Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria Pollutant Levels

*Yahaya Abbas Aliyu, Joel Ondego Botai, Aliyu Zailani Abubakar,Terwase Tosin Youngu, Jimoh Olanrewaju Sule, Mohammed Wachin Shebe and Mohammed Ahmed Bichi*

## **Abstract**

In Nigeria, the rising levels of used/poorly maintained vehicles are contributing to most urban air pollution with possible repercussion on the general public health. This study evaluates the inferences of vehicular traffic surge on outdoor pollutant measurement using Zaria, northern Nigeria, as a case study. The study collected a 1-year time-series dataset for the vehicular count and the respective outdoor criteria pollutant measurements over 19 study sites. The vehicular traffic was categorized into motorcycles (2-W), tricycles (3-W), cars, buses, light-duty vehicles (LDV) and heavy-duty vehicles (HDV). The outdoor pollutants that were measured include carbon monoxide (CO), sulfur dioxide (SO2) and particulate matter (PM2.5/PM10). We utilized validated portable monitors (CW-HAT200 particulate counter and the MSA Altair 5x multigas sensor) for the outdoor measurements during December 2015–November 2016. The observed measurements for the validation procedure were normally distributed [kurtosis (0.301); skewness (0.334)] and coefficient of determination (R2 ≥ 0.808). The time-series analysis of particulate matter (PM) measurements displayed alarming concentrations levels. Combined vehicular traffic density analysis revealed significant contribution (R ≥ 0.619) to the population exposed outdoor pollutant measurements. The 2-W (motorcycle) was found to be the vehicular category that attributed the most significant relationship with observed outdoor pollutant measurements.

**Keywords:** urban air quality, vehicular traffic, portable sensors, criteria pollutants, Zaria-Nigeria

### **1. Introduction**

In most developing countries, atmospheric pollution continues to affect exposed population health [1–3]. In Africa, air quality studies are devising alternative and reliable means to obtain pollutant measurements for research. The approach


#### **Table 1.**

*List of abbreviations and units.*

includes reliable validation of sampling techniques that contribute to the up-todate understanding of criteria pollutants, maintenance outflow and technical know-how [4].

Nigeria's rising population is escalating anthropogenic activities within its territory without any reliable information on its air quality [5]. The atmospheric air quality of most of its urban cities continues to remain exposed to the growing, poorly managed vehicular traffic from ineffective fuel combustion [6]. The situation is familiar, however, the motivation to address it lingers ambiguously.

environments [8]. This study employed the CW-HAT200 particulate counter and the MSA Altair 5x multi-gas sensor to collect particulate matter (PM2.5 and PM10) while the MSA Altair 5x collect carbon monoxide (CO) and sulfur dioxide (SO2) respectively. The instrument re-calibration was conducted using manufacturer's

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

Owing to the unavailability of real-time reference air pollution monitors within the study region, the devices were validated the portable pollutants monitors using the WHO air filter sampling model Eq. (1). To validate the portable devices, total suspended particulates (TSP) were collected at two distinct sample test stations at 1.5 m above the existing ground level. Validation site 1 had dense outdoor traffic activity, while validation site 2 had minimal outdoor traffic activity tagged control site. The validation samples and synchronized portable monitor measurements were obtained across three epochs, that are, morning, afternoon and evening for 17 days. TSP is described as particulate fraction ranging from 0.1 to about 100 μm in size (diameters). Particulates matter PM2.5 (diameter < 2.5 μm) and PM10 (diameter < 10 μm) fall within the specified range. Based on [9] which identified a significant relationship between total suspended particulates, PM10 and PM2.5 and [10] which reported that there is a significant correlation among pollutant emissions resulting from a common source, the study validated the portable devices using the WHO air sampling filter technique. Eq. (1) describes the WHO air sample

*total suspended particulates* µg m�<sup>3</sup> <sup>¼</sup> *MS* � *MO*

where *MO* is the filter paper mass without TSP samples, *MS* is the filter paper mass with TSP samples, *V* is the TSP volume. To determine the concentration

In line with Eq. (1), the validation samples were collected individually on filter papers and collocating pollutant measurements with the portable device over the

), model Eq. (1) was divided by the sample time (in hours).

*<sup>V</sup>* (1)

span calibration mixed gas specifications.

*The 19 study sites adopted for study data acquisition.*

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

model technique [11].

(μg m�<sup>3</sup>

**37**

**Figure 1.**

The rising levels of used/poorly managed vehicular operations remains an unnoticed contributor to urban atmospheric air pollution. While the literature has established alarming pollutant levels and how they contribute to the respiratory wellbeing of the exposed population [3], there is a need to further establish the relationship between the categories of the existing vehicular traffic surge and corresponding criteria pollutant levels observed. This will facilitate the process of the traffic-related atmospheric air pollution management plan in many Nigerian cities. For familiarity with the terminology, **Table 1** highlights a list of abbreviations and units utilized for this study.

### **2. Methodology**

#### **2.1 Study area**

Zaria metropolis described in **Figure 1**, has an estimated area of 296.04 km2 . Its estimated population as reported in 2014 is 938,521. The city climate characteristics are divided into two. The dry season ranges from October to May, and the rainy season ranges from June to September. The altitude is averagely 670 m above mean sea level [7]. Major road intersections are the concept adopted for the selection of the 19 study sites.

#### **2.2 Instrumentation and methods**

There is increasing use of portable devices for examining outdoor air (atmospheric) quality. With comparison to established reference devices, their reliability allows for effective real-time data acquisition, especially in limited resource

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

**Figure 1.** *The 19 study sites adopted for study data acquisition.*

environments [8]. This study employed the CW-HAT200 particulate counter and the MSA Altair 5x multi-gas sensor to collect particulate matter (PM2.5 and PM10) while the MSA Altair 5x collect carbon monoxide (CO) and sulfur dioxide (SO2) respectively. The instrument re-calibration was conducted using manufacturer's span calibration mixed gas specifications.

