions from artificial and natural facilities have

also been attributed to failures of dispersion models in predicting pollutant

concentration. These obstructions impede the flow from the direction of the source.

Works have been carried out on different aspects of obstruction to pollutant

dispersion.

Works of Zhao-Lin and others (2011) considered street canyons as the obstruction

and concentrated on the effect on uneven heights of buildings given that earlier works

had considered even heights. Works of Yucong and others (2014) also considered

street canyons but tried to use different street canyon configurations. Flow pattern

inside the street canyon has been studied to be controlled by flow wind velocity,

building shapes, atmospheric instability and height of building (Xie and others, 2005;

Niachou and others, 2008; Hang and others, 2010; Baik and others, 2000; Ahmad and

others, 2005). As a result of these obstructions poor air quality has been recorded at

pedestrian levels. Air recirculation has been seen to be the major reason why

pollutants do not move in the wind ward direction (Depaul and Sheih, 1986;

Oke,1988).Many methods have been used to investigate obstruction of pollutants

dispersion where mostly street canyons have been used as the obstruction. These

widely used methods are the in-situ measurements (Depaul and Sheih, 1986; Kumar

and others, 2009; Li and others, 2007) and the computational fluid dynamic (CFD)

simulations (Baik and others, 2000; Yang and shao, 2008; Murena and others, 2009;

Gu and others, 2010; Balczo and others, 2009).Works of Harisankar and Paruthuraj

(2010) used a hill slope as the obstruction to flow and results also showed pollutant

recirculation but at the summit of the hill and this grows intense with steeper slopes.

A Predictive Model For Ozone Uplifting In Obstruction Prone Environment


Left out of literature is a model to predict the concentration of pollutant at a height

above ground level in obstruction prone areas and a sensitivity analysis to see which

meteorological parameter plays the major role in vertical uplifting of pollutants in

obstruction environments as it is obvious that wind speed is the major meteorological

parameter responsible fordispersing air pollutants in non-obstruction environments

(Andrej and others, 2015; Seinfeld, 1986).

Xie and others (2005) observed the effect of solar radiation on pollutant dispersion

in street canyons. They observed that the heating of the earth’s surface causes some

sort of buoyancy force which helps to disperse pollutants by reducing pollutant

concentration in street canyons. Works of Henshaw and others (2015) have observed

surface solar radiation as high as 1220 W/m2which is capable of heating the earth’s

surface to as high as 39 0C in the southern part of Nigeria and this can be used to an

advantage in case of obstruction to wind direction.

The pollutant used to demonstrate this effect of uplifting is the ozone gas. Ground

level Ozone is a secondary pollutant formed from nitrogen oxides and VOC’s in the

presence of sunlight, it is colorless and the impact to health on exposure is that it

affects the human respiratory system especially the lungs. The World Bank group in

1998 has generalized short term concentrations to be within 300 – 800 µg/m3 in urban

regions.

This work addresses the problem by using a 4 metre wall within a high activity

area as the obstruction in the windward direction and considering meteorological

parameters like wind speed, solar radiation and temperatures using the dimensional

analysis approach.

The use of the Buckingham’s π-method in Dimensional Analysis has been

recorded in literature as a means of deriving empirical models wherein calibrations

are done with experimental data or field measurements via the linear or multiple

regression analysis (Afangideh, 2008).

This work is aimed at developing a model which is capable of predicting ozone

concentration above ground level in obstruction prone areas where the obstruction is

in the direction of the wind. The obstruction used in this work is a four metre fence

wall within a high activity area. The proposed model is dependent of ground level

temperature, four metre height temperature, solar radiation and wind speed.

2. MATERIALS AND METHOD

2.1. Study Area

The study area for this work is the section of Choba Park, one of the three campuses

of the University of Port Harcourt, Nigeria. The exact location of Choba Park where

measurements were made is within the small exit gate of the park. This section of

Choba Park comprises of high commercial activities with respect to small kiosk for

sale of snacks and business centers that do printing/photocopying jobs and sales of

stationeries, etc. With the insufficiency in power supply these business own

generators which are always left on when the central power supply is unavailable.

