American Journal of Environmental Engineering

p-ISSN: 2166-4633    e-ISSN: 2166-465X

2018;  8(3): 54-73

doi:10.5923/j.ajee.20180803.02

 

Assessment of Vehicular Emission and Volatile Organic Contaminants in the Central State of North Central Nigeria

A. O. Lawal 1, A. G. Salisu 1, Aminu Saidu 1, H. H. Isah 2, S. K. Habila 2

1Department of Applied Science, Kaduna Polytechnic, Kaduna, Nigeria

2Kaduna State Environmental Protection Authority, Nigeria

Correspondence to: A. O. Lawal , Department of Applied Science, Kaduna Polytechnic, Kaduna, Nigeria.

Email:

Copyright © 2018 The Author(s). Published by Scientific & Academic Publishing.

This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/

Abstract

The aim of this study is to determine the vehicular emission profile in the central state of north central Nigeria. Powdered active carbon was used as adsorbent in an improvised sampler for measurement of Benzene, Toluene, Ethyl benzene and Xylene (VOCs). Commercial air samplers were used for the measurement of CO, NOx, SO2 in the sampling locations. Gas chromatography-Mass spectrometer (GC-MS) revealed the presence of VOCs in carbon disulphide extracts indicating the effectiveness of the derivatization of the active carbon into canister adsorbent for sampling VOCs in ambient air. This also indicates potential of active carbon for domestication for mopping the carcinogens from the air. The vehicular emission profile indicates significant difference among the locations on the levels of CO, H2O, SO2 and O2 measured, since the Wilk’s lambda (λ)=1.622, F(36, 320)= 0.002, and its p-value (0.000) is less than 0.05 level of significance at 95% confidence interval. The Multivariate partial Eta Squared (η2)=0.801 indicates 80% of the variance parameters is associated with the locations and the time of sampling of CO, H2O, SO2 and O2, with Wilk’s lambda (λ) =0.031, F (4, 77)= 604.664, and p-value (0.000) is less than 0.05 level of significance at 95% confidence limit. The interaction of the location and time is statistically significant, (p=0.000) with Wilk’s lambda (λ)=0.002, F (36, 290)= 36.018. The effect of location on the parameters was not the same for the morning and evening sampling periods.

Keywords: Vehicular, Emission, VOCs, Diffusive, Air sampler

Cite this paper: A. O. Lawal , A. G. Salisu , Aminu Saidu , H. H. Isah , S. K. Habila , Assessment of Vehicular Emission and Volatile Organic Contaminants in the Central State of North Central Nigeria, American Journal of Environmental Engineering, Vol. 8 No. 3, 2018, pp. 54-73. doi: 10.5923/j.ajee.20180803.02.