Owing to the unavailability of real-time reference air pollution monitors within the study region, the devices were validated the portable pollutants monitors using the WHO air filter sampling model Eq. (1). To validate the portable devices, total suspended particulates (TSP) were collected at two distinct sample test stations at 1.5 m above the existing ground level. Validation site 1 had dense outdoor traffic activity, while validation site 2 had minimal outdoor traffic activity tagged control site. The validation samples and synchronized portable monitor measurements were obtained across three epochs, that are, morning, afternoon and evening for 17 days. TSP is described as particulate fraction ranging from 0.1 to about 100 μm in size (diameters). Particulates matter PM2.5 (diameter < 2.5 μm) and PM10 (diameter < 10 μm) fall within the specified range. Based on [9] which identified a significant relationship between total suspended particulates, PM10 and PM2.5 and [10] which reported that there is a significant correlation among pollutant emissions resulting from a common source, the study validated the portable devices using the WHO air sampling filter technique. Eq. (1) describes the WHO air sample model technique [11].

$$\text{total supplied particles } \left(\text{\(\mu\text{g m}^{-3}\)} = \frac{M\_{\text{S}} - M\_{\text{O}}}{V} \right)$$

where *MO* is the filter paper mass without TSP samples, *MS* is the filter paper mass with TSP samples, *V* is the TSP volume. To determine the concentration (μg m�<sup>3</sup> ), model Eq. (1) was divided by the sample time (in hours).

In line with Eq. (1), the validation samples were collected individually on filter papers and collocating pollutant measurements with the portable device over the

includes reliable validation of sampling techniques that contribute to the up-todate understanding of criteria pollutants, maintenance outflow and technical

PM2.5 Particulate matter, with a diameter of <2.5 μm PM10 Particulate matter, with a diameter of <10 μm

μg m<sup>3</sup> Microgram per meter cube 2-W Two-wheeler (motorcycle) 3-W Three-wheeler (tricycle) CO Carbon monoxide HDV Heavy-duty vehicle LDV Light-duty vehicle

ppm Parts per million SO2 Sulfur dioxide

TSP Totally suspended particles

Nigeria's rising population is escalating anthropogenic activities within its territory without any reliable information on its air quality [5]. The atmospheric air quality of most of its urban cities continues to remain exposed to the growing, poorly managed vehicular traffic from ineffective fuel combustion [6]. The situation is familiar, however, the motivation to address it lingers ambiguously. The rising levels of used/poorly managed vehicular operations remains an unnoticed contributor to urban atmospheric air pollution. While the literature has established alarming pollutant levels and how they contribute to the respiratory wellbeing of the exposed population [3], there is a need to further establish the relationship between the categories of the existing vehicular traffic surge and corresponding criteria pollutant levels observed. This will facilitate the process of the traffic-related atmospheric air pollution management plan in many Nigerian cities. For familiarity with the terminology, **Table 1** highlights a list of abbreviations

Zaria metropolis described in **Figure 1**, has an estimated area of 296.04 km2

estimated population as reported in 2014 is 938,521. The city climate characteristics are divided into two. The dry season ranges from October to May, and the rainy season ranges from June to September. The altitude is averagely 670 m above mean sea level [7]. Major road intersections are the concept adopted for the selection of

There is increasing use of portable devices for examining outdoor air (atmospheric) quality. With comparison to established reference devices, their reliability allows for effective real-time data acquisition, especially in limited resource

. Its

know-how [4].

*List of abbreviations and units.*

*Atmospheric Air Pollution and Monitoring*

**Table 1.**

and units utilized for this study.

**2.2 Instrumentation and methods**

**2. Methodology**

the 19 study sites.

**36**

**2.1 Study area**

study duration. The particulate filter samples were processed in the laboratory to obtain their individual concentrations using Eq. (1). They were then compared with the separately recorded collocating pollutant measurements from the portable devices. The collocating measurements were then analyzed using linear regression and bias, for the validation of the portable monitors. The analysis is described in **Figures 2** and **3**. The observed measurements for the validation procedure were normal distributed [skewness (0.334); kurtosis (0.301)]. The study adopted two performance indicators for the purpose of validating the portable pollutant instrument. The performance indicators are The Bland-Altman agreement plot and the coefficient of determination (R2 ). The Bland-Altman plot evaluates the systematic bias between the two measurements techniques, while the coefficient of determination indicates how strongly related the pair(s) of variables are. The Bland-Altman agreement plot can be seen in **Figure 2**.

From **Figure 2**, it can be seen that there is no significant systematic difference in the measurements. Additionally, the coefficient of determination (R2 ) across the two test sites showed that the TSP measurements from the WHO model technique and criteria pollutant measurements from the MSA Altair 5x/CW-HAT200 devices were significantly correlated. The linear regression can be seen in **Figure 3**. **Figures 2** and **3** illustrate that the reliability of the portable pollutant monitors has been validated based on [9, 10].