Mostly students and lecturers do business in these areas and most of these business

centers have been observed to run till 8.00 pm. Outside the Choba park premises is

the Choba junction which is one of the busiest junctions in Port Harcourt because it

serves as an exit towards the western region of Nigeria. Figures 1 and 2 represent the

study area; Choba park (Figure 1) and enlarged position of pollutant measurement

(Figure 2)



Figure 1 Study area showing observation point and weather station point

Figure 2 Study area showing details of the observation point

2.2. Measuring Equipment

The Equipment used for this work are as listed in Table 1

Table1 List of Equipment used for Ozone uplifting modeling

S/N Equipment Number Purpose

1 Military compass 1 To determine the direction of the poles

2 Weather station 1 To measure meteorological parameters

3 Solar radiation meter 1 To measure solar radiation

4 Aeroqual gas monitor 4 To measure pollutant gases

2.3. Procedure

A total of four locations were established for observation of ozone gas. Two of these

locations are inside the Choba park premises by the fence and the other two outside

the park premises by the fence. Both inside and outside locations comprise of ground

and a four metre height point. The weather station was installed at 10 metres from the

ground level. The observation of ozone was carried out for five days with a time

interval of two hours. The solar radiation was measured hourly from sun rise at about

6.00 am to 9.00 pm for the 5-day duration.



2.4. Model Formulation and Development

The technique used for model development is the Buckingham’s π-method and the

various parameters considered are presented in Table 2 with their corresponding

symbols and dimensions.

Table 2 Proposed model parameters for Ozone uplifting modeling

S/N Variable Symbol Dimensions

LMT Ѳ

1 Height H L

2 Wind speed v LT-1

3 Solar radiation I MT-3

4 Temperature at ground level TGL Ѳ

5 Temperature at four metre height above

ground

TH Ѳ

6 Pollutant concentration at ground level CGL

ML-3

7 Pollutant concentration at four metre height

above ground

CH

ML-3

Let the pollutant concentration, CH be a function of the six parameters listed in

Table 2, viz

CH = f (H, v, I, TH, TGL, CGL) ……………………………………..Equation (1)

Where number of variables n = 7; m = number of standard units=4; and Number

of π’s according to Buckingham theory = n-m(7-4=3)

Thus, Equation (1) can be rewritten as:

F ( ) = 0………………………………………………….Equation (2)

Selecting the repeating variables as H, v, I, and CGL, the π- models become:

= (H, v, I,CGL,CH) ………………………………………….......Equation (3)

= (H, v, I,CGL,CGL)…………………………………………..…Equation (4)

= (H, v, I,TGL,TH) ……………………………..……………….Equation (5)

As a typical example, is evaluated by substituting the applicable dimensions (from Table 1) to Equation (3), to obtain Equation (6):

= …………………………….Equation (6)

By relating the constants a, b, and c to Length, L; Mass, M; Time, T and

Temperature, Ѳ the resulting three simultaneous Equations were solved with the

following results: a=0,b=3,c=-1 and d=0, respectively. Thus, Equation (6) becomes:

=

………………………………………………………….. Equation (7)

Adopting similar procedure we obtain the following results for Equations (4) and (5),

respectively

=

……………………………………………………………Equation (8)

=

……………………………………………………………...Equation (9)

Thus, Equation (2) can be rewritten as



=

……………………………………………………….Equation (10)

or

=

×

……………………………………………Equation (11)

Let

Y=

; x1 =

and x2 =

;

That is, Y=

or

Ln Y = a Ln + b Ln + K……………………………………..…Equation (12)

2.5. Model Calibration

Using multiply regression software on Microsoft excel, the model (Equation 12) was

developed for prediction of O3 at four metre height above ground level. Table 3

presents field data from the observation of Ozone concentrations and estimation of x1,

x2 and Y as in Equation (12). Table 4 presents a modified version of Table 3 by

striking out all zeros, Table 5 shows the regression analysis summary and the

calibrated model for the prediction of O3 pollutant at 4 metre above ground height as

per Equation (12)



Table 3 Input Data Table

SR- Solar Radiation; WS-Wind Speed; CGL – Pollutant Concentration At Ground Level;

C4M- Pollutant Concentration At Four Metre Height Level

S/N DAY TEMP

@ GL

TEMP

@ 4m

SR WS CGL C4M x1 x2 Y Lnx1 Lnx2 LnY

1 MON 25 29 0 0 0 0 #DIV/0! 1.16 #DIV/0! #DIV/0! 0.14842 #DIV/0!

2 28 28 280 4.8 0 0.28 0 1 0.110592 #NUM! 0 -2.20191

3 31 32 350.9 9.7 0.26 0.28 0.676247 1.032258 0.728266 -0.3912 0.03175 -0.31709

4 34 31 413.8 8 0 0.32 0 0.911765 0.39594 #NUM! -0.0924 -0.92649

5 37 34 1220 11.3 0 0 0 0.918919 0 #NUM! -0.0846 #NUM!