1. Introduction

Even though significant improvements in fuel and engine technology have been achieved during the recent years, urban environments are still largely affected by traffic emissions [1]. The main traffic-related pollutants are CO, NOx, hydrocarbons, and particulates. As lead-containing antiknock additives were reduced and eliminated, throughout the past 15 years more aromatics including benzene are blended into gasoline [2] for antiknock purposes and, therefore, benzene concentrations have increased to more than 5% [3, 4]. Other volatile organic contaminants of similar origin are toluene, ethylbenzene and xylene. Benzene and its alkyl derivatives (BTEX) are especially harmful for human health, because of their toxic, mutagenic or carcinogenic properties [5]. In particular, benzene is characterized as a human carcinogen [6]. It has low acute toxicity but repeated exposure to high concentrations can cause effects on blood and blood-forming organs [7]. The most convincing evidence is found for the development of leukemia as a result of benzene exposure [8]. Toluene and xylenes strongly affect the nervous system. They may induce brain function disturbances and problems with balance, vision, hearing and speech. They may also induce damage to kidneys or liver. Some studies suggest a connection between the brain tumors and toluene and xylenes exposure. To evaluate the impacts of atmospheric BTEX on air quality, the ambient levels of BTEX at rural, suburban, city-center and industrial sites in many nations are being investigated [9-18]. These measurements provide useful information about the spatial and temporal variations of these compounds, and the ratios of benzene/toluene (B/T) and ethylbenzene/m, p-xylene (E/X) are recognized as useful indicators of atmospheric photochemical activity as well as the sources [19].
Kaduna, the north central state has many petrol stations to support gasoline demand. Large amount of gasoline consumption can cause enormous emission of BTEX [1]. Workers in petrol stations may be exposed to emission of BTEX as there is no implementable strategy to prevent the exposure. This study is expected to produce an adsorbent based trap for BTEX in the air especially in petrol stations. The strategy was used to investigate possible use of the device as air sampler for BTEX because commercial air samplers are expensive and scarce. The approach presented in this study produced a locally fabricated air diffusive sampler for sampling Benzene, Xylene and Toluene in Petrol stations.
Monitoring of BTEX includes manual sampling followed by analysis at a laboratory and automatic measurements in situ [20, 21]. The principle of passive sampling is diffusive uptake of BTEX on sorption cartridge, according to Fick’s 1st law [22, 23]. Many different adsorbing materials and cartridge types are used, depending on the desorption method and sampling time [24, 25]. Ambient air is sucked through a sorption tube and then the trapped compounds are injected into a chromatograph by thermal desorption, either directly or via a cryo-focusing trap [26, 27]. The automatic BTEX measurements can also be performed along an optical path by means of differential optical absorption spectroscopy [28, 29]. The objective of this study is to determine the vehicular emission profile in Kaduna, the central state of north central Nigeria using fabricated diffusive air sampler for measurement of Benzene, Toluene, Ethyl benzene and Xylene and commercial air sampler for CO, NOx, SO2 and methane.

2. Materials and Methods

Commercial activated carbon was used as adsorbent. Gas chromatography-Mass spectrometer was used to determine Benzene, Toluene, Ethyl benzene and Xylene following the extraction of the BTEX into Carbon disulphide.

2.1. Collections of Air Samples

Air samples were collected from Petrol Stations and roads within Kaduna metropolis. Cans with adjustable vents were used as adsorbent carrier for the improvised air sampler (Figure 1).
Figure 1. Improvised Air sampler

2.2. Design of Improvised Air Sampler

Plastic cans were used as the carbon adsorbent carrier and the content was sealed with polyethylene sheet and corked with plastic cover equipped with venting option.
The air was sampled readily by using the ambient volatile organic canisters improvised for air BTEX and carbon disulphide was used to desorb BTEX from the adsorbent. Air samples were taken at different times and labeled as UPT1, UPT2, UPT3, DNT1, DNT2 and DNT3. UPT denotes the upstream of the petrol station (nozzle point) and DNT denotes the downstream of the petrol station (away from nozzle point but within the station).

2.3. Extraction of BTEX from Carbon Adsorbent Samples

The carbon adsorbents were emptied from the plastic canisters into 250cm3 conical flasks and extracted with carbon disulphide in three batches of 20cm3. The carbon disulphide extracts were analysed for BTEX using GC-MS. The analytes removed from the sorbents by solvent desorption with carbon disulphide were detected by flame ionization detector coupled with a mass detector. GC-MS is a very powerful and sensitive instrument used to study trace amounts of chemicals in the air. The GC-MS is used to detect chemicals in amounts as small as a picogram (0.000000000001g). Many of the pollutants found in air are sometimes present at concentrations lower than one picogram in a cubic meter of air. It is important for the instrument to be able to detect these low concentrations.

2.4. Characterisation of the Adsorbent (Commercial Active Carbon)

The characterization data reported for the commercial carbon by Wyasu G. et al., 2016 [30] was adopted because the same batch of the commercial carbon was used in this study. Nitrogen gas adsorption-desorption isotherm of the commercial active carbon at liquid nitrogen temperature (–195.6°C) was carried out using Micrometrics Tristar II adsorption unit, Autosorb –1. The surface morphology of the carbon was determined by the Scanning Electron Microscope (SEM) (JEOL, JSM-T330). The possible surface functional groups on the commercial active carbon were determined by FTIR-2000, Perkin Elmer spectrophotometer. Shimadzu Differential Scanning Calorimeter (DSC-60) coupled with TA—60workstation was used to determine the heat flow characteristic of the commercial carbon (Wyasu G. et al., 2016) [30].