#### **Figure 2.**

*Bland-Altman bias plot highlighting the agreement of observed validation measurements (PM2.5 and PM10) within the 95% confidence interval: (a) less densely populated site and (b) densely populated site.*

**Figure 3.**

**39**

*Scatter plots showing the linear regression and coefficient of determination between the TSP and the portable*

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

*monitor samples: (a) densely populated site and (b) control site.*

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

study duration. The particulate filter samples were processed in the laboratory to obtain their individual concentrations using Eq. (1). They were then compared with the separately recorded collocating pollutant measurements from the portable devices. The collocating measurements were then analyzed using linear regression and bias, for the validation of the portable monitors. The analysis is described in **Figures 2** and **3**. The observed measurements for the validation procedure were normal distributed [skewness (0.334); kurtosis (0.301)]. The study adopted two performance indicators for the purpose of validating the portable pollutant instrument. The performance indicators are The Bland-Altman agreement plot and the

bias between the two measurements techniques, while the coefficient of determination indicates how strongly related the pair(s) of variables are. The Bland-Altman

the measurements. Additionally, the coefficient of determination (R2

were significantly correlated. The linear regression can be seen in **Figure 3**. **Figures 2** and **3** illustrate that the reliability of the portable pollutant monitors has

From **Figure 2**, it can be seen that there is no significant systematic difference in

two test sites showed that the TSP measurements from the WHO model technique and criteria pollutant measurements from the MSA Altair 5x/CW-HAT200 devices

*Bland-Altman bias plot highlighting the agreement of observed validation measurements (PM2.5 and PM10) within the 95% confidence interval: (a) less densely populated site and (b) densely populated site.*

). The Bland-Altman plot evaluates the systematic

) across the

coefficient of determination (R2

*Atmospheric Air Pollution and Monitoring*

been validated based on [9, 10].

**Figure 2.**

**38**

agreement plot can be seen in **Figure 2**.

*Scatter plots showing the linear regression and coefficient of determination between the TSP and the portable monitor samples: (a) densely populated site and (b) control site.*



 *levels).*

With the above-described validation, the portable instruments were utilized to commence the measurement of ground level roadside pollution concentrations. The duration of the sampling measurement was from 01 December 2015 to 30 November 2016. The outdoor concentration levels were observed using the approach described in [12, 13]. The vehicular traffic count was also conducted to obtain the volume of vehicles contributing to the outdoor air pollution across the sampling sites. The vehicular count was obtained to determine the contributory level of vehicular density to outdoor air pollution. The vehicles are categorized as follows: motorcycles (2-W), tricycles (3-W), cars, buses, light-duty vehicles (LDV) and heavy-duty vehicles (HDV). The study analysis was performed using software:

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

**Table 2** highlights the dispersal relationship of the observed CO, SO2, PM2.5 and

PM10 across the 19 study sites. This was achieved using Pearson's correlation coefficient. The inter-study-site correlation matrix (**Table 2**), showed that the relationship of the measured pollutants was significant at the 0.01 level across all the study sites. And only study site 6 (a control site) revealed lower coefficient values in comparison to the remaining study sites. From **Table 2**, study sites 2 and 9

**Study site 2-W 3-W Car Bus LDV HDV** 16,034 17 3186 4 10,242 11 5613 6 958 1 1417 2 15,111 16 2955 4 8443 9 4971 6 643 1 1204 2 8554 8 888 1 3021 3 1177 1 2571 1 641 1 19,948 22 3731 4 11,785 12 7279 8 1561 2 3444 4 11,688 12 2063 2 2960 4 1418 2 340 1 571 1 5602 6 615 1 1585 2 542 1 241 1 412 1 18,012 18 3954 5 4045 5 6656 7 502 2 442 1 17,069 17 3556 4 5153 5 6353 7 428 1 211 1 27,008 27 5529 7 16,307 17 9352 10 1495 2 1628 2 14,870 15 3575 4 8628 9 3667 5 784 2 1320 1 14,453 16 4446 6 7089 8 2321 3 296 1 369 1 27,058 28 5720 6 15,746 17 9643 10 1436 2 929 2 22,012 23 4982 6 9559 10 8551 9 919 2 537 1 28,897 29 6205 7 5123 6 6797 8 897 2 500 1 17,482 18 3736 5 11,748 13 6761 7 1343 2 899 2 20,678 22 4672 6 22,656 24 13,050 14 2405 3 2666 4 11,167 12 2241 3 12,647 13 6447 8 1013 2 1131 2 6710 7 756 1 4194 5 2528 3 364 1 713 1 10,529 11 2048 3 9781 10 6063 7 872 2 896 2 **Total 312,882 64,858 170,712 109,189 19,068 19,930**

produced a perfect relationship with site 10 and site 13, respectively.

*Vehicular traffic density (total average per 3 min) across the 19 sampling sites in the study.*

SPSS, Microsoft Excel and MATLAB.

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

**3. Results and discussion**

**Table 3.**

**41**

**Table**

#### *Atmospheric Air Pollution and Monitoring*

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

With the above-described validation, the portable instruments were utilized to commence the measurement of ground level roadside pollution concentrations. The duration of the sampling measurement was from 01 December 2015 to 30 November 2016. The outdoor concentration levels were observed using the approach described in [12, 13]. The vehicular traffic count was also conducted to obtain the volume of vehicles contributing to the outdoor air pollution across the sampling sites. The vehicular count was obtained to determine the contributory level of vehicular density to outdoor air pollution. The vehicles are categorized as follows: motorcycles (2-W), tricycles (3-W), cars, buses, light-duty vehicles (LDV) and heavy-duty vehicles (HDV). The study analysis was performed using software: SPSS, Microsoft Excel and MATLAB.

## **3. Results and discussion**

**Table 2** highlights the dispersal relationship of the observed CO, SO2, PM2.5 and PM10 across the 19 study sites. This was achieved using Pearson's correlation coefficient. The inter-study-site correlation matrix (**Table 2**), showed that the relationship of the measured pollutants was significant at the 0.01 level across all the study sites. And only study site 6 (a control site) revealed lower coefficient values in comparison to the remaining study sites. From **Table 2**, study sites 2 and 9 produced a perfect relationship with site 10 and site 13, respectively.


