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Ambient Air Quality Assessment of Orlu, Southeastern, Nigeria



Ibe Francis Chizoruo, Opara Alexander Iheanyichukwu, Njoku Pascal Chukwuemeka and Alinnor Jude Ikechukwu
 
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ABSTRACT

Background and Objective: Ambient air quality assessment of Orlu was carried out with reference to four criteria air pollutants which include particulate matter (PM10 ), nitrogen dioxide (NO2 ), sulfur (iv) oxide (SO2 ) and carbon monoxide (CO). The objective of the study was to determine the atmospheric concentration and to further assess the air quality level of Orlu. Methodology: Five locations were studied within the months of November, 2014-February, 2015 using mobile air quality monitoring devices. The sampling was carried out once a week in each of the five air monitoring locations, 3 times/day (morning, afternoon and evening) and 4 times a month for a period of 3 months. The measured air quality data were analyzed using one way ANOVA (p<0.05) while its spatial distribution was studied using the Box and Whiskers plots. Similarly, the influence of wind speed and wind direction on atmospheric dynamics was assessed with the aid of wind rose diagrams while air quality condition was determined by using air quality index technique (AQI). Results: The result of the study showed that the mean concentration of the air pollutants ranged as follows: PM10 (3.40-11.53) mg m–3, NO2 (0.20-0.70) ppm, SO2 (0.17-0.75) ppm and CO (26.00-51.00) ppm. The observed variations of mean levels of the atmospheric pollutants are in the order: Umuna junction>Banana junction>Umuaka>Ogboko junction>Umuago Urualla. The mean level of PM10, NO2 and CO in all the air quality monitoring locations exceeded the US NAAQS (US National Ambient Air Quality Standard) and Nigerian National ambient Air Quality Standards except the NO2 concentration at Umuago Urualla, while SO2 level was within Nigerian NAAQS limit but above US NAAQS. ANOVA (p<0.05) analysis revealed no significant difference in the mean concentrations of the measured air pollutants except NO2 at Banana junction, PM10 at Umuaka junction and Ogboko junction. Results of the AQI analysis ranged from 151-225 which implies unhealthy and very unhealthy atmosphere. The wind rose diagrams revealed that the wind speed and wind direction contributed significantly to the dispersion and transportation of the atmospheric pollutants. Conclusion: The findings of the present study suggests that anthropogenic activities in the area and environs are responsible for the observed air quality levels. Strict monitoring of the atmospheric conditions of the study area is, therefore, recommended in view of the adverse health implications.

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Ibe Francis Chizoruo, Opara Alexander Iheanyichukwu, Njoku Pascal Chukwuemeka and Alinnor Jude Ikechukwu, 2017. Ambient Air Quality Assessment of Orlu, Southeastern, Nigeria. Journal of Applied Sciences, 17: 441-457.

DOI: 10.3923/jas.2017.441.457

URL: https://scialert.net/abstract/?doi=jas.2017.441.457
 
Received: September 10, 2017; Accepted: October 30, 2017; Published: November 29, 2017


Copyright: © 2017. This is an open access article distributed under the terms of the creative commons attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

INTRODUCTION

One of the major challenging environmental problems that has bedeviled both the developed and developing countries of the world today is air pollution which has recently been linked to increased morbidity and mortality rates1,2. Atmospheric pollution is a condition in which certain substances, which include gases (sulphur dioxide, nitrogen oxides, carbon monoxides, hydrocarbons, etc.), particulate matters (smoke, dust, fumes, aerosols, etc.), radioactive materials and many others are present in such concentrations that may produce undesirable effects on man and ecosystem3. Human exposure to air pollutants is unavoidable in today’s perspective especially in the urban areas of most developing countries4. Though, air pollution could be due to natural sources, a major anthropogenic source of air pollution is due to man’s quest for a better standard of living and the utilization of natural resources for rapid industrialization, urbanization and consequently causing excessive air pollution5. Therefore, air pollution problems have continued to receive a great deal of interest worldwide due to its negative impacts on human health and welfare6-9. Among the reported cases of extreme air pollution conditions that affects humanity include the issues of high blood pressure and other cardiovascular problems10,11. Air pollution, therefore, is a serious threat to environmental health in many cities of the world today12 -14. It is very pertinent to note that this condition is not unconnected to the fact that one of the basic requirements of human health and existence is clean air15,16.

