SEASONAL VARIATION OF MONTHLY AIR QUALITY INDICES FOR CALABAR, NIGERIA

Sunday O. Udo¹, Igwe O. Ewona², Mfon David Umoh^3* and Cletus Nzan Agbor⁴

¹Department of Physics, University of Calabar, Calabar, Nigeria.

²Department of Physics, Cross River State University of Technology, Calabar, Nigeria. P.M.B. 1123 Calabar, Nigeria.

³Department of Science Maritime Academy of Nigeria, Oron, Nigeria.

⁴Department of Physics University of Calabar, Calabar, Nigeria.

*Corresponding Authors:

Mfon David Umoh
Department of Science Maritime
Academy of Nigeria, Oron, Nigeria
E-mail: mfonslago@yahoo.com

Received Date: 10 November, 2017 Accepted Date: 23 April, 2018

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Abstract

Air Quality Index (AQI) for the city of Calabar has been computed for the different seasons of the year covering December 2015 to November 2016. The values obtained from the calculation of the combined AQI shows that the AQI for the different months of the year were categorized as “good”. The highest value of 15.87 was obtained in December 2015 and the least value of 5.06 was obtained in April 2016. The coefficient of determination obtained for the period of study shows that total suspended particulate (TSP) had the highest effect on the variation of AQI with the value of 87.3%, while temperature with value 1.8% had the least effect on the variation of AQI within the study period. The AQI calculated from the individual pollutants showed that Ozone had the highest value of 18.75 in December 2015 and its least value of 7.81 in March 2016. Carbon monoxide (CO) had the highest value of 29.7 in September 2016 and its least value of 6.06 in April 2016. Sulphur dioxide (SO2) had the highest value of 22.06 in July 2016 and August its least value of 8.8 in December and January 2016. NOx had the highest value of 93.75 in December 2015 and its least value of 0 in April, July, October and November 2016. All the calculated AQI for the individual pollutants were categorized as “Good” except NOx which was categorized as moderate.

Keywords

Air Quality Index, Coefficient of determination, Ozone, Pollutants, Categorized, September

Introduction

An Air Quality Index (AQI) may be defined as a numerical rating that reflects the composite Influences on overall quality of a number of air quality parameters (Vennapu, 2014). AQI is typically a numerical scale, usually colour coded, intended to convey the likely severity of the adverse health effects at the monitored concentration levels (Cairncross, et al., 2007). Nigeria as a developing country is facing serious air pollution crisis as some major cities have very poor air quality. In Nigeria, Lagos is of particular interest for air pollution studies because of its high population density coupled with intense industrial and commercial activity (Oluyemi and Asubiojo, 2001). Calabar is also gaining attention because of its tourism potentials. The determination of the quality of air is a very important factor in assessing the suitability of the air we breathe. Evaluating Air Quality Index (AQI) is one such method of determining the quality of air in an area (Kumar, et al., 2015).

Several Authors have worked on the estimation of AQI such as (Reddy, et al., 2003; Srivastava and Rajasree 2006; Tiwari, 1987; Vennapu, 2014; Kumar, et al., 2015; Murad, et al., 2014; Shivangi, et al., 2015; Chuanglin, et al., 2015; Akuagwu, et al., 2016; Shukla, et al., 2010). This paper looks at the estimation of seasonal air quality index for the urban city of Calabar.

Geography of Calabar

Calabar, the location of the study, is a city in the Niger Delta Area of Nigeria. It is the capital of Cross River state, Southern Nigeria. The city is watered by the Calabar River and Great Qua Rivers Creeks of the Cross River. Its Coordinates are lat. 5°16’07.6”N, Long. 8°23’34. 56E. Calabar has an estimated population of 1.2 million residents. Calabar covers an area of 604 km² (Umoh, et al., 2013). March and April are the months preceding the heavy rains in Calabar. The project site which is at the University of Calabar is at an elevation of 62 m with lat. 4°57’07’’ and long. 8°20’51’’. Calabar is almost surrounded by sea water at distances between three to five kilometres to the south, east and west to the station. Two major winds which affect the climate of West African coast blow across this region bringing about two major seasons in the area, namely: wet and dry seasons, probably named after the pattern at which the rain falls. While the wet season last between April and October, the dry season is normally from November to March of the following year (Ewona and Udo, 2008) (Figure. 1).

Figure 1: Map of the study area.