6 33 33 600 12.9 0 0.25 0 1 0.894454 #NUM! 0 -0.11154

7 29 28 14 4.8 0.01 0.02 0.078994 0.965517 0.157989 -2.53838 -0.0351 -1.84523

8 27 26 0 1.6 0 0.32 #DIV/0! 0.962963 #DIV/0! #DIV/0! -0.0377 #DIV/0!

9 26 26 0 1.6 0.28 0 #DIV/0! 1 #DIV/0! #DIV/0! 0 #DIV/0!

10 TUE 25 29 0 0 0.3 0.27 #DIV/0! 1.16 #DIV/0! #DIV/0! 0.14842 #DIV/0!

11 28 28 280 4.8 0.26 0.28 0.102693 1 0.110592 -2.27602 0 -2.20191

12 31 32 350.9 9.7 0.25 0.3 0.650237 1.032258 0.780285 -0.43042 0.03175 -0.2481

13 34 31 413.8 8 0.37 0.4 0.457806 0.911765 0.494925 -0.78131 -0.0924 -0.70335

14 37 34 1220 11.3 0.31 0.32 0.366638 0.918919 0.378465 -1.00338 -0.0846 -0.97163

15 33 33 600 12.9 0.34 0.35 1.216457 1 1.252235 0.195943 0 0.22493

16 29 28 14 4.8 0.29 0.35 2.290834 0.965517 2.7648 0.828916 -0.0351 1.016968

17 27 26 0 1.6 0.27 0.29 #DIV/0! 0.962963 #DIV/0! #DIV/0! -0.0377 #DIV/0!

18 26 26 0 1.6 0.29 0.3 #DIV/0! 1 #DIV/0! #DIV/0! 0 #DIV/0!

19 WED 24 24 0 1.6 0.25 0.24 #DIV/0! 1 #DIV/0! #DIV/0! 0 #DIV/0!

20 27 27 240.6 4.8 0.25 0.25 0.114913 1 0.114913 -2.16358 0 -2.16358

21 30 30 151.2 4.8 0.28 0.29 0.2048 1 0.212114 -1.58572 0 -1.55063

22 35 33 1025 6.4 0.29 0.25 0.074168 0.942857 0.063938 -2.60143 -0.0588 -2.74985

23 30 29 83 12.9 0.39 0.4 10.08685 0.966667 10.34549 2.311233 -0.0339 2.336551

24 27 26 37.8 6.4 0.3 0.29 2.080508 0.962963 2.011158 0.732612 -0.0377 0.698711

25 27 26 12.4 1.6 0.3 0.29 0.099097 0.962963 0.095794 -2.31166 -0.0377 -2.34556

26 26 26 0 0 0.21 0.27 #DIV/0! 1 #DIV/0! #DIV/0! 0 #DIV/0!

27 26 25 0 0 0.25 0.26 #DIV/0! 0.961538 #DIV/0! #DIV/0! -0.0392 #DIV/0!

28 THUR 25 24 0 0 0.26 0.27 #DIV/0! 0.96 #DIV/0! #DIV/0! -0.0408 #DIV/0!

29 25 25 14.1 0 0.31 0.31 0 1 0 #NUM! 0 #NUM!

30 25 26 21.9 1.6 0.3 0.27 0.05611 1.04 0.050499 -2.88045 0.03922 -2.98581

31 36 26 102 0 0.3 0.27 0 0.722222 0 #NUM! -0.3254 #NUM!

32 30 28 250 4.8 0.33 0.36 0.145981 0.933333 0.159252 -1.92428 -0.069 -1.83726

33 29 29 170.1 3.2 0.27 0.29 0.052013 1 0.055865 -2.95627 0 -2.88481

34 27 26 16 1.6 0.25 0.26 0.064 0.962963 0.06656 -2.74887 -0.0377 -2.70965

35 26 26 0 0 0.23 0.27 #DIV/0! 1 #DIV/0! #DIV/0! 0 #DIV/0!

36 26 25 0 1.6 0.22 0.29 #DIV/0! 0.961538 #DIV/0! #DIV/0! -0.0392 #DIV/0!

37 FRI 24 23 0 0 0.22 0.3 #DIV/0! 0.958333 #DIV/0! #DIV/0! -0.0426 #DIV/0!

38 27 26 60.2 0 0.38 0.31 0 0.962963 0 #NUM! -0.0377 #NUM!

39 36 29 341 1.6 0.3 0.38 0.003604 0.805556 0.004564 -5.62584 -0.2162 -5.38946

40 30 35 1092 0 0.37 0.4 0 1.166667 0 #NUM! 0.15415 #NUM!