The level of air pollutant concentration depend not only on the quantities that are emitted from air pollution sources but also on the ability of the atmosphere to either absorb or disperse these emissions17. This is hinged on space variation of sources as well as atmospheric gradients which most often results in the diffusion and transportation of the pollutants to areas outside the source of the air pollution18. Atmospheric dynamics which are generally controlled by meteorological factors (including temperature, humidity, wind speed and direction, etc) remarkably influence the tendency for the release of atmospheric toxins to the environment19. In view of this, therefore, atmospheric pollutant conditions most often are subjected to spatio-temporal variations causing the air pollution pattern to change with space and time due to changes in meteorological and topographical conditions17.

Air quality assessment and monitoring is, therefore, very important in determining the nature of population exposure to atmospheric pollutants which may result in a variety of health effects. These health effects generally depend on the type of pollutant, its magnitude, duration and frequency of exposure and of course the toxicity of the pollutant20. The way people live and breathe could be affected by the air quality of a particular locality and air quality like weather normally changes from time to time. Report of the air quality index of a locality is therefore important in ambient air quality assessment and monitoring. Air quality index (AQI) rating therefore, may be useful in understanding the atmospheric concentration levels of a locality since it helps in the classification of the health conditions inherent in human exposure to air pollution21. There is, therefore, the urgent need of assessing the air quality condition within Orlu metropolis owing to increase in population, industrialization and urbanization levels of the area.

The objective of this study was to assess the air pollution level of Orlu and environs in Imo State, Southeastern Nigeria within the geographical locations geo-referenced with the GARMIN GPSmap76 equipment as shown in Table 1. The acquisition of ambient air quality data in the study area has become necessary due to the paucity of data on atmospheric pollutant concentrations. Nigeria like most developing countries lack the capacity for continuous air pollution monitoring. Therefore, there is need for acquisition of air pollution data at regular intervals in the study area to ascertain the air quality conditions. In this regard, therefore, air pollution level of the study area was determined with emphasis to particulate matter (PM10), nitrogen (iv) oxide (NO2 ), sulphur (iv) oxide (SO2) and carbon monoxide (CO). Similarly, since atmospheric pollutant concentrations could be influenced by meteorological factors, therefore, meteorological variables (wind speed and wind directions) were also measured in order to assess the dynamics of atmospheric dispersion of the air pollutants in the study locations. The air quality data measured were analyzed with reference to the specified threshold limits prescribed by the Nigerian National Ambient Air Quality Standards (Nigerian NAAQS)22 and the United States National Ambient Air Quality Standards (US NAAQS)23.

MATERIALS AND METHODS

Study area: The study was carried out in Orlu area of Imo State, Nigeria between the months of November, 2014-February, 2015. The need for this research was necessitated by the fact that Orlu is very close to Ohaji/Egbema/Oguta area of Imo State known for crude oil related activities and gas flaring24. This is also in addition to the glaring fact that Orlu is undergoing a rapid population, industrialization and urbanization growth25. The increasing level of urbanization in the area is associated with a lot of commercial and industrial activities including the use of power generators and high volume of vehicular traffic.

Table 1:Air quality monitoring locations of Orlu and environs, Southeastern Nigeria
Image for - Ambient Air Quality Assessment of Orlu, Southeastern, Nigeria

There is also the prevalence of two stroke engine automobiles (mainly motorcycles and tricycles) used mainly for transportation in the study area. These two stroke engines are well known for incomplete combustion of fossil fuels which generally leads to the emission of noxious atmospheric air pollutants17,26. Orlu is located by the geographical coordinate given by latitudes 5°40'15.50"-5°51'09.36"N and longitudes 7°00'59.92"-7°04'51.68"E and is within the tropical rainforest region with two distinct seasons which include wet and dry seasons. It has an annual rainfall of about 1700-2500 mm, which is observed almost entirely within the months of March and October. Average relative humidity is about 80% with up to 90% occurring during the rainy season. The mean daily maximum air temperatures ranges from about 28-35°C, while the mean daily air temperature minimum ranges from about 19-24°C27. The air quality sampling was carried out at five different locations within Orlu area as shown in Table 1. A measuring location sited at Umuago Urualla was chosen to serve as the control during the field study.