Research Methodology

Materials and Methods

The data for this research has been obtained from the stationary Aeroqual AQM65 equipment procured by funds from Tertiary Education Fund (TETFUND). This equipment is stationed at the University of Calabar main monitoring station. It measures continuously the following gases: SO₂, NOx, O₃, H₂S, CO, TSP and meteorological parameters such as wind speed, wind direction, Temperature and relative humidity. The AQM 65 is a fully integrated air quality monitoring station that delivers ‘near reference’ levels of performance. The AQM 65 measures the criteria pollutants to WHO air quality limits. The data covers a period of one year from December 2015 to November 2016.

Methodology

R-programming language was used in performing the correlation analysis in this study. The statistical package for the social sciences (SPSS) was used in obtaining the coefficient of determination.

Calculating the combined AQI

Let there be n air quality parameter Pi (i=1, 2, 3…n), which are to be taken into account for calculating the AQI. Let V_i be the observed values of the ith parameter in the ambient air and let V_si be the standard value recommended for this parameter. Then the quality rating Q_i for this parameter is given by:

(1)

If Q_i<100, it is to be noted that the given parameter is within the prescribed limit. On the other hand, If Q_i>100, it implies that the ith parameter exceeds the prescribed standard and the ambient air is harmful for breathing by human beings. It is assumed here that all the parameters have equal importance and so only the unweighted air quality indices are calculated. The geometric unweighted AQI may be calculated from the Quality rating Q_i by taking their geometric mean.

This relation is simplified and hence

AQI=Antilog (ΣLog Qi)/n (2)

The Ambient AQI can be calculated using (1) and (2) (Kumar, et al., 2015) (Table 1).

Table 1. AQI categorization table.

Air quality index based on the individual pollutants

AQI for the four pollutants were calculated by using equation 3 below. The equation is used to calculate the AQI of a location based on the measured concentration of a particular pollutant. Ip is the AQI that is to be calculated based on the pollutant. Cp is the concentration of the pollutant rounded to a reasonable decimal. BPHi is the breakpoint that is greater than or equal to the rounded concentration of the pollutants. BPLo is the breakpoint that is less than or equal to the rounded concentration of the pollutant Cp. IHi is the AQI value that corresponds to BPHi. While ILo is the AQI value that corresponds to BPLo. The measured concentration of the four pollutants were used in the computation of the AQI. The AQI was based on the breakpoints published by the (United State Environmental Protection Agency (USEPA), 2006). These breakpoints are presented in Table 2. The average of the AQI for each point was calculated to represent the AQI for the point.

Table 2. Break points for the different pollutants.

AQI Equation

(3)

Where: I_P=The index for pollutant P

C_P=The rounded concentration of pollutant P

BP_Hi=The breakpoint that is greater than or equal to CP

BP_Lo=The breakpoint that is less than or equal to C_P

I_Hi=The AQI value corresponding to BP_Hi

I_Lo=The AQI value corresponding to BP_Lo

Results and Discussion

Concentration of pollutants

The concentration of pollutants measured within the study period shows that, hydrogen sulphide has the highest value of 0.096 ppm in May 2016 and its lowest value of 0 ppm in October and November 2016. Ozone has the highest value of 0.024 ppm in December 2015 and its lowest value of 0.010 ppm in March and December 2016. CO has the highest value of 2.620 ppm in September 2016 and its lowest value of 0.533 ppm in April 2016. SO₂ has the highest value of 0.02 ppm in December 2016 and its lowest value of 0.005 ppm in February 2016. NOx has the highest value of 0.03 ppm in December 2015 and its lowest value of 0.0 ppm in April, July, October and December 2016. The calculated AQI values had the highest value of 15.87 in December 2015 and its lowest value of 5.060 in April 2016. Table 1 shows the AQI categorization table. This table shows the different categories of AQI and its description. The AQI categorization falls between 0 – 500, with 0 -50 being categorized as “Good” and 401 – 500 being categorized as “Hazardous”. Table 2 shows the breakpoints for the different pollutants as published by the United States Environment protection agency USEPA. The break points were used to calculate the AQI for the individual pollutants.

(Figure. 2) shows plot for the calculated values of AQI. The (Figure. 2) shows that the calculated AQI values was high in December 2015 but dropped in March and April 2016. The value went up slightly and stabilized throughout the year.

Figure 2: Graph showing monthly variation of combined AQI.