41 31 36 639 4.8 0.43 0.46 0.07442 1.16129 0.079612 -2.59803 0.14953 -2.53059

42 32 32 422.5 3.2 0.36 0.36 0.027921 1 0.027921 -3.57839 0 -3.57839

43 29 30 79.2 1.6 0.33 0.35 0.017067 1.034483 0.018101 -4.07063 0.0339 -4.01179



Table 4 Modified Input Data Table

S/N TEMP

@ GL

TEMP

@ 4m SR WS PGL P4M X1 X2 Y LnX1 LnX2 LnY

1 31 32 350.9 9.7 0.26 0.28 0.67625 1.032 0.728266 -0.3912 0.031749 -0.31709

2 29 28 14 4.8 0.01 0.02 0.07899 0.966 0.157989 -2.5384 -0.03509 -1.84523

3 28 28 280 4.8 0.26 0.28 0.10269 1 0.110592 -2.276 0 -2.20191

4 31 32 350.9 9.7 0.25 0.3 0.65024 1.032 0.780285 -0.4304 0.031749 -0.2481

5 34 31 413.8 8 0.37 0.4 0.45781 0.912 0.494925 -0.7813 -0.09237 -0.70335

6 37 34 1220 11.3 0.31 0.32 0.36664 0.919 0.378465 -1.0034 -0.08456 -0.97163

7 33 33 600 12.9 0.34 0.35 1.21646 1 1.252235 0.19594 0 0.22493

8 29 28 14 4.8 0.29 0.35 2.29083 0.966 2.7648 0.82892 -0.03509 1.016968

9 27 27 240.6 4.8 0.25 0.25 0.11491 1 0.114913 -2.1636 0 -2.16358

10 30 30 151.2 4.8 0.28 0.29 0.2048 1 0.212114 -1.5857 0 -1.55063

11 35 33 1025 6.4 0.29 0.25 0.07417 0.943 0.063938 -2.6014 -0.05884 -2.74985

12 30 29 83 12.9 0.39 0.4 10.0869 0.967 10.34549 2.31123 -0.0339 2.336551

13 27 26 37.8 6.4 0.3 0.29 2.08051 0.963 2.011158 0.73261 -0.03774 0.698711

14 27 26 12.4 1.6 0.3 0.29 0.0991 0.963 0.095794 -2.3117 -0.03774 -2.34556

15 25 26 21.9 1.6 0.3 0.27 0.05611 1.04 0.050499 -2.8804 0.039221 -2.98581

16 30 28 250 4.8 0.33 0.36 0.14598 0.933 0.159252 -1.9243 -0.06899 -1.83726

17 29 29 170.1 3.2 0.27 0.29 0.05201 1 0.055865 -2.9563 0 -2.88481

18 27 26 16 1.6 0.25 0.26 0.064 0.963 0.06656 -2.7489 -0.03774 -2.70965

19 36 29 341 1.6 0.3 0.38 0.0036 0.806 0.004564 -5.6258 -0.21622 -5.38946

20 31 36 639 4.8 0.43 0.46 0.07442 1.161 0.079612 -2.598 0.149532 -2.53059

21 32 32 422.5 3.2 0.36 0.36 0.02792 1 0.027921 -3.5784 0 -3.57839

22 29 30 79.2 1.6 0.33 0.35 0.01707 1.034 0.018101 -4.0706 0.033902 -4.01179

Table 5 Results from Multiple Regression analysis

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.996011

R Square 0.992037

Adjusted R Square 0.991199

Standard Error 0.168763

Observations 22

ANOVA

df SS MS F

Significance

F

Regression 2 67.41776 33.70888 1183.555 1.15E-20

Residual 19 0.54114 0.028481

Total 21 67.9589

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.055953 0.051002 1.097082 0.286316 -0.05079 0.162702 -0.05079 0.162702

LnX1 0.993589 0.020575 48.29019 2.4E-21 0.950524 1.036653 0.950524 1.036653

LnX2 -0.38145 0.548898 -0.69493 0.495509 -1.5303 0.767409 -1.5303 0.767409



From Table 5, the parameters of Equation (12) are thus, substituted to yield

Equation (13) as:

Equation (13a)

OR

Equation (13b)

Substituting the terms of Equation (11) into Equation (13) yields Equation (14) as:

Equation (14a)

OR

…………………….. Equation (14b)

……………… Equation (14c)

OR

…… ……………………………………… Equation (15)

2.5. Model Verification

The model was verified by plotting the observed against predicted ozones (See Figure

3) and the correlation coefficient was obtained together with the mean square error

(MSE). Table 6 shows observed and predicted Ozones with differences in error.