Air quality sampling procedure: The air pollutants SO2, CO, NO2 and PM10, commonly used in air quality index (AQI) assessment were sampled 3 times a day (morning, afternoon and evening)15,28. The SO2, CO and NO2 were determined with the specific Gasman air monitor, Crowcon Instruments Ltd., England, while PM10 was measured with Haze-dust particulate monitor 10 μm, model HD1000, Environmental Device Corporation, USA. The meteorological parameters which include wind speed and wind direction were measured with a Multifunctional Microprocessor digital meter Anemometer, Model Am-4836C, China. The geographical coordinates and elevation were determined with the aid of the Garmin, GPSmap 76. Sampling was carried out during dry season within the months of November, 2014-February, 2015. This involved 3 months of air quality monitoring for a period of 12 weeks. The sampling was carried out once a week in each of the five air monitoring stations, 3 times/day (morning, afternoon and evening) and 4 times a month for a period of 3 months.

Method of data interpretation: Data was analyzed using geospatial and geostatistical techniques with the mean values of the air pollutant concentrations estimated for measurements made in the morning, afternoon and evening hours. The standard deviation (SD) and variance were determined while the estimated co-efficient of variation (CV%) was used to assess the variation in the concentration levels of the air pollutant monitored using Eq. 1:

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(1)

Where:
CV (%) = Coefficient of variation in percentage
SD = Standard deviation

Variation in the concentration levels was categorized as little variation (CV %<20), moderate variation (CV % = 20-50) and high variation (CV %>50)29. Box and Whiskers plots and one way ANOVA (p<0.05) were carried out with the help of Matlab software version 7.9, while Grapher 10 was used to develop the dominant wind dynamics (wind rose). Sim-air quality software was used to calculate the air quality index of the air pollutants using Eq. 2:

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(2)

Where:
Ip = Index for pollutant
Cp = Rounded concentration of pollutant p
BPHi = Breakpoint that is>Cp
BPLo = Breakpoint that is<Cp
IHi = AQI value corresponding to BPHi
ILo = AQI value corresponding to BPLo

RESULTS AND DISCUSSION

Variation of atmospheric pollutant concentration: The mobile air monitoring devices were deployed at five different locations, out of which one location at Umuago Urualla was chosen as the control for the field acquisition. It was observed that the lowest concentration of the air pollutants were recorded at this location. Elevated concentration of the air pollutants were observed in most of the areas with higher commercial activities as indicated in Table 1. The observed level of the ambient air pollutants at the locations may be attributed to the influence of meteorological factors such as wind speed and wind direction in addition to anthropogenic activities. The summary of air pollutant concentration levels in Orlu is presented in Table 2. The result revealed that higher mean level of NO2 were observed in the afternoon than in the morning and evening. Table 2 also shows that the mean level of SO2 recorded in the afternoon was higher when compared with values recorded in the morning and evening. In the case of PM10, the highest mean concentrations were also observed in the afternoon. Though elevated mean levels of CO was obtained in the afternoon, the maximum value was observed in the evening while the minimum value was observed in the morning.

Figure 1a-d are the results of air quality data obtained from the 5 locations studied within Orlu and environs, indicating the concentration levels of the atmospheric pollutants within the 12 weeks of air quality study. In the case of PM10, the result as shown in Fig. 1a ranged from 3.40-11.53 mg m–3 with higher levels of PM10 recorded at all the air sampling sites when compared with the control. Highest PM10 concentration level was recorded at Banana and Umuna junctions. This may not be unconnected with the level of commercial activities and traffic situation at the locations. The concentration levels of PM10 recorded across the study area is in the increasing order of Umuna junction>Banana junction>Umuaka junction>Ogboko>Umuago Urualla.

Figure 1b shows the level of NO2 values observed at the locations which revealed that the values ranged from 0.20-0.70 ppm. However, higher values of NO2 was observed at Umuna junction when compared with other sites including the control station. The values observed are in the order: Umuna junction>Banana junction>Umuaka>Ogboko junction>Umuago Urualla.

Similarly, Fig. 1c shows the level of SO2 at the locations sampled with the values ranging from 0.17-0.75 ppm. Elevated values of SO2 were observed at Umuna junction. The observed values are in the increasing order of Umuna junction>Banana junction>Umuaka>Ogboko junction>Umuago Urualla. The observed elevated levels of NO2 and SO2 at some stations could be attributed to higher vehicular traffic, presence of three stroke engine tricycles and higher commercial activities including the use of power generating sets30.