**Table 3.**

*Vehicular traffic density (total average per 3 min) across the 19 sampling sites in the study.*

**Study sites**

**40**

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 **Table 2.** *Pearson's correlation*

 *coefficient matrix of seasonal pollutant* 

*measurement*

 *across the 19 study sites (significant*

 *at 0.01 levels).*

1

 0.907

1

 0.851

1

 0.971

1

 0.938

1

 0.820

1

 0.994

1

 0.991

1

 0.985

1

 0.991

1

 0.963

1

 0.999

1

 0.994

1

 0.993

1

 0.997

1

 0.979

1

 0.981

1

 0.978

1

 0.998

 0.931

 0.985

 0.988

 0.943

 0.991

 0.998

 0.965

 0.909

 0.974

 0.990

 0.996

 0.973

 0.933

 0.983

 0.996

 0.990

 0.997

 0.971

 0.928

 0.981

 0.968

 0.957

 0.980

 0.971

 0.995

 0.981

 0.992

 0.985

 0.988

 0.974

 0.987

 0.985

 0.993

 0.978

 0.997

 0.967

 0.999

**1.000**

0.997

 0.992

 0.998

 0.973

 0.928

 0.982

 0.999

 0.985

 0.992

 0.993

 0.982

 0.990

 0.990

 0.990

 0.968

 0.996

 0.999

 0.989

 0.975

 0.998

 0.998

 0.997

 0.996

 0.999

 0.980

 0.937

 0.987

 0.878

 0.815

 0.892

 0.881

 0.820

 0.829

 0.775

 0.808

 0.803

 0.873

 0.944

 0.875

 0.967

 0.989

 0.963

 0.993

 0.982

 0.964

 0.969

 0.944

 0.961

 0.959

 0.982

 0.988

 0.986

 0.828

 0.998

 0.994

 0.995

 0.992

 0.983

 0.993

 0.994

 0.993

 0.999

 0.998

 0.989

 0.950

 0.993

*Atmospheric Air Pollution and Monitoring*

 0.951

 0.997

 0.841

 0.895

 0.834

 0.910

 0.906

 0.837

 0.847

 0.797

 0.833

 0.826

 0.897

 0.961

 0.897

 0.993

 0.992

 0.888

 0.989

 0.998

 0.985

**1.000**

0.993

 0.984

 0.987

 0.974

 0.988

 0.985

 0.994

 0.977

 0.997

1 0.997

 0.878

 0.998

 0.983

 0.858

 0.997

 0.999

 0.995

 0.997

 0.986

 0.994

 0.996

 0.988

 0.994

 0.995

 0.989

 0.960

 0.995

 **s1**

 **s2**

 **s3**

 **s4**

 **s5**

 **s6**

 **s7**

 **s8**

 **s9**

 **s10**

 **s11**

 **s12**

 **s13**

 **s14**

 **s15**

 **s16**

 **s17**

 **s18**

 **s19**

The traffic count for the individual sampling site per epoch was computed based on the vehicular category, as shown in **Table 3**. In general, the study site with the highest weighted average of the criteria pollutants measured over the 19 study locations is study site 14. The reason for the high measurements is because the site is within the study area's main market (Sabon-Gari market) with the highest average count of 2-W and 3-W vehicle density (**Table 3**). The traffic volume was determined by direct counting the traffic during the daily sampling epoch for the study period (1 year).

600 μg m<sup>3</sup> for CO, SO2, PM2.5 and PM10 respectively. Except for PM during the Harmattan season which falls between sample days 1–91. For sites 9 and 15, the majority of the observed criteria pollutant measurements were above the earlier

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

**Table 4** presents the computed 1-year weighted average of the measured criteria pollutants concentrations across the 19 study sites [14]. Study sites 3 and 6 recorded the least pollutant measurements CO/SO2 and PM2.5/PM10. This could attribute to minimal population activities at the sites. The sites 3, 6 and 18 were actually selected to serve as control sites for the study. From **Table 4**, the weighted average of the observed criteria pollutants for the study area is deduced as CO (29.220 ppm), SO2

Additionally, the weighted average computed for the observed criteria pollutant was compared against the stipulated guidelines in the WHO air quality document [15]. The comparison revealed that the weighted average of criteria pollutants observed over the 19 study sites did exceed the WHO stipulated threshold (blue line across bar charts) for SO2, PM2.5 and PM10 in all the study sites, except for CO, whose weighted average stayed within the stipulated limits only in sites 3, 6 and 18.

Pearson's correlation matrix was utilized to investigate the seasonal level of association between measured criteria pollutants and traffic activities within the 19

1 11.080 7.695 Kofar Kibo 33.036 0.363 258.873 528.000

3 11.064 7.673 Madaci, Saye 7.994 0.159 117.177 232.246 4 11.054 7.682 Gwargwaje 29.703 0.351 250.294 509.957 5 11.044 7.701 Kofar Gayan 16.811 0.212 182.562 372.982 6 11.041 7.720 Kofar Kona 4.586 0.137 99.068 202.008 7 11.051 7.699 Zaria City market 38.281 0.383 276.448 561.482 8 11.066 7.706 Babban Dodo 27.242 0.290 220.292 448.332 9 11.081 7.710 Kofar Doka 46.844 0.449 312.469 631.429 10 11.074 7.725 Banzazzau 22.880 0.260 208.111 424.255

13 11.104 7.721 PZ 38.848 0.399 282.524 573.486

15 11.124 7.715 MTD 29.600 0.302 173.255 448.810 16 11.130 7.703 Kwangila bridge 50.130 0.465 282.891 576.923

19 11.159 7.651 Samaru market 19.652 0.244 185.319 369.965

**Site Latitude Longitude Description CO (ppm) SO2 (ppm) PM2.5 (μg m<sup>3</sup>**

Wusasa

Kaya

Wada

market

road

Dogo

*The 1-year weighted average of the observed pollutants (N = 19, 104).*

) and PM10 (451.958 μg m<sup>3</sup>

).

20.838 0.264 214.720 432.571

19.728 0.243 179.426 367.067

55.959 0.525 328.026 662.063

65.073 0.627 342.588 704.262

19.180 0.238 167.004 352.324

8.795 0.167 93.807 189.041

**) PM10 (μg m<sup>3</sup>**

**)**

described values.