Table 6 Observed and predicted Ozone concentrations

S/N OBSERVED PREDICTED DIFF IN

ERROR

PERCENTAGE

ERROR (%)

1 0.28 0.272334763 5.88E-05 0.02098425

2 0.02 0.010893837 8.29E-05 0.414611

3 0.28 0.279004013 9.92E-07 0.000354282

4 0.3 0.261926201 0.001449614 0.483204667

5 0.4 0.407361415 5.42E-05 0.0135476

6 0.32 0.340771576 0.000431458 0.134830625

7 0.35 0.359114913 8.31E-05 0.0237376

8 0.35 0.30917434 0.001666734 0.476209714

9 0.25 0.268079786 0.000326879 0.1307516

10 0.29 0.299139094 8.35E-05 0.028801034

11 0.25 0.318925135 0.004750674 1.9002696

12 0.4 0.411666754 0.000136113 0.03402825

13 0.29 0.320356599 0.000921523 0.317766552

14 0.29 0.326670352 0.001344715 0.463694828

15 0.27 0.318378615 0.00234049 0.866848148

16 0.36 0.362744786 7.53E-06 0.002092736

17 0.29 0.291001258 1.00E-06 0.000345697

18 0.26 0.272989405 0.000168725 0.064894231



S/N OBSERVED PREDICTED DIFF IN

ERROR

PERCENTAGE

ERROR (%)

19 0.38 0.35719472 0.000520081 0.136863421

20 0.46 0.436747523 0.000540678 0.117538696

21 0.36 0.38955228 0.000873337 0.242593611

22 0.35 0.353615691 1.31E-05 0.0037352

Estimating the Mean Square Error (MSE)

The Computation of MSE value is obtained via Equation (15), viz:

MSE =

…………………. Equation (16)

where N is number of observations

MSE = 0.000720732

Figure 3 Plot of observed against predicted Ozone concentration

2.7. Sensitivity Analysis

A sensitivity analysis was carried out to estimate the significance of each

meteorological parameter to the model developed as Equation (15). Table 7 presents

wind speed, ground level temperature, four metre height temperature and solar

radiation as they relate to ground level ozone and four metre height ozone

concentrations. Tables 8,9,10 and 11 present regression summaries of the significance

of each of the meteorological parameters related to pollutant uplifting.

Table 7 Field meteorological parameters with ozone concentrations

S/N WS SR TEMP

@ GL

TEMP

@ 4M PGL P4M

1 0 0 25 29 0 0

2 4.8 280 28 28 0 0.28

3 9.7 350.9 31 32 0.26 0.28

4 8 413.8 34 31 0 0.32

5 11.3 1220 37 34 0 0

6 12.9 600 33 33 0 0.25

7 4.8 14 29 28 0.01 0.02

8 1.6 0 27 26 0 0.32

9 1.6 0 26 26 0.28 0

10 0 0 25 29 0.3 0.27

11 4.8 280 28 28 0.26 0.28

y = 0.9436x + 0.025 R² = 0.9059

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 0.1 0.2 0.3 0.4 0.5

pre

dic

ted

co

nce

ntr

atio

n.

observed concentration.

Linear (PREDICTED)