The result of CO levels across the study area is shown in Fig. 1d. The result revealed that the values ranged from 26.00-51.00 ppm across the study area. It was observed that the CO levels were high in all the locations including the control. This calls for attention and proper monitoring on a regular basis. The recorded high values is believed to be a reflection of the activities going on in the area including high volume of traffic, numerous commercial activities, rampant use of power generating sets and other domestic activities including combustion of biomass17,31. The observed values were also noticed to have the same trend and order as PM10, NO2 and SO2 values earlier discussed. The observed elevated concentration of the studied air pollutants in the afternoon is an indication that the sources of these pollutants are due to increased commercial activities during working hours. The mean level of NO2 and SO2 observed in this study are within the annual and 24 h stipulated values, while the mean level of PM10 and CO exceeded both the annual and 24 h limits recommended by NAAQS and US NAAQS.

Table 2:Summary of air pollutant concentration level
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M: Morning, A: Afternoon, E: Evening, Min: Minimum value, Max: Maximum value, SD: Standard deviation, Var: Variance and CV: Coefficient of variation

Statistical analysis of the air pollutant concentration: Variation of the air pollutants in the morning, afternoon and evening as shown in Table 2 revealed that for NO2, the co-efficient of variation is in the order: A>E>M. The highest variation (12%) was observed in the afternoon.

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Image for - Ambient Air Quality Assessment of Orlu, Southeastern, Nigeria
Fig. 1(a-d):
Variation of weekly PM10 concentration levels at different monitoring locations, (b) Variation of weekly concentration levels of NO2 at different monitoring locations, (c) Variation of weekly levels of SO2 at different monitoring locations and (d) Variation of weekly levels of CO at different monitoring locations

The order of the variation of SO2 is also is given as A>E>M, while the order of variation of PM10 is A>M>E. Similarly, Table 2 revealed that for CO, the co-efficient of variation is in the order A>E>M.

The distribution of the concentrations of the air pollutants in the study locations are represented with Box and Whisker plots as shown in Fig. 2-6. In Fig. 2a at Umuago Urualla, the plot revealed that in the morning, afternoon and evening, 25% CO concentration recorded is within 29-32 ppm while 75% of the value is within 33-37 ppm. ANOVA result (F = 2.7, sig. value = 0.0823, p<0.05) shows that there is no significant difference in the mean level of CO. Figure 2b shows that for SO2, 25 and 75% of the values observed in the morning, afternoon and evening are within 0.15-0.17 and 0.22-0.24 ppm, respectively. ANOVA result (F = 0.26, Sig. value = 0.78, p<0.05) indicates that there is no significant difference in the mean value of SO2 recorded. In Fig. 2c, the box and whisker plot shows that 25 and 75% concentration of NO2 are respectively within 0.18-0.21 and 0.25-0.28 ppm, respectively in the morning, afternoon and evening. ANOVA result (F = 0.04, Sig. value = 0.96, p<0.05) reveals no significant difference in the mean value of NO2. Similarly, Fig. 2d shows that in the morning, afternoon and evening, 25 and 75% values of PM10 recorded are within 2.6-3.2 and 4.8-5.2 mg m–3, respectively. The result of the ANOVA interpretation (F = 0.09, sig. value 0.92, p<0.05) revealed no significant difference in the mean level of PM10 at Umuago Urualla.

At Ogboko junction, it was clearly shown by Fig. 3a that for CO, 25 and 75% of the values recorded in the morning, afternoon and evening are within 34-37 and 40-47 ppm, respectively. Results of the ANOVA analysis (F = 1.62, Sig. value = 0.214, p<0.05) showed that the difference in the average value of CO do not differ significantly. Figure 3b revealed that 25 and 75% of the observed values of SO2 lie, respectively within 0.30-0.32 and 0.40-0.53 ppm in the morning, afternoon and evening. Similarly, results of the ANOVA analysis (F = 0.74, sig. value = 0.49, p<0.05) revealed that there is no significant difference in the SO2 mean concentration. Figure 3c indicated that 25 and 75% concentration of NO2 recorded are respectively within 0.13-0.34 ppm and 0.43 and 0.48 ppm in the morning, afternoon and evening with the result of the ANOVA analysis (F = 0.22, sig. value 0.80, p<0.05) showing that the p-value is less than the significant value meaning that there is no significant statistical difference in the mean concentration of NO2 observed. Figure 3d showed that the values, 4.50-5.90 and 6.10-8.80 mg m–3 are, respectively the 25 and 75% of the PM10 values observed in the morning, afternoon and evening. On the other hand ANOVA result (F = 3.92, sig. value = 0.03, p<0.05) shows that there is a statistical significant difference in the mean concentration of PM10 level observed in this location in the morning, afternoon and evening. This could be attributed to the influence of meteorological parameters, seasonal and spatial variations.

