Issue 3 (199)/2024

ALSE and ACS Style
Anyika, L.C.; Obi, C. Predictive Air Pollution Assessment Using Matrix Algebra and GIS/GPS in Aguleri Anambra State. Journal of Applied Life Sciences and Environment 2024, 57 (3), 437-458.
https://doi.org/10.46909/alse-573146

AMA Style
Anyika LC, Obi C. Predictive Air Pollution Assessment Using Matrix Algebra and GIS/GPS in Aguleri Anambra State. Journal of Applied Life Sciences and Environment. 2024; 57 (3): 437-458.
https://doi.org/10.46909/alse-573146

Chicago/Turabian Style
Anyika, Leonard Chukwuemeka, and Chidi Obi. 2024. “Predictive Air Pollution Assessment Using Matrix Algebra and GIS/GPS in Aguleri Anambra State” Journal of Applied Life Sciences and Environment 57, no. 3: 437-458. 
https://doi.org/10.46909/alse-573146

Predictive Air Pollution Assessment Using Matrix Algebra And GIS/GPS in Aguleri Anambra State

Leonard Chukwuemeka ANYIKA¹ and Chidi OBI^2*

¹Department of Industrial Chemistry, Madonna University Elele Campus, Rivers State Nigeria
²Department of Pure and Industrial Chemistry, University of Port Harcourt, Rivers State Nigeria

*Correspondence: chidi.obi@uniport.edu.ng

Received: Jun. 02, 2024. Revised: Jul. 22, 2024. Accepted: Jul. 31, 2024. Published online: Nov. 18, 2024

ABSTRACT. This study assessed the air pollution loads of sulphur dioxide (SO₂), nitrogen dioxide (NO₂) and particulate matter (PM₁₀) from Aguleri in Anambra State of Nigeria using matrix algebra and the geographical information system (GIS)/global positioning system (GPS) attachment to MATLAB. The pollutant values of SO₂ and NO₂ were obtained using the Crowcon Gas Monitor Model CE 89/336/EEC, while the PM₁₀ values were obtained with the Crowcon Particulate Monitor Model No.1000 with the serial number 298621. The pollution characteristics of the study area were simulated using the polynomial expression yi = k + k₁x₁ + k₂x₂ + k₃x₃ +… k_nx_n.. The predictive parameter constants, k, were determined with the solution to the simultaneous equations arising from the polynomial expressions using matrix algebra. MATLAB 7.9 curve fitting software was used to produce associated model equations from the fitted curves for the variations of SO₂, NO₂ and PM₁₀ as a function of locations in Aguleri for both rainy and dry seasons. The evaluation of pollution models used for the study showed that constants from the fitted curves do not closely match constants from ab initio calculations. The corresponding coordinates in both GIS/GPS contour and surface plots revealed a pollution distribution concentration of 50% in Aguleri. The results revealed that the stations in Aguleri had a satisfactory air pollution index rating. This study serves as an improvement to air quality studies and a veritable tool for air quality management and policymaking.

Keywords: air pollutants; particulate matter; polynomial equations; seasons; software.

 

INTRODUCTION

Air pollution is a global phenomenon that has gradually distorted the Earth’s climate, leading to the greenhouse effect, acid rain, flooding, high temperatures, death of aquatic species, and disease spread (Abdul-Lateef et al., 2021; Abulude et al., 2024; Chengyue et al., 2021; Ilmas et al., 2018; Omokpariola et al., 2024; Tella and Balogun, 2022). Abdul-Lateef et al. (2021) reported that according to the World Health Organization (WHO), ambient and indoor air pollution have significantly increased mortality and morbidity rates, especially in developing countries. The WHO (2022) reported that the combined effects of ambient and household air pollution are associated with 6.7 million premature deaths annually, with an estimated 4.2 million premature deaths recorded worldwide in 2019, 89% of which occurred in low- and middle-income countries, particularly in the WHO South-East Asia and Western Pacific Regions.