S/N WS SR TEMP

@ GL

TEMP

@ 4M PGL P4M

12 9.7 350.9 31 32 0.25 0.3

13 8 413.8 34 31 0.37 0.4

14 11.3 1220 37 34 0.31 0.32

15 12.9 600 33 33 0.34 0.35

16 4.8 14 29 28 0.29 0.35

17 1.6 0 27 26 0.27 0.29

18 1.6 0 26 26 0.29 0.3

19 1.6 0 24 24 0.25 0.24

20 4.8 240.6 27 27 0.25 0.25

21 4.8 151.2 30 30 0.28 0.29

22 6.4 1025 35 33 0.29 0.25

23 12.9 83 30 29 0.39 0.4

24 6.4 37.8 27 26 0.3 0.29

25 1.6 12.4 27 26 0.3 0.29

26 0 0 26 26 0.21 0.27

27 0 0 26 25 0.25 0.26

28 0 0 25 24 0.26 0.27

29 0 14.1 25 25 0.31 0.31

30 1.6 21.9 25 26 0.3 0.27

31 0 102 36 26 0.3 0.27

32 4.8 250 30 28 0.33 0.36

33 3.2 170.1 29 29 0.27 0.29

34 1.6 16 27 26 0.25 0.26

35 0 0 26 26 0.23 0.27

36 1.6 0 26 25 0.22 0.29

37 0 0 24 23 0.22 0.3

38 0 60.2 27 26 0.38 0.31

39 1.6 341 36 29 0.3 0.38

40 0 1092 30 35 0.37 0.4

41 4.8 639 31 36 0.43 0.46

42 3.2 422.5 32 32 0.36 0.36

43 1.6 79.2 29 30 0.33 0.35

Table 8 Regression summary of significance of wind speed to Ozone uplifting

SUMMARY OUTPUT


Multiple R 0.612107

R Square 0.374675

Adjusted R

Square 0.3434087


Observations 43

ANOVA

df SS MS F

Significance

F

Regression 2 0.161106757 0.080553 11.98337 8.36E-05

Residual 40 0.268883941 0.006722

Total 42 0.429990698

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.1371951 0.032979952 4.159956 0.000164 0.07054 0.20385 0.07054 0.20385

WS 0.0034904 0.003124896 1.11697 0.270672 -0.00283 0.009806 -0.00283 0.009806

PGL 0.5203211 0.106927182 4.866126 1.81E-05 0.304213 0.736429 0.304213 0.736429



Table 9 Regression summary of significance of solar radiation to Ozone uplifting

SUMMARY OUTPUT


Multiple R 0.6017704

R Square 0.3621276

Adjusted R

Square 0.3302339


Observations 43

ANOVA

df SS MS F Significance F

Regression 2 0.155711484 0.077855742 11.35423 0.000124359

Residual 40 0.274279214 0.00685698

Total 42 0.429990698

Coefficients Standard Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.1487193 0.030675323 4.848173089 1.92E-05 0.086722135 0.210716 0.086722 0.210716

SR 2.441E-05 3.6961E-05 0.660493418 0.512723 -5.02885E-05 9.91E-05 -5E-05 9.91E-05

PGL 0.5059721 0.107212431 4.719341636 2.88E-05 0.289287684 0.722656 0.289288 0.722656

Table 10 Regression summary of significance of wind speed to Ozone uplifting

SUMMARY OUTPUT


Multiple R 0.615835

R Square 0.379252



Observations 43

ANOVA

df SS MS F

Significance

F

Regression 2 0.163075 0.081538 12.21922 7.22E-05

Residual 40 0.266916 0.006673

Total 42 0.429991

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.032137 0.102542 0.313402 0.755604 -0.17511 0.239381 -0.17511 0.239381

TEMP @ GL 0.004215 0.003384 1.245708 0.220119 -0.00262 0.011054 -0.00262 0.011054

PGL 0.506045 0.105763 4.784682 2.35E-05 0.292289 0.719801 0.292289 0.719801



Table 11 Regression summary of significance of temperature at four metre height to Ozone

uplifting

8


Multiple R 0.613032

R Square 0.375808



Observations 43

ANOVA

df SS MS F Significance F

Regression 2 0.161594 0.080797 12.04142 8.06E-05

Residual 40 0.268397 0.00671

Total 42 0.429991

Coefficients Standard Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0% Upper 95.0%

Intercept 0.030771 0.111589 0.275749 0.784161 -0.19476 0.2563 -0.19476 0.2563

TEMP @ 4m 0.004393 0.00382 1.149997 0.256974 -0.00333 0.012113 -0.00333 0.012113

PGL 0.500585 0.10616 4.715383 2.92E-05 0.286028 0.715142 0.286028 0.715142

2.8. Modified Equation and Verification

Equation (15) is rewritten by removing the solar radiation parameter (See Equation

17). Table 12 shows new values of observed and predicted ozone values and these are

used to estimate correlation coefficient by plotting observed against predicted ozone

concentrations.

…… ………………………………………………Equation (17)

Table 12 Observed and predicted Ozone concentrations

POINTS OBSERVED PREDICTED DIFF IN ERROR PERCENTAGE ERROR

(%)

1 0.28 0.262292523 0.000313555 0.111983929

2 0.02 0.010711075 8.63E-05 0.4314205

3 0.28 0.269104971 0.000118702 0.042393571

4 0.3 0.252267773 0.002278366 0.759455333

5 0.4 0.391925614 6.52E-05 0.016298925

6 0.32 0.325594238 3.13E-05 0.009779844

7 0.35 0.344685261 2.82E-05 0.008070429

8 0.35 0.303987423 0.002117157 0.604902

9 0.25 0.25881985 7.78E-05 0.03111592

10 0.29 0.289667698 1.10E-07 3.80776E-05

11 0.25 0.305061216 0.003031738 1.2126952

12 0.4 0.400168211 2.83E-08 7.0737E-06

13 0.29 0.31298273 0.000528206 0.18214

14 0.29 0.321439907 0.000988468 0.340851034

15 0.27 0.31214063 0.001775833 0.657715926

16 0.36 0.350128921 9.74E-05 0.027066167

17 0.29 0.281574824 7.10E-05 0.024477103



POINTS OBSERVED PREDICTED DIFF IN ERROR PERCENTAGE ERROR

(%)