For Umuna junction, the Box and Whiskers plots shown in Fig. 4a revealed that 25 and 75% of the values of CO recorded are respectively within 43.00-46.50 and 48.00-50.50 ppm in the morning, afternoon and evening.

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Fig. 2(a-d): Box and whiskers plots of (a) CO, (b) SO2, (c) NO2 and (d) PM10 at Umuago Urualla

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Fig. 3(a-d): Box and whiskers plots of (a) CO, (b) SO2, (c) NO2 and (d) PM10 at Ogboko junction

Analysis of the ANOVA statistical interpretation (F = 0.34, sig. value = 0.713, p<0.05) revealed that there is no statistical significant difference in the mean concentration of the pollutant in this location. Figure 4b shows that for SO2, the values given as 0.52-0.54 and 0.68-0.76 ppm are, respectively the 25 and 75% of the concentration recorded in the morning, afternoon and evening.

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Fig. 4(a-d): Box and whiskers plots of (a) CO, (b) SO2, (c) NO2 and (d) PM10 at Umuna junction

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Fig. 5(a-d): Box and whiskers plots of (a) CO, (b) SO2, (c) NO2 and (d) PM10 at Banana junction

The result obtained from ANOVA (F = 0.38, sig. value = 0.68, p<0.05) indicated that there is no statistical significant difference in the mean concentration of the atmospheric pollutant.

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Fig. 6(a-d): Box and whiskers plots of (a) CO, (b) SO2, (c) NO2 and (d) PM10 at Umuaka

Similarly, the 25 and 75% of NO2 concentration as shown in Fig. 4c are respectively within 0.53-0.57 and 0.67-0.77 ppm of the values recorded in the morning, afternoon and evening. The result of the ANOVA interpretation (F = 0.27, sig. value = 0.77, p<0.05) revealed that the values do not statistically differ significantly. Figure 4d implies that 25 and 75% of PM10 concentration observed are respectively within 8.60-9.30 and 10.30-11.20 mg m–3, respectively in the morning, afternoon and evening. Results deduced from ANOVA analysis (F = 0.62, sig. value = 0.55, p<0.05) revealed that there is no statistical difference in the mean level of PM10 as any variation in concentration could be due to chance.

At Banana junction, the Box and Whiskers plots presented in Fig. 5a revealed that the 25 and 75% of CO values recorded, respectively lie within 37.00-43.80 and 47.00-50.00 ppm in the morning, afternoon and evening. It was also observed that the mean values are not statistically significant as indicated by the results of the ANOVA (F = 1.71, sig. value = 0.19, p<0.05). Any observed difference therefore could be attributed to chance. Similarly, Fig. 5b revealed that 0.40-0.46 and 0.61- 0.73 ppm of SO2 concentration recorded are respectively the 25 and 75% of the values observed in the morning, afternoon and evening. The ANOVA result (F = 0.95, sig. value = 0.39, p<0.05) showed that the difference in the mean concentration of SO2 obtained is not statistically significant. Figure 5c indicated that in the morning, afternoon and evening, 25 and 75% of NO2 values obtained in this location are respectively within 0.52-0.57 and 0.58-0.67 ppm. The ANOVA result (F = 3.93, sig. value = 0.029, p<0.05) implies that the difference in the mean concentration of this air pollutant obtained in this location differ statistically. This could be due to the effect of meteorological parameters, location and season. On the other hand, Fig. 5d revealed that 25 and 75% of PM10 values recorded are respectively within 7.20-8.10 and 9.50-10.10 mg m–3 in the morning, afternoon and evening. Analysis of variance (F = 0.37, sig. value = 0.695, p<0.05) indicated that the difference in average concentration of PM10 at this location is not significant and any difference that may exist therefore may be due to chance.