Table 3 shows the individual AQI values calculated based on the concentration of the pollutants; ozone (O₃), carbon monoxide (CO), sulphur dioxide (SO₂) and nitrogen oxides (NOx). Ozone had the highest value of 18.75 in December 2015 and its least value of 7.81 in March 2016. CO had the highest value of 29.7 in September 2016 and its least value of 6.06 in April 2016. SO₂ had the highest value of 22.06 in July 2016 and August its least value of 8.8 in December and January 2016. NOx had the highest value of 93.75 in December 2015 and its least value of 0 in April, July, October and November 2016. (Figure. 3) shows that the AQI calculated from ozone dropped in March and April and increased from May towards the end of the year. (Figure. 4) shows AQI calculated from CO. The AQI increased from April to November 2016. (Figure. 5) shows AQI calculated from SO₂. The AQI increased in July and dropped towards December 2016. (Figure. 6) shows AQI for NOx. The AQI values dropped in March 2016 and did not increase much throughout the year.

Figure 3: Graph showing monthly variation of AQI calculated from ozone.

Figure 4: Graph showing monthly variation of AQI calculated from carbon dioxide.

Figure 5: Graph showing monthly variation of AQI calculated from sulphur dioxide.

Figure 6: Graph showing monthly variation of AQI calculated from nitrogen dioxides.

Table 3. Values of AQI for the different pollutants.

Correlation coefficient

There is need to carry out correlation between pollutants though Table 4 shows that the AQI for the study location all fall in the “good” category. This is to help the authors to see the effect these pollutants have on each other and also on the AQI of the study location. The correlation coefficients are shown in Table 5. A quick inspection of the table shows that AQI correlates strongly with NOx with R=0.93. this value shows that NOx has the greatest effect on the AQI at the location. Relative humidity also has a strong correlation with AQI, though negatively, with R=-0.86. This negative value shows that the increase in relative humidity at the location led to the decrease in AQI. CO had the least correlation with AQI with R=-0.12. Ozone had R=0.46, while SO₂ had R=-0.33. Wind speed had a fairly weak correlation of R=0.48. It shows that wind speed affected the AQI positively in some way. Table 6 shows Coefficient of determination between AQI and pollutants. The coefficient of determination R² between AQI and H₂S is R²=0.094. This tells us that 9.4% of the variation in the Air Quality index within the period of study is related to the effect of H₂S. The coefficient of determination R² between AQI and O₃ is R²=0.211. This tells us that 21% of the variation in the Air Quality index within the period of study is related to the effect of O₃. The coefficient of determination R² between AQI and CO is R²=0.014. This tells us that 1.4% of the variation in the Air Quality index within the period of study is related to the effect of CO. The coefficient of determination R² between AQI and SO₂ is R²=0.108. This tells us that 10.8% of the variation in the Air Quality index within the period of study is related to the effect of SO₂. The coefficient of determination R² between AQI and NOx is R²=0.858. This tells us that 85.8% of the variation in the Air Quality index within the period of study is related to the effect of NOx. The coefficient of determination R² between AQI and TSP is R²=0.873. This tells us that 87.3% of the variation in the Air Quality index within the period of study is related to the effect of TSP. The coefficient of determination R² between AQI and wind speed is R²=0.235. This tells us that 23.5% of the variation in the Air Quality index within the period of study is related to the effect of wind speed. The coefficient of determination R² between AQI and Temperature is R²=0.018. This tells us that 1.8% of the variation in the Air Quality index within the period of study is related to the effect of Temperature. The coefficient of determination R² between AQI and Relative humidity is R²=0.766. This tells us that 76.6% of the variation in the Air Quality index within the period of study is related to the effect of Relative humidity.

Table 4. The combined AQI for the study location.

Table 5. Correlation coefficient for the pollutants and combined AQI values.

Table 6. Coefficient of determination between AQI and pollutants.

Summary and Conclusion

The Air Quality Index (AQI) for the city of Calabar has been computed for the different seasons of the year covering December 2015 to December 2016. The values obtained from the calculation of the combined AQI shows that the AQI for the different months of the year were categorized as “good”. The highest value of 15.87 was obtained in December 2015 and the least value of 5.06 was obtained in April 2016. The coefficient of determination obtained for the period of study shows that total suspended particulate (TSP) had the highest effect on the variation of AQI with the value of 87.3%, while temperature with value 1.8% had the least effect on the variation of AQI within the study period. The AQI calculated from the individual pollutants showed that Ozone had the highest value of 18.75 in December 2015 and its least value of 7.81 in March 2016. CO had the highest value of 29.7 in September 2016 and its least value of 6.06 in April 2016. SO₂ had the highest value of 22.06 in July 2016 and August its least value of 8.8 in December and January 2016. NOx had the highest value of 93.75 in December 2015 and its least value of 0 in April, July, October and November 2016. All the calculated AQI for the individual pollutants were categorized as “Good” except NOx which was categorized as moderate.

Acknowledgement

The authors are grateful to Tertiary education fund (TETFUND) for providing funds for this research.

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