2.5. Air Sampling with Commercial Gas Samplers

The sampling locations are highlighted in Figure 3. MSA-ALTAIR-5X multi gas meter (Figure 2a) was used to determine CO, H2S, SO2, O2 and CH4. Methane (CH4) was actually determined as combustible. Carbon dioxide (CO2), Humidity and Temperature were determined by Lutron MCH-383SD (Figure 2b).
Figure 2a-b. Commercial Air Samplers used
Figure 3. Metropolitan map of Kaduna city center showing sampling locations

2.6. GPS Coordinates of Sampling Location

Leventis roundabout
328175.90E
1163572. 88N
FRCN junction
329219. 89E
1163695. 11N
Police Hq junction
329274. 30E
1163334. 54N
Police college roundabout
329555. 57E
1161722. 69N
Stadium roundabout
327679. 68E
1161327. 44N
Station market roundabout
326956. 24E
1160462. 71N
Dutsinma junction
324705. 06E
1162681. 75N
Command junction
327465. 55E
1155003. 75N
NNPC Refinery junction
335214. 60E
1154664. 41N
Mando roundabout
328503. 50E
1170534. 79N
Kawo overhead bridge
330069. 86E
1170532. 34N

2.7. Distances (as the Crow Flies) of Air Quality Collection/ Sampling Points from City Center (Leventis Round about)

Reference point for adjoining distances is Leventis round about.
To formulate mathematical model to represent dynamic behaviour of the vehicular emission profile with respect to effect of location and sampling time on the contaminants, the General Linear Model was applied as described below.
General Linear Model
In matrix terms, the general linear regression model is:
(1)
where y is a vector of responses, β is a vector of parameters, X is the design matrix of constants and ε is a vector of independent normal random variables.
In fitting a General Linear model, it is much more convenient to express the mathematical operations using matrix notation. For k regressor variables and n observations, (xi1, xi2, …, xik, yi), i = 1, 2, … , n and that the model relating the regressors to the response is as shown in equation 2.
(2)
This model is a system of n equations that can be expressed in matrix notation as
(3)
where
(4)
In general, y is an (n x 1) vector of the observations, X is an (n x k) matrix of the levels of the independent variables, β is a (k x 1) vector of the regression coefficients, and ε is an (n x 1) vector of random errors.
The aim is to find the vector of least squares estimators, β, that minimizes
(5)
The least squares estimator is the solution for β in the equations
(6)
However, the resulting equations that must be solved are
(7)
To solve the normal equations multiply both sides by the inverse of XX. Therefore, the least squares estimate of β is
(8)
Note that there are p = k + 1 normal equations in p = k + 1 unknowns (the values of β0, β1, … , βk). For two predictors (i.e. k = 2), we have three equations in three unknowns (β0, β1, β2). The procedures are as follows:
The matrix of the predictor variables is:
(9)
The vector of dependent variable is:
(10)
Then the design matrix is:
(11)
The vector of observations is:
(12)
Then the vector of parameters is defined as in equation 6
(13)

3. Results and Discussion

3.1. Characterisation of the Adsorbent (Commercial Activated Carbon)

Figure 4. Nitrogen adsorption-desorption isotherm of the commercial active carbon [30]
Figure 5. SEM image of commercial active carbon [30]
Figure 6. DSC of commercial activated carbon [30]
Figure 7. FTIR Spectral of the commercial carbon [30]

3.2. Air sampling with Commercial Gas Samplers

Figure 8. Sampling Locations
Multivariate analysis summary for air sampling with commercial gas samplers is presented in Table 1.
Table 1. Multivariate Testsa for the effect of location and sampling time on vehicular emission profile
     