The Box and Whiskers plots of Umuaka and environs revealed that 25 and 75% of the CO values recorded are within 40.00-43.00 and 48.50-49.50 ppm in the morning, afternoon and evening, respectively (Fig. 6a). ANOVA result (F = 0.16, sig. value = 0.85, p<0.05) revealed that the difference in the mean concentration of the parameters are not statistically significant and any variation may be as result of chance. However, the recorded values are ranged between 0.30-0.32 and 0.49-0.61 ppm, respectively for the 25 and 75% of the data obtained in the morning, afternoon and evening for SO2. (Fig. 6b). The results from ANOVA (F = 1.94, sig. value = 0.16, p<0.05) revealed that the mean concentration do not differ significantly at this location.

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Fig. 7(a-c): Wind rose diagrams generated at Umuaka junction, (a) Morning, (b) Afternoon and (c) Evening

Figure 6c showed that 25 and 75% of NO2 values recorded are respectively within 0.43-0.47 and 0.53-0.58 ppm in the morning, afternoon and evening. The result of ANOVA (F = 0.67, sig. value = 0.519, p<0.05) showed that the difference in mean concentration of NO2 is not statistically significant and any variation may be due to chance. On the other hand Fig. 6d indicated that the 25 and 75% of PM10 concentration recorded in the area lie within 5.10-7.30 and 7.00-9.80 mg m–3 in the morning, afternoon and evening. ANOVA result (F = 7.12, sig. value = 0.003, p<0.05) showed that the difference in the mean concentration of the atmospheric pollutant is statistically significant. This difference could be associated with influence of meteorological parameters, location and season.

Analysis of air pollutant migration and dispersion using the Wind rose diagrams: The active agents responsible for mixing of atmospheric pollutants are the meteorological variables such as wind speed, wind direction, relative humidity and ambient temperature32. Giri et al.33 noted that among the array of climatic factors, the most important is wind, which helps in the dispersion, transformation and removal of air pollutants from the ambient environment. Meteorological factors such as wind speed and direction which are used together to generate the windrose, is a veritable tool that can be used to predict the dynamical processes of atmospheric dispersion, inversion and turbulence. The windrose diagram provides real-time information on the migration and dispersal of air pollutants in an area in relationship with sources and pollutant levels34. The windrose diagrams in Fig. 7-11 were used to explain the process of dispersion of the measured atmospheric pollutants in the study locations. Results of the wind rose diagrams suggest that the dispersion and migration of the measured air pollutants are associated with the prevailing wind speed and directions observed at the study locations.

Figure 7 revealed that at Umuaka junction, the dominant wind speed in the morning, afternoon and evening ranged from 0.90->3.50 sec–1 in NE, SE and SW directions. In Fig. 8, the dominant wind speed at Banana junction in the morning, afternoon and evening ranged from>0.05->3.50 sec–1 in the SE, NW, NE and SW directions.

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Fig. 8(a-c): Wind rose diagrams generated at Banana junction, (a) Morning, (b) Afternoon and (c) Evening

Table 3:AQI values, level of health concern and colours
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AQI: Air quality index

At Umuna junction, the dominant wind speed and direction in Fig. 9a, b ranges from>0.50->3.50 sec–1 in the NE, SE, SW and NW direction, while in Fig. 9c, the dominant wind speed is in the range>2.00-3.5 sec–1 in the SE direction. Figuer 10 revealed that in the morning and evening (Fig. 10b, c) at Ogboko, the dominant speed is between>2.00-3.50 sec–1 in the NE direction, while Fig. 10a suggests that the dominant wind direction is also within>2.00-3.50 sec–1 but in the SE direction. At Umuago Urualla (Fig. 11), it was shown that the prevailing wind speed ranged from>0.90->3.50 sec–1 in the SW, NW, NE and SE directions. The above highlighted dominant wind speed and direction in Fig. 7-11 generally revealed the prevailing directions along which the dispersion and migration of the atmospheric pollutants in the study area may have taken.