18 0.26 0.268179872 6.69E-05 0.025734731

19 0.38 0.344086428 0.001289785 0.339417105

20 0.46 0.419029294 0.001678599 0.364912826

21 0.36 0.374741314 0.000217306 0.060362778

22 0.35 0.343841902 3.79E-05 0.010834914

Figure 4 Plot of observed against predicted Ozone concentration

3. DISCUSSION OF RESULTS

The development of a model to predict ozone concentration in obstruction prone areas

is presented as Equation (15). The model developed is obtained via dimensional

analysis and the multiple linear regression was employed to calibrate it. This model

achieves a correlation coefficient of 0.996 and shows a very high significance in the

x1 term made of wind speed, ground level ozone concentration and solar radiation.

Verification of the model shows a correlation coefficient of 0.905 and MSE of 0.0007

when the observed concentrations were plotted against the predicted.

The maximum ozone gas measured from the Choba study area was 0.45mg/m and

this lies within the range for urban areas (0.3mg/m3 – 0.8mg/m

3) as stated by the

World Bank group in 1998.

Detailed sensitivity analysis on individual meteorological parameters was carried

out to evaluate the significance of each parameter on the uplifting of ozone. A

summary of the results of significance is presented on Table 13. Because of the

sensitivity in variation of air pollution concentrations 20% level of significance was

selected for critical value of t-statistic. Temperature at ground level is the most

significant meteorological parameter in pollutant uplifting. Though it has been

established that thermal effects result mainly from the variation of solar heating of the

ground (Xie, 2005), surprisingly solar radiation shows no significance in pollutant

uplifting. This is because the process of heating takes time and so the time a high

solar radiation is measured would be different from the time pollutants start showing

significant uplifting (See Figure 5).

y = 0.9436x + 0.025 R² = 0.9059

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 0.1 0.2 0.3 0.4 0.5

pre

dic

ted

observed

Linear (PREDICTED)



Table 13 Results of Significance.

S/N Parameter t-statistic t-critical (t.80) comment

1 Wind speed 1.11697 0.858 Significant

2 Solar radiation 0.66049 0.858 Non-Significant

3 Temperature at ground level 1.245708 0.858 Significant

4 Temperature at 4 m height 1.149997 0.858 Significant

Figure 6 Plot showing solar radiation against ground level and four metre height ozone

With the information obtained from Table 13, Equation (15) is rewritten with

solar radiation removed and the verification of the modified model shows no

reduction in the correlation coefficient which further confirms the irrelevance of solar

radiation in the developed model.

Figures 6 and 7 show a plot of the concentrations of ozone at ground level and

four metre height measured on the field with corresponding wind speed and ground

level temperatures. In agreement with the modeling carried out, peak values of wind

speed and temperatures are associated with ground level uplifting (the case where the

4 metre height ozone concentrations are greater than the ground level concentrations).

Figure 6 Field observations of average wind speed, ground and four metre height ozone

concentrations

0

200

400

600

800

1000

1200

1400

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 2 3 4 5 6 7 8 9

SOLA

R R

AD

IATI

ON

OZO

NE

CO

NC

ENTR

ATI

ON

NUMBER OF OBSERVATIONS

SAMPLE DAY RECORD

GROUND LEVEL OZONE

FOUR METRE OZONE

SOLAR RADIATION

SIGNIFICAN

0

2

4

6

8

10

12

14

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

win

d s

pee

d

ozo

ne

con

ct.

sampling number

OZONE CONC. GL

OZONE CONC. @ 4M

WIND SPEED



Figure 7 Field observations of ground level temperature, ground and four metre height Ozone

concentrations

High temperatures and wind speed have been seen to facilitate vertical uplifting of

ozone and this agrees with works of Xie (2015), which considered heating of the

earth’s surfaces with small velocities of 1 & 2 m/s. In this work higher wind velocities

and temperatures have shown increased pollutant uplifting (See to Figures 6 & 7).

Other measured pollutants also confirmed temperature as the most important

meteorological variable in pollutant uplifting when their significance are considered

in relation to pollutant uplifting (See Table 14).