(0.319 ppm), PM2.5 (219.729 μg m<sup>3</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

This is illustrated in **Figure 5**.

2 11.078 7.686 Danmagaji,

11 11.079 7.735 FCE/Ungwan

12 11.093 7.717 Agwaro, Tudun

14 11.113 7.730 Sabon Gari

17 11.139 7.686 Aviation by NITT

18 11.177 7.672 Basawa by Hayin

**Table 4.**

**43**

**Figure 4** displays the time-series plots of vehicular traffic count and resulting criteria pollutants measurements collected over selected study sites 3, 9 and 15. It can be observed that the study sites 3 which is a control site, did have the majority of its pollutant concentration levels below 40 ppm, 0.6 ppm, 300 μg m<sup>3</sup> and

#### **Figure 4.**

*Time-series of the weighted average for the vehicular traffic count against the measured criteria pollutants over randomly selected sites 3, 9, and 15 for the 366 days duration: (a) Study site 3, (b) Study site 9, and (c) Study site 15.*

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

600 μg m<sup>3</sup> for CO, SO2, PM2.5 and PM10 respectively. Except for PM during the Harmattan season which falls between sample days 1–91. For sites 9 and 15, the majority of the observed criteria pollutant measurements were above the earlier described values.

**Table 4** presents the computed 1-year weighted average of the measured criteria pollutants concentrations across the 19 study sites [14]. Study sites 3 and 6 recorded the least pollutant measurements CO/SO2 and PM2.5/PM10. This could attribute to minimal population activities at the sites. The sites 3, 6 and 18 were actually selected to serve as control sites for the study. From **Table 4**, the weighted average of the observed criteria pollutants for the study area is deduced as CO (29.220 ppm), SO2 (0.319 ppm), PM2.5 (219.729 μg m<sup>3</sup> ) and PM10 (451.958 μg m<sup>3</sup> ).

Additionally, the weighted average computed for the observed criteria pollutant was compared against the stipulated guidelines in the WHO air quality document [15]. The comparison revealed that the weighted average of criteria pollutants observed over the 19 study sites did exceed the WHO stipulated threshold (blue line across bar charts) for SO2, PM2.5 and PM10 in all the study sites, except for CO, whose weighted average stayed within the stipulated limits only in sites 3, 6 and 18. This is illustrated in **Figure 5**.

Pearson's correlation matrix was utilized to investigate the seasonal level of association between measured criteria pollutants and traffic activities within the 19


#### **Table 4.**

*The 1-year weighted average of the observed pollutants (N = 19, 104).*

The traffic count for the individual sampling site per epoch was computed based on the vehicular category, as shown in **Table 3**. In general, the study site with the highest weighted average of the criteria pollutants measured over the 19 study locations is study site 14. The reason for the high measurements is because the site is within the study area's main market (Sabon-Gari market) with the highest average count of 2-W and 3-W vehicle density (**Table 3**). The traffic volume was determined by direct counting the traffic during the daily sampling epoch for the study

**Figure 4** displays the time-series plots of vehicular traffic count and resulting criteria pollutants measurements collected over selected study sites 3, 9 and 15. It can be observed that the study sites 3 which is a control site, did have the majority of its pollutant concentration levels below 40 ppm, 0.6 ppm, 300 μg m<sup>3</sup> and

*Time-series of the weighted average for the vehicular traffic count against the measured criteria pollutants over randomly selected sites 3, 9, and 15 for the 366 days duration: (a) Study site 3, (b) Study site 9,*

period (1 year).

*Atmospheric Air Pollution and Monitoring*

**Figure 4.**

**42**

*and (c) Study site 15.*

#### **Figure 5.**

*The comparison of weighted criteria pollutants average: (a) CO; (b) SO2; (c) PM2.5; and (d) PM10 against the WHO air quality guidelines.*

**CO**

Pollutants

**45**

CO

DJF

MAM

JJA SON

> SO2

DJF MAM

JJA SON

> PM2.5

DJF MAM

JJA SON

> PM10

DJF MAM

JJA SON

Traffic count DJF MAM

JJA SON

> **Table 5.**

*Seasonal correlation*

 *of the measured pollutants*

 *against the traffic variables (significant*

 *at 0.01 levels).*

1 0.984 0.969 0.976 0.995 0.975 0.780 0.970 0.971 0.934 0.781 0.969 0.971 0.949 0.882 0.898 0.861 0.822 1 0.946 0.949 0.979 0.992 0.749 0.949 0.958 0.943 0.752 0.951 0.959 0.954 0.894 0.903 0.882 0.860

1 0.985 0.968 0.929 0.747 0.930 0.922 0.879 0.750 0.929 0.922 0.898 1 0.983 0.930 0.751 0.935 0.923 0.861 0.752 0.931 0.923 0.885 0.828 0.842 0.791 0.749

1 0.972 0.789 0.966 0.963 0.917 0.790 0.963 0.963 0.933 0.867 0.887 0.845 0.804 1 0.761 0.955 0.971 0.968 0.766 0.957 0.972 0.975 0.909 0.914 0.891 0.864

1 0.871 0.837 0.778 0.999 0.864 0.836 0.785 1 0.989 0.952 0.871 0.999 0.989 0.960 0.885 0.904 0.858 0.815 1 0.977 0.837 0.992 1.000 0.984 0.892 0.911 0.874 0.831

1 0.783 0.960 0.977 0.996 0.919 0.922 0.896 0.861

1 0.864 0.837 0.790 1 0.992 0.967 0.886 0.905 0.861 0.819 1 0.985 0.893 0.911 0.875 0.832 1 0.898 0.903 0.872 0.834 1 0.985 0.973 0.955