Air quality index analysis: In order to assess possible effects of population exposure to the air quality level, AQI evaluation of the air pollutants studied were carried out. The result of the AQI evaluation is presented in Table 4, while Table 3 is the AQI values, level of health concern and colours. The result as presented in Table 3 shows the individual AQI, conditional pollutant and the average AQI values.

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Fig. 9(a-c): Wind rose diagrams of Umuna junction, (a) Morning, (b) Afternoon and (c) Evening

Table 4:Individual and average AQI for Orlu (wet season)
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AQI: Air quality index

The individual AQI for NO2 is 0.00 because the observed mean values were less than 0.65 ppm, the minimum value required by the Sim-air Quality software for AQI determination of the criteria air pollutants. CO is the conditional pollutant and its individual AQI contribution is the highest too. This implies that CO is the dominant pollutant and therefore mainly responsible for the air quality condition. The AQI values of the measured atmospheric pollutants is in the order CO>SO2>PM10>NO2. AQI results values are of the study locations as shown in Table 4 revealed that the AQI values are between 151-225 which implies that the atmospheric environment in this area is both unhealthy and very unhealthy for the general public as certain groups of people could be at greatest risk. The observed AQI levels are indications of atmospheric pollution resulted from elevated concentrations of the measured atmospheric pollutants. This calls for best environmental management practices to mitigate and ameliorate the risk associated with exposure to air pollution in the study area. The unregulated use of two stroke tricycles and generators that are the major sources of CO should be discouraged by the government. Similarly, old vehicles with poor engine conditions which oozes black smoke while in motion should be banned immediately to reduce unnecessary release of poisonous gases.

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Fig. 10(a-c): Wind rose diagrams of Ogboko junction, (a) Morning, (b) Afternoon and (c) Evening

Results of this study revealed that some of the ambient air pollutants studied have high concentration levels. The observed high concentration of PM10 in this study when compared with earlier reports of particulate matter in this area are generally high. Though the assessment of the ambient air particulate matter level in Orlu urban reported lower concentrations of PM10 , however, the reported values were still above international air quality standard35. Elevated concentration of PM10 has been reported elsewhere in a related study36. The high concentration of PM10 reported in this study may be attributed to a number of factors. Firstly, the months of November-February in Nigeria are the peak of the harmattan period with its dominant Northeast trending wind usually heavily loaded with dust and other particles. This may have contributed significantly-the elevated concentration of PM10. Similarly, the harmattan period is associated with heat emission which has a strong link with some dynamical processes which may lead to increased particulate concentrations. The observed elevated values of PM10 could also result from bush burning and other agricultural practices37. In addition, there were a lot of ongoing road constructions yet to be asphalted within the study area which could have significantly contributed to PM10 level in the area. The observed relatively high human activities such high vehicular and motorcycle traffic, market, motor park, commercial activities, artisan workshops as indicated in Table 1 may have also added to the elevated PM10 level. Finally, gas flaring from the nearby oil fields in the Egbema/Izombe/Osssu axis close to the area may have also contributed to the high PM10 level. The results of this study are in agreement with earlier studies which reported higher particulate matter levels in areas of high human activities38.

Though elevated concentration of NO2 was observed in most of the areas monitored, the mean concentrations are within the annual (53 ppb) US NAAQS but above the 24 h (120 μmg m–3) Nigerian NAAQS guideline values for NO2. The mean concentrations range of 0.48-0.50 ppm for NO2 recorded in this study is below some of the values reported for some air pollution studies in the region. A study on the assessment of air quality and noise pollution around Okrika community, River State, Nigeria reported higher mean concentration (1.7 ppm) of NO2 in dry season above result of the present study39.

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Fig. 11(a-c): Wind rose diagrams of Umuago Urualla junction, (a) Morning, (b) Afternoon and (c) Evening

However, a related work on seasonal analysis of atmospheric pollutant concentrations in urban and rural land use areas of Rivers State, Nigeria also reported lower concentration of NO2 than the present study40.