The result from the Multivariate test (Table 1) shows that there is significant difference among the locations on the parameters (CO, H2O, SO2 and O2) measured, since the Wilk’s lambda (λ)=1.622, F(36, 320)= 0.002, and its p-value (0.000) is less than 0.05 level of significance at 95% confidence interval. Hence the Multivariate partial Eta Squared (η2)=0.801 indicates that approximately 80% of the multivariate variance of the parameters is associated with the locations.
Furthermore results on the Multivariate test table shows that there is significant difference among the time on the parameters (CO, H2O, SO2 and O2) measured, since the Wilk’s lambda (λ) =0.031, F (4, 77)= 604.664, and p-value (0.000) is less than 0.05 level of significance at 95% confidence limit. The Multivariate partial Eta Squared (η2)=0.969 indicates that approximately 97% of the multivariate variance of the parameters is associated with the Time.
The findings indicate that the interaction of the location and time is statistically significant, since the p-value (0.000) from the table is less than 0.05 level of significance, with the Wilk’s lambda (λ) =0.002, F(36, 290)= 36.018, hence Multivariate partial Eta Squared (η2)=0.796 indicates location and time account for approximately 80% of the multivariate variability in the parameters. This implies that the effect of location on the parameters is not the same for evening and morning time.
The result from the ANOVA table (Table 2) depicts that there is significant difference in the average parameters (CO, SO2, and O2) among locations, since their p-values (0.002, 0.001, and 0.000) respectively is less than 0.05 level of significant at 95% confidence limit, this implies that the parameters differ significantly across the various location. Meanwhile the parameter H2S did not indicate any significant difference among the locations, considering its p-value (0.539) from the table which is greater than 0.05 level of significance at 95% confidence limit suggesting that the average level of H2S measured at various locations did not differ statistically.
Table 2. ANOVA Table for air sampling with commercial gas samplers (Tests of Between-Subjects Effects)
     
The output on the table indicates that there is statistical significant difference in the average parameters (CO, SO2, O2) between time, since their p-values (0.007, 0.007, and 0.000) respectively is less than 0.05 level of significant at 95% confidence limit. This finding implies that these parameters (CO, SO2 and O2) differ between the periods of time measured. On the other hand, H2S indicates not a significant difference between the times, considering its p-value (0.161) from the table which is greater than 0.05 level of significance at 95% confidence limit. The average level of H2S measured at different periods did not differ statistically.
The ANOVA table also revealed that, the interaction effect of location and time in which the parameters (CO and H2S) are measured is not statistically significant, considering their p-values (0.063 and 0.539) greater than 0.05 level of significance at 95% confidence limit. This depicts that the various location does not have influence on period of time in which CO and H2S is measured.
Meanwhile the interaction effect of location and period of time in which the parameters (SO2 and O2) were measured is statistically significant; suggesting that the parameters SO2 and O2 measured at different times is influenced by the various locations.
Table 3 depicts the average level of CO is high across the various locations in the evening compared to the morning period, except at Dutsima junction, where high level of CO was recorded in the morning, meanwhile the level of H2S was the same across all the various locations in the morning but relatively high levels of H2S were obtained in the evening at FRCN junction and Leventis round about.
Table 3. Mean and Standard deviation of air parameters of various locations
     
Higher levels of SO2 were recorded in the morning from various locations than in the evening, except Kawo-Mando round about, Kawo fly over and police college where the levels of SO2 were low, in the morning. The table also revealed that the levels of O2 measured in the different locations were approximately the same in the morning and similarly in the evening, leaving Dustima junction as exception, where high level of O2 was recorded. The trends in the parameters studied are presented in Figures 9-15. The location mean for O2 was higher in the evening than the morning average. The mean distribution of O2 across the sampling locations was lower than the reference mean of the locations (21.226) except at Dustima junction which may have enjoyed contribution from the vegetation around the area.
Figure 9. Distribution of O2
Figure 10. Distribution of SO2
Figure 11. Distribution of H2S
Figure 12. Distribution of CO
Table 4. Carbon dioxide (CO2), Humidity and Temperature determined by Lutron MCH-383SD
     
The result from the Multivariate test (Table 5) shows that there is significant difference among the locations on the parameters (CO2, Relative humidity and Temperature) measured, since the Wilk’s lambda (λ)=0.014 F(24, 99.212)= 0.002, and its p-value (0.000) is less than 0.05 level of significance at 95% confidence interval. Hence the Multivariate partial Eta Squared (η2)=0.761 indicates that approximately 76.1% of the multivariate variance of the parameters is associated with the locations.
Table 5. Multivariate Testsa on Carbon dioxide (CO2), Humidity and Temperature
     