1 0.986 0.964 1 0.992

1

0.676 0.694 0.667 0.622

0.673 0.691 0.664 0.619

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

1 0.992 0.972 0.976 0.984 0.988 0.958 0.782 0.968 0.960 0.916 0.783 0.966 0.960 0.930 0.882 0.898 0.854 0.814

 1 0.983 0.975 0.940 0.962 0.957 0.963 0.927 0.778 0.951 0.941 0.900 0.779 0.948 0.941 0.910

 Seasons

 DJF MAM

 JJA SON

 DJF MAM

 JJA SON

 DJF MAM

 JJA SON

 DJF MAM

 JJA SON

 DJF MAM

0.870 0.876 0.837 0.790 0.816 0.837 0.781 0.737

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

 JJA SON

**SO2**

**PM2.5**

**PM10**

**Traffic count**


*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

> **Table 5.**

*Seasonal correlation of the measured pollutants against the traffic variables (significant at 0.01 levels).*

**Figure 5.**

**44**

*WHO air quality guidelines.*

*Atmospheric Air Pollution and Monitoring*

*The comparison of weighted criteria pollutants average: (a) CO; (b) SO2; (c) PM2.5; and (d) PM10 against the*


outdoor pollutant measurements. This is followed by the 3-W (tricycles) and buses. The findings of the study will assist Nigerian policymakers on decisive steps for

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria…*

This study is supported by a postgraduate bursary to the first author, from the University of Pretoria, South Africa and the Ahmadu Bello University,

Yahaya Abbas Aliyu1,3\*, Joel Ondego Botai1,2, Aliyu Zailani Abubakar3

3 Department of Geomatics, Ahmadu Bello University, Zaria, Nigeria

2 South African Weather Service, Pretoria, South Africa

\*Address all correspondence to: u15221408@tuks.co.za

provided the original work is properly cited.

, Jimoh Olanrewaju Sule<sup>3</sup>

1 Department of Geography, Geoinformatics and Meteorology, University of

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

,

, Mohammed Wachin Shebe<sup>3</sup> and

vehicular worthiness to urban air quality management.

*DOI: http://dx.doi.org/10.5772/intechopen.86554*

The authors declare no conflict of interest.

**Acknowledgements**

**Conflict of interest**

**Author details**

Terwase Tosin Youngu<sup>3</sup>

Pretoria, South Africa

**47**

Mohammed Ahmed Bichi<sup>3</sup>

Zaria, Nigeria.

*\* The gradient of the shaded cells highlights (in decreasing order) the ranking of correlation of the various categories of vehicles to the observed criteria pollutants.*

#### **Table 6.**

*Statistical correlation between vehicular categories collated at the study sites against the criteria pollutant measurements.*

sampling locations. The data capture period was categorized into seasons that include December-January-February (DJF); March-April-May (MAM); June-July-August (JJA) and September-October-November (SON). This aims to appraise the environmental implication of road traffic movement to outdoor air pollution in Zaria across the seasons. From **Table 5**, it can be observed that all the measured variables were correlated positively at 0.01 p-levels. The analysis also indicates that the traffic activities (that is, the vehicular counts at the time of criteria pollutant observations) contributed significantly to observed criteria pollutants concentration levels except for the December-January-February (DJF) season. The DJF season (**Table 5**, red text) recorded lower correlation coefficients compared to the remaining seasons. The lower Pearson's coefficients during the DJF season can be attributed to the Harmattan and the holiday season within the study area. The Harmattan season is characterized by natural dusty-windy conditions and low temperatures, while the holiday season attributed to the lesser than usual traffic activities within the study area. From **Table 5**, this study concludes that emissions from vehicular activities are significantly responsible for measured pollutants observations in this study.

The contribution of traffic variables to the outdoor air pollution level is further evaluated with the consideration of the various vehicle categories (2-W, 3-W, cars, buses, LDV and HDV). **Table 6** described the contributory relationship between the observed criteria pollutants and the vehicular category. From **Table 6**, it can be observed that 2-W (motorcycles) counts showed the strongest relationship with the individual criteria pollutants measured, this followed by the 3-W (tricycles) and then buses. These findings confirmed the theory of the terrible state of these categories of the vehicle in the study.

## **4. Conclusions**

Urban air quality management remains a continuous task for Nigerian policymakers. This study assessed the implication of varying categories of vehicular traffic on outdoor air pollution over a developing Nigeria city. This was achieved through day-time primary data capture of vehicular traffic and corresponding criteria pollutant measurements over a period of 1 year (December 2015–November 2016). The result of the criteria pollutant measurements was alarmingly high as confirmed by similar studies. Furthermore, the study concluded that the combined vehicular traffic did contribute significantly (R ≥ 0.619) to the observed pollutant measurements all through the study. The 2-W (motorcycle) was found to be the vehicular category that attributed the most significant relationship with observed

*Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria… DOI: http://dx.doi.org/10.5772/intechopen.86554*

outdoor pollutant measurements. This is followed by the 3-W (tricycles) and buses. The findings of the study will assist Nigerian policymakers on decisive steps for vehicular worthiness to urban air quality management.

## **Acknowledgements**

This study is supported by a postgraduate bursary to the first author, from the University of Pretoria, South Africa and the Ahmadu Bello University, Zaria, Nigeria.