The mean concentration of SO2 as shown in Table 2 is slightly below the 3 h 0.5 ppm and 1 h 0.75 ppb permissible limits of US NAAQS but above the annual and 24 h guideline values of Nigerian NAAQS. However, the result of the present study is in agreement with earlier assessment of some air pollutants and their corresponding air quality at selected activity areas in Kaduna metropolis, Nigeria16. Sulphur and sulphur gases are emitted in large quantities as a result of volcanic activity and forest fire41. The concentration of these atmospheric pollutants must have increased significantly due to the large influx of old and fairly used vehicles imported into the country following the economic situation of the country with the associated high inflation26. This is compounded by poor vehicle maintenance culture and presence of a class of vehicles known as "super emitters" that emits more harmful air pollutants which may elevate the level of these pollutants42. This assertion was also supported by the fact that increases in SO2 emissions could be associated with increase in motor vehicle population since combustion of fossil fuels is an important source of SO2 emission into the atmospheric environment43,44.

Elevated concentrations of CO above Nigeria NAAQS and US NAAQS for CO was recorded in this study. The result of this study is comparable to the values reported by some related studies in Nigeria39,16,45. The mean value recorded in this study is above the value reported for Blantyre province, Malawi46 and for the Peninsular, Malaysia47. Higher concentrations of CO observed in this study could be attributed to high vehicular traffic29, presence of three stroke engine tricycles known for incomplete combustion17 and combustion of biomass31. In all, the highest level of variation in the concentrations of the air pollutants were observed in the afternoon followed by evening hours, while the least variation was recorded in the morning except for PM10. Though the co-efficient of variation for air pollutants in the study area do not differ significantly, however, the observed level of variation is generally related to the activities going on this area. This is exemplified in the case of commercial activities and traffic events that generally contribute significantly to air pollution29. Air pollution events such as dust storms, biomass combustion and firework displays which are normal occurrences during the months of data acquisition may have impacted negatively to the air quality level48. Another reason could be due to the life cycle of some of these air pollutants. For example, PM10 has a life span of about two weeks in the atmosphere before being deposited on the earth surface due to gravity while CO, which is a very poisonous gas having a life span of two months in the atmosphere before being converted to CO2 where it plays a major role in the generation of greenhouse gases and formation of ozone O333,34. Similarly, SO2 may be released into the atmosphere in large quantities by natural processess. One of the major sources is the action of anaerobic bacteria in marshes, forming hydrogen sulphide (H2S), which is further oxidized to sulphur dioxide (SO2) and sulphur trioxide (SO3) in the atmosphere35. The implication of the observed high concentration of the ambient air pollutants could be as well linked to the activities that generate these pollutants and the meteorological variables in these locations which do not differ markedly.

CONCLUSION

The findings of this study generally revealed high concentration levels of most of the measured pollutants especially CO and PM10. The observed elevated concentrations of the studied air pollutants in the afternoon is an indication that the sources of these pollutants are due to increased commercial activities during working hours. The concentration of these atmospheric pollutants must have increased significantly due to the large influx of old and fairly used vehicles imported into the country following the economic situation of the country with the associated high inflation. This is compounded by poor vehicle maintenance culture. Higher concentrations of CO observed in this study could be attributed to high vehicular traffic, presence of three stroke engine tricycles known for incomplete combustion and combustion of biomass. The observed AQI level is an indication of atmospheric pollution arising due to elevated concentrations of the measured atmospheric pollutants. This calls for best environmental management practices to mitigate and ameliorate the associated risks.

SIGNIFICANCE STATEMENT

The study discovered that most of the pollution indices analyzed in this study, nitrogen oxides (NO2), carbon monoxide (CO), sulphur dioxide (SO2) and particulate matter (PM10), are ubiquitous in the ambient environment. These atmospheric pollutants pose the greatest negative effects on the environment. Models that can elucidate the distribution of these pollutants like the Box and Whisker plots using Matlab software 7.9 is of immense benefit for the management of air pollution problems. The use of Grapher 10 to plot the wind rose models which accounts for the migration and diffusion of the air pollutants is significant in this study. It determines the dispersion of ambient air pollutants. The observed AQI of the study area therefore, demands serious attention by environmentalist, researchers, regulatory bodies and of course the government at the various levels in order to mitigate the anthropogenic sources of these pollutants.

ACKNOWLEDGMENT

The authors acknowledge the support from Laboratory Services and Environmental Research Department/UNIDO RAC for Pollution Monitoring and Assessment, Ministry of Environment and Petroleum, Imo State, Nigeria, New Concept Laboratory, Owerri , Nigeria and Prime mover Consult Port Harcourt, Nigeria for providing the air quality monitoring equipments and technical assistant during the field work.

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