The ANOVA table (Table 6) decomposes the variance of CO2 into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 16.564, is a ratio of the between-group estimate to the within-group estimate. Since the P-value (0.000) of the F-test is less than 0.05, there is a statistically significant difference between the mean CO2 from one level of Sample Location to another at the 95.0% confidence level. The ANOVA table also decomposes the variance of Relative humidity into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 31.545, is a ratio of the between-group estimate to the within-group estimate. Since the P-value (0.000) of the F-test is less than 0.05, there is a statistically significant difference between the mean Relative Humidity from one level of Sample location to another at the 95.0% confidence level.
Table 6. ANOVA table for Carbon dioxide (CO2), Humidity and Temperature
     
Furthermore the ANOVA table decomposes the variance of Temperature into two components: a between-group component and a within-group component. The F-ratio in this case equals 11.740, and is a ratio of the between-group estimate to the within-group estimate. Since the P-value (0.000) of the F-test is less than 0.05, there is a statistically significant difference between the mean Temperatures from one level of Sample Location to another at the 95.0% confidence level.

3.3. Main Effect Plot of CO2

The main effects plot of CO2 (Figure 13) displays the response means for each sample location. A horizontal line is drawn at the grand mean. The effects are the differences between the means and the reference line. The variety effects upon CO2 are large. Furthermore it depicts that the level of CO2 at Refinery junction > Station Round about > Command Junction > Stadium Round about while the other locations had low levels of CO2 below the grand mean (Reference line) 1308.
Figure 13. Distribution of CO2

3.4. Main Effect Plot of Relative Humidity

The main effects plot of relative humidity (Figure 14) displays the response means for each sample location. A horizontal line is drawn at the grand mean, the effects are the differences between the means and the reference line, and the variety effects upon Relative humidity are large. It depicts that the level of Relative Humidity at Dustima Junction > Leventis Round about > FRCN Round about > Kawo flyover while the other locations had low levels of relative humidity below the grand mean (Reference line) 13.298.
Figure 14. Distribution of Relative Humidity

3.5. Main Effect Plot of Temperature

The main effects plot in Figure 15 displays the response means for each sample location. A horizontal line is drawn at the grand mean; the effects are the differences between the means and the reference line. The variety effects upon temperature are large. The level of temperature at Station Round about > Command Junction > Station Round about > Police College > Refinery junction, while other locations had low levels of temperature below the grand mean (Reference line) 36.262.
Figure 15. Distribution of Temperature
Temperature and humidity have presented possible meteorological variables which affect the distribution of vehicular emissions. This is corroborated by the findings of Pattamaporn [31] on BTEX.
The vehicular emissions reported in this studies have been corroborated by other researchers who also reported that pollutants emitted from vehicles and a range of toxic gases, such as carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx), are of concern in populated urban areas because of their effects on human health and also atmospheric visibility [32].
To formulate mathematical model to represent dynamic behavior of the vehicular emission profile with respect to effect of location and sampling time on the contaminants, the General Linear Model was formulated as described below.

3.6. General Linear Model for CO versus Locations and Time

CO = 22.25 + 7.35 Locations_Command Junction + 0.75 Locations_Dustima Junction
- 9.45 Locations_FRCN R/about + 2.45 Locations_Kawo-Mando R/about + 6.75 Locations_Kawo fly-over - 2.55 Locations_Leventis R/about - 8.85 Locations_Police College + 9.05 Locations_Refinery junction + 6.75 Locations_Stadium R/about - 12.25 Locations_Station R/about + 3.73 Time_Evening - 3.73 Time_Morning.

3.7. General Linear Model: H2S versus Locations and Time

H2S = 0.0200 - 0.0200 Locations_Command Junction - 0.0200 Locations_Dustima Junction
+ 0.0800 Locations_FRCN R/about - 0.0200 Locations_Kawo-Mando R/about
- 0.0200 Locations_Kawo fly-over + 0.0800 Locations_Leventis R/about
- 0.0200 Locations_Police College - 0.0200 Locations_Refinery junction
- 0.0200 Locations_Stadium R/about - 0.0200 Locations_Station R/about
- 0.0200 Time_Evening + 0.0200 Time_Morning.