## **Conflict of interest**

sampling locations. The data capture period was categorized into seasons that include December-January-February (DJF); March-April-May (MAM); June-July-August (JJA) and September-October-November (SON). This aims to appraise the environmental implication of road traffic movement to outdoor air pollution in Zaria across the seasons. From **Table 5**, it can be observed that all the measured variables were correlated positively at 0.01 p-levels. The analysis also indicates that the traffic activities (that is, the vehicular counts at the time of criteria pollutant observations) contributed significantly to observed criteria pollutants concentration levels except for the December-January-February (DJF) season. The DJF season (**Table 5**, red text) recorded lower correlation coefficients compared to the remaining seasons. The lower Pearson's coefficients during the DJF season can be attributed to the Harmattan and the holiday season within the study area. The Harmattan season is characterized by natural dusty-windy conditions and low temperatures, while the holiday season attributed to the lesser than usual traffic activities within the study area. From **Table 5**, this study concludes that emissions from vehicular activities are significantly responsible for measured pollutants observa-

*Statistical correlation between vehicular categories collated at the study sites against the criteria pollutant*

**2-W 3-W Cars Buses LDV HDV**

CO 0.865\* 0.793\* 0.523 0.665\* 0.542 0.433 SO2 0.710\* 0.694 0.422 0.587\* 0.458 0.352 PM2.5 0.763\* 0.719\* 0.465 0.593\* 0.461 0.361 PM10 0.766\* 0.720\* 0.468 0.600\* 0.462 0.359

*The gradient of the shaded cells highlights (in decreasing order) the ranking of correlation of the various categories of*

The contribution of traffic variables to the outdoor air pollution level is further evaluated with the consideration of the various vehicle categories (2-W, 3-W, cars, buses, LDV and HDV). **Table 6** described the contributory relationship between the observed criteria pollutants and the vehicular category. From **Table 6**, it can be observed that 2-W (motorcycles) counts showed the strongest relationship with the individual criteria pollutants measured, this followed by the 3-W (tricycles) and then buses. These findings confirmed the theory of the terrible state of these

Urban air quality management remains a continuous task for Nigerian policymakers. This study assessed the implication of varying categories of vehicular traffic on outdoor air pollution over a developing Nigeria city. This was achieved through day-time primary data capture of vehicular traffic and corresponding criteria pollutant measurements over a period of 1 year (December 2015–November 2016). The result of the criteria pollutant measurements was alarmingly high as confirmed by similar studies. Furthermore, the study concluded that the combined vehicular traffic did contribute significantly (R ≥ 0.619) to the observed pollutant measurements all through the study. The 2-W (motorcycle) was found to be the vehicular category that attributed the most significant relationship with observed

tions in this study.

*\**

**Table 6.**

*measurements.*

*vehicles to the observed criteria pollutants.*

*Atmospheric Air Pollution and Monitoring*

**4. Conclusions**

**46**

categories of the vehicle in the study.

The authors declare no conflict of interest.

## **Author details**

Yahaya Abbas Aliyu1,3\*, Joel Ondego Botai1,2, Aliyu Zailani Abubakar3 , Terwase Tosin Youngu<sup>3</sup> , Jimoh Olanrewaju Sule<sup>3</sup> , Mohammed Wachin Shebe<sup>3</sup> and Mohammed Ahmed Bichi<sup>3</sup>

1 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, South Africa

2 South African Weather Service, Pretoria, South Africa

3 Department of Geomatics, Ahmadu Bello University, Zaria, Nigeria

\*Address all correspondence to: u15221408@tuks.co.za

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Patton AP, Laumbach R, Ohman-Strickland P, Black K, Alimokhtari S, Lioy PJ, et al. Scripted drives: A robust protocol for generating exposures to traffic-related air pollution. Atmospheric Environment. 2016;**143**: 290-299

[2] Gorai AK, Tchounwou PB, Mitra G. Spatial variation of ground-level ozone concentrations and its health impacts in an urban area in India. Aerosol and Air Quality Research. 2017;**17**(4):951-964

[3] Aliyu YA, Botai JO. An exposure appraisal of outdoor air pollution on the respiratory well-being of a developing city population. Journal of Epidemiology and Global Health. 2018; **8**(1):91-100

[4] Al-Awadi LT, Popov V, Khan AR. Seasonal effects of major primary pollutants in Ali Sabah Al-Salem residential area in Kuwait. International Journal of Environmental Technology and Management. 2015;**18**(1):54-82

[5] Marais EA, Jacob DJ, Wecht K, Lerot C, Zhang L, Yu K, et al. Anthropogenic emissions in Nigeria and implications for atmospheric ozone pollution: A view from space. Atmospheric Environment. 2014;**99**:32-40

[6] Aliyu YA, Musa IJ, Jeb DN. Geostatistics of pollutant gases along high traffic points in urban Zaria, Nigeria. International Journal of Geomatics and Geosciences. 2014;**5**(1): 19-31

[7] Aliyu YA, Botai JO. Reviewing the local and global implications of air pollution trend in Zaria, northern Nigeria. Urban Climate. 2018;**26**:51-59

[8] Snyder EG, Watkins TH, Solomon PA, Thoma ED, Williams RW, Hagler GSW, et al. The changing paradigm of air pollution monitoring. Environmental Science & Technology. 2013;**47**: 11369-11377

[9] Brook JR, Dann TF, Burnett RT. The relationship among TSP, PM10, PM2.5, and inorganic constituents of atmospheric participate matter at multiple Canadian locations. Journal of the Air & Waste Management Association. 1997;**47**(1):2-19

[10] Guo H, Wang Y, Zhang H. Characterization of criteria air pollutants in Beijing during 2014–2015. Environmental Research. 2017;**154**: 334-344

[11] Efe SI, Efe AT. Spatial distribution of particulate matter (PM10) in Warri metropolis, Nigeria. The Environmentalist. 2008;**28**(4):385-394

[12] Yazdi MN, Delavarrafiee M, Arhami M. Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran. Science of the Total Environment. 2015; **538**:375-384

[13] Aliyu YA, Botai JO. Appraising cityscale pollution monitoring capabilities of multi-satellite datasets using portable pollutant monitors. Atmospheric Environment. 2018;**179**:239-249

[14] Llanes S. How to calculate timeweighted average (TWA). In: 26th Annual California Industrial Hygiene Council (CIHC) Conference; 2016; San Diego, USA. Available from: http:// www.thecohengroup.com/article/calc ulate-time-weighted-average-twa/ [Accessed: 07 October 2017]