Table 14 T-statistic for other pollutants on relating the significance of pollutant uplifting

S/N VARIABLES NO CO TSP t-critical (t.80)

t-statistic values

1 Temperature @ ground level 2.226274 -1.76596 -

1.41408

0.858

Lastly, efforts to calibrate the general model (Equation 11) for other pollutants

like NO2, CO and TSP measured from the study area failed to produce high

correlation coefficients (See Figures 8-10 and Table 15). This may be taken as an

unexplained variation which can be attributed to the fact that ozone is a secondary

pollutant and can be formed hundreds of kilometers from the source of

emission(World Bank group, 1998) and primary pollutants do not have this property.

The other measured pollutants still show trends of pollutant uplifting during periods

of high temperature and wind speed (See Figures 11 -16).

Figure 8 Comparison of observed and predicted TSP

0

5

10

15

20

25

30

35

40

0

0.1

0.2

0.3

0.4

0.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

gro

un

d le

vel t

emp

.

ozo

ne

con

ct.

sampling number

OZONE CONC. GL

OZONE CONC. @ 4M

temp. @ GL

y = 0.5606x + 98.591 R² = 0.2345

0

200

400

600

800

0 50 100 150 200 250 300 350 400 450 500

PR

EDIC

TED

TSP

OBSERVED TSP

OBSERVED VERSUS PREDICTED TSP



Figure 9 Comparison of observed and predicted CO

Figure 10 Comparison of observed and predicted NO2

Figure 11 Field observations of ground level temperature, ground and four metre height CO

concentrations

y = -0.2187x + 7.7625 R² = 0.036

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

PR

EDIC

TED

CO

OBSERVED CO

OBSERVED VERSUS PREDICTED CO

y = 0.6825x + 0.0333 R² = 0.3362

0

0.05

0.1

0.15

0.2

0.25

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

PR

EDIC

TED

NO

2

OBSERVED NO2

OBSERVED VERSUS PREDICTED NO2

0

10

20

30

40

50

60

70

80

90

100

0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

CO

NC

T.

TEM

P/C

ON

CT

SAMPLE NUMBER

GL TEMP

4 M CO POLLUTANT

GL CO POLLUTANT



Figure 12 Field observations of average wind speed, ground and four metre height CO

concentrations

Figure 13 Field observations of average wind speed, ground and four metre height NO2

concentrations

Figure 14 Field observations of ground level temperature, ground and four metre height NO2

concentrations

0

10

20

30

40

50

60

70

80

90

100

0

2

4

6

8

10

12

14

16

18

20

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

CO

CO

NC

T.

CO

CO

NC

T/ W

S

SAMPLE NUMBER

WS

4 M CO CONCT

GL CO CONCT

0

0.5

1

1.5

2

2.5

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

NO

2 C

ON

CT.

WS

SAMPLE NUMBER

WS

GL NO2 CONCT. 4 M NO2 CONCT.

0

0.5

1

1.5

2

2.5

0

5

10

15

20

25

30

35

40

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

NO

2 C

ON

CT.

TEM

P

SAMPLE NUMBER

GL TEMP.

GL NO2 CONCT

4 M NO2 CONCT.



Figure 15 Field observations of average wind speed, ground and four meter height TSP

concentrations

Figure 16 Field observations of ground level temperature, ground and four metre height TSP

concentrations

4. CONCLUSION

From the research carried out, the following conclusions can be drawn;

1. A model has been developed and calibrated for predicting ozone uplifting from

ground level to a maximum of four metre height. The model yielded a correlation

coefficient of 0.996.

2. The process of pollutant uplifting is generally facilitated by ground level temperature

and wind speed velocity.

3. The most important meteorological parameter during ozone uplifting has been

established statistically through test of significance to be ground level Temperature

0

2

4

6

8

10

12

14

0

200

400

600

800

1000

1200

1400

1600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

WS

TSP

CO

NC

T.

SAMPLE NUMBER

GL TSP CONCT.

4 M TSP CONCT.

WS

0

5

10

15

20

25

30

35

40

0

200

400

600

800

1000

1200

1400

1600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

GL

TEM

P.

TSP

CO

NC

T.

SAMPLE NUMBER

GL TSP

4 M TSP CONCT.

GL TEMP



4. NO2, CO and TSP also show similar trend in uplifting as temperature remains the

most important variable.

5. Solar radiation though responsible for heating the earth’s surface is found to have a

negligible effect in the developed model.

6. The maximum amount of ozone measured from the study area (0.45 mg/m3) lies

within the WHO range for urban ozone which is 0.3–0.8 mg/m3.

7. The property of ozone that makes it form hundreds of kilometers away from any

source is suspected to be the reason why it is the most responsive pollutant in

calibrating the uplifting model.

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