3.8. General Linear Model: SO2 versus Locations and Time

SO2 = 0.2221 - 0.0521 Locations_Command Junction - 0.0621 Locations_Dustima Junction
- 0.0521 Locations_FRCN R/about - 0.0821 Locations_Kawo-Mando R/about
- 0.0321 Locations_Kawo fly-over + 0.3089 Locations_Leventis R/about
+ 0.0079 Locations_Police College + 0.0779 Locations_Refinery junction
- 0.0921 Locations_Stadium R/about - 0.0221 Locations_Station R/about
- 0.0561 Time_Evening + 0.0561 Time_Morning.

3.9. General Linear Model: O2 versus Locations and Time

O2 = 21.226 - 0.276 Locations_Command Junction + 3.624 Locations_Dustima Junction
- 0.386 Locations_FRCN R/about - 0.426 Locations_Kawo-Mando R/about
- 0.426 Locations_Kawo fly-over - 0.426 Locations_Leventis R/about
- 0.426 Locations_Police College - 0.426 Locations_Refinery junction
- 0.406 Locations_Stadium R/about - 0.426 Locations_Station R/about + 0.384 Time_Evening - 0.384 Time_Morning.
The improvised samplers for air BTEX are shown in Figure 16. The samplers were applied to the sampling of air BTEX in petrol station as shown in Figure 17.
Figure 16. Improvised Air BTEX Samplers
Figure 17. BTEX sampling location in petrol station

3.10. BTEX from Carbon Adsorbent Samples

The GC-MS spectral of the carbon disulphide extracts analysed for BTEX are shown in Figures 18-23. The results reveal the presence of VOCs in the carbon disulphide extracts as shown in Table 7. Vainiotalo et al., (1999) [33] also reported the emission of VOCs during gasoline refueling and Hartle (1993) [34] reported exposure to VOCs by service station attendants and operators.
Figure 18. GC-MS chromatogram for UPT1 extracted in carbon disulphide solution
Figure 19. GC-MS chromatogram for UPT2 extracted in carbon disulphide solution
Figure 20. GC-MS chromatogram for UPT3 extracted in carbon disulphide solution
Figure 21. GC-MS chromatogram for DNT1 extracted in carbon disulphide solution
Figure 22. GC-MS chromatogram for DNT2 extracted in carbon disulphide solution
Figure 23. GC-MS chromatogram for DNT3 extracted in carbon disulphide solution
Table 7. Hydrocarbon components in carbon disulphide extract of UPT1
     
All the compounds detected in UPT2, UPT3, DNT1, DNT2 and DNT3 were as detected in UPT1. This confirms consistency in detection by the improvised sampler as the ambient wind is expected to have capacity to redistribute the VOCs from nozzle point. Temporal variation of BTEX concentration in the area of petrol station, i.e. front, center, and back has been reported by Pattamaporn [31] [35] [36]. However our improvised sampler would be applied to the quantification of BTEX in our future studies on validations.

4. Conclusions

The vehicular emission profile indicates significant difference in the levels of CO, H2O, SO2 and O2 among the study locations. 80% of the variance parameters were associated with the locations and the time of sampling. The interaction of the location and time is statistically significant, (p=0.000) with Wilk’s lambda (λ)=0.002, F (36, 290)= 36.018. The effect of location on the parameters was not the same for the morning and evening sampling periods. The derivatization of active carbon into canister adsorbent for sampling VOCs was effective and opens a new strategy for improvised detection and measurement of VOCs in ambient air. This potential can be domesticated for mopping carcinogens from ambient air.

ACKNOWLEDGEMENTS

This research project was supported by the Institution Based Research Grant provided by Nigeria Tertiary Education Tax fund (TeTfund) Reference No.TETFUND/DESS/POLY/KADUNA/RP/VOL.VI. The authors are grateful to Edi-Jen Petrol pump station for granting permission to sample the air in the pump station.

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