[15] WHO. Evolution of WHO Air Quality Guidelines. Past, Present and Future. World Health Organization; 2017. Available from: http://www.euro. who.int/\_\_data/assets/pdf\_file/0019/ 331660/Evolution-air-quality.pdf [Accessed: 26 September 2017]

**49**

biological studies.

contaminations

**1. Introduction**

**Chapter 5**

City Areas

*and Chavdar Ghelev*

**Abstract**

Long-Distance LIDAR Mapping

Bioaerosol Pollution over Large

*Zara Cherkezova-Zheleva, Ivan Grigorov, Georgy Kolarov,* 

Light detection and ranging (LIDAR) atmospheric sensing is a major tool for remote monitoring of aerosol pollution and its propagation in the atmosphere. Combining LIDAR sensing with ground-based aerosol monitoring can form the basis of integrated air-quality characterization. When present, biological atmospheric contamination is transported by aerosol particles of different size known as bioaerosol, whose monitoring is now among the basic areas of atmospheric research, especially in densely-populated large urban regions, where many bioaerosol-emitting sources exist. Thus, promptly identifying the bioaerosol sources, including their geographical coordinates, intensities, space-time distributions, etc., becomes a major task of a city monitoring system. This chapter argues in favor of integrating a LIDAR mapping schematic with in situ sampling and characterization of the bioaerosol in the urban area. The measurements, data processing, and decision-making aimed at preventing further atmospheric contamination should be performed in a near-real-time mode, which imposes certain demands on the typical LIDAR schematics, including long-range sensing as a critical parameter, especially over large areas (10 – 100 km2). In this chapter, we describe experiments using a LIDAR schematic allowing near-real-time long-distance measurements of urban bioaerosol combined with its ground-based sampling and physicochemical and

**Keywords:** LIDAR monitoring , particulate matter, atmospheric pollution,

Atmospheric aerosol pollution or more appropriately particulate matter (PM)

is key subject for the human health and ecosystem stability. At present, more than 2000 papers are published per year addressing research topics related to

*Dimitar Stoyanov, Ivan Nedkov, Veneta Groudeva,* 

*Mihail Iliev, Ralitsa Ilieva, Daniela Paneva* 

Schematic for Fast Monitoring of

## **Chapter 5**

**References**

290-299

[1] Patton AP, Laumbach R, Ohman-Strickland P, Black K, Alimokhtari S, Lioy PJ, et al. Scripted drives: A robust protocol for generating exposures to

*Atmospheric Air Pollution and Monitoring*

Science & Technology. 2013;**47**:

and inorganic constituents of atmospheric participate matter at multiple Canadian locations. Journal of

the Air & Waste Management Association. 1997;**47**(1):2-19

[10] Guo H, Wang Y, Zhang H. Characterization of criteria air

metropolis, Nigeria. The

pollutants in Beijing during 2014–2015. Environmental Research. 2017;**154**:

[11] Efe SI, Efe AT. Spatial distribution of particulate matter (PM10) in Warri

Environmentalist. 2008;**28**(4):385-394

[12] Yazdi MN, Delavarrafiee M, Arhami

pollutant levels and estimating emission factors: Case study of Tehran, Iran. Science of the Total Environment. 2015;

[13] Aliyu YA, Botai JO. Appraising cityscale pollution monitoring capabilities of multi-satellite datasets using portable pollutant monitors. Atmospheric Environment. 2018;**179**:239-249

[14] Llanes S. How to calculate timeweighted average (TWA). In: 26th Annual California Industrial Hygiene Council (CIHC) Conference; 2016; San Diego, USA. Available from: http:// www.thecohengroup.com/article/calc ulate-time-weighted-average-twa/ [Accessed: 07 October 2017]

[15] WHO. Evolution of WHO Air Quality Guidelines. Past, Present and Future. World Health Organization; 2017. Available from: http://www.euro. who.int/\_\_data/assets/pdf\_file/0019/ 331660/Evolution-air-quality.pdf [Accessed: 26 September 2017]

M. Evaluating near highway air

[9] Brook JR, Dann TF, Burnett RT. The relationship among TSP, PM10, PM2.5,

11369-11377

334-344

**538**:375-384

Atmospheric Environment. 2016;**143**:

[2] Gorai AK, Tchounwou PB, Mitra G. Spatial variation of ground-level ozone concentrations and its health impacts in an urban area in India. Aerosol and Air Quality Research. 2017;**17**(4):951-964

[3] Aliyu YA, Botai JO. An exposure appraisal of outdoor air pollution on the respiratory well-being of a developing

Epidemiology and Global Health. 2018;

[4] Al-Awadi LT, Popov V, Khan AR. Seasonal effects of major primary pollutants in Ali Sabah Al-Salem

residential area in Kuwait. International Journal of Environmental Technology and Management. 2015;**18**(1):54-82

[5] Marais EA, Jacob DJ, Wecht K, Lerot C, Zhang L, Yu K, et al. Anthropogenic emissions in Nigeria and implications for atmospheric ozone pollution: A view from space. Atmospheric Environment.

[6] Aliyu YA, Musa IJ, Jeb DN. Geostatistics of pollutant gases along high traffic points in urban Zaria, Nigeria. International Journal of Geomatics and Geosciences. 2014;**5**(1):

[7] Aliyu YA, Botai JO. Reviewing the local and global implications of air pollution trend in Zaria, northern Nigeria. Urban Climate. 2018;**26**:51-59

[8] Snyder EG, Watkins TH, Solomon PA, Thoma ED, Williams RW, Hagler GSW, et al. The changing paradigm of air pollution monitoring. Environmental

city population. Journal of

**8**(1):91-100

2014;**99**:32-40

19-31

**48**

traffic-related air pollution.