The current population pressure in Nigeria, alongside urbanisation, modern agricultural practices, and industrialisation, has rapidly metamorphosed into air pollution (Abulude et al., 2024; Omokpariola et al., 2024). Abdulraheem et al. (2023) observed an increasing pattern in pollutants such as carbon monoxide (CO), oxides of nitrogen (NOx), particulate matter of 2.5 micrometres in diameter (PM_2.5), particulate matter of 2.5 micrometres in diameter (PM₁₀), sulphur dioxide (SO₂), ammonia (NH₃) and non-methane volatile organic compounds arising from the population surge in Nigeria from 1980 to 2020. The total emissions such as CO, NOx, PM_2.5, PM₁₀, SO₂, NH₃ and NMVOC recorded increased from 1736 to 6210 Gg, 143 to 338 Gg, 126 to 551 Gg, 171 to 717 Gg, 19 to 60 Gg, 4 to 28 Gg and 471 to 1587 Gg, respectively. The author reported that emissions from wood fuel, transportation, and municipal waste are the major sources contributing to 63%, 16%, and 15% of the total CO emissions (Abdulraheem et al., 2023). Environmental pollution modelling is a numerical tool used to describe the causal relationships between emission, discharge, and fluxes of various kinds through the natural environmental matrix. Such tools are of considerable importance in the agricultural field due to the overwhelming influence of land and water use for sustainable development (Lokeshwari et al., 2014).

The agricultural non-point source (AGNPS) pollution model was developed to analyse non-point source pollution in agricultural watersheds. Within this framework, run-off characteristics and transport processes of sediments and nutrients can be simulated for each geographical map cell routed. This permits the run-off, sedimentation, encrustations and erosion in each cell in the watershed to be determined or simulated (Luo et al., 2023; Zhu et al., 2020). Thus, AGNPS can identify sources contributing to a potential pollution challenge and prioritise those locations where remedial measures could be initiated to improve land use quality. Such modelling can be applied to air pollution studies by the incorporation of a geographic information system (GIS), global positioning service (GPS), and remote or proximate sensing (Borah et al., 2024; Firouraghi et al., 2022; Khaslan et al., 2024; Thakur et al., 2017; Utbah et al., 2023). Data from these systems can be processed in multiple dimensions on a digitised geographical map (Balogun et al., 2011; Chaminé et al., 2021; Najibullah, 2020).

In this process, GIS data layers required by models similar to AGNPS models can be created using appropriate statistical map treatment. The data generated will subsequently become spatial information for pollution studies (Khan and Jehangir, 2023; Matejicek, 2005; Tella and Balogun, 2022; Verma et al., 2023). When air pollution attributes such as NO₂, SO₂, PM, and ozone (O₃) are available, GIS, GPS and digitised map formats can be extracted and combined with other data such as meteorological indices reformatted as needed for various geographical and best management practices in the total environment (Badach et al., 2020; Seham et al., 2022; Yerramilli et al., 2011; Yoo, 2022).

With the increase in urbanisation, industrialisation, the number of vehicles, and other anthropogenic activities, researchers have employed more sophisticated methodologies such as machine learning, artificial intelligence, and the Internet of Things to solve air pollution problems (Abdul-Lateef et al., 2021; Patra et al., 2016; Zezhi et al., 2024). Several studies of atmospheric pollution by attributes such as CO, NO_x, SO_x, hydrocarbons (C_nH_2n+2), and PM, which may or may not encapsulate metal species or volatile organic residues, have been carried out all over the world (Abdulraheem et al., 2023; Daful et al., 2020; Gerard, 2021; Jyethi et al., 2016; Knepnick and Sebastian, 1990; Manisalidis et al., 2020). Such studies, including research on emerging pollutants, are becoming widespread in Nigeria (Abam and Unachukwu, 2009; Augustine, 2012; Dimari et al., 2008; Ediagbonya and Tobin, 2013; Egbuna et al., 2021; Obisesan and Weli, 2019; Omofonmwan and Osa-Edoh, 2008; Mahmud et al., 2023; Tawari and Abowei, 2012; Yalwaji et al., 2022). However, only a handful of these studies have been able to relate physical, geographical, and meteorological data into a model which can respond to best management practices (Anyika et al., 2018). The data available so far have been only a little better than baseline statistics comparing available physical concentrations of attributes with either WHO standards or FEPA equivalents. The level of air pollution monitoring or control in Nigeria is not comparable to that of large cities like Cairo, Tehran, and Johannesburg. These countries have established regular air pollution monitoring stations for many years. Therefore, the focal point of this work is to attempt an improved interpretation of air pollution assessment using GIS, GPS, and matrix algebra.

 

MATERIALS AND METHODS

Description of Study Area

Aguleri is an area north-north-east of Onitsha town bound by the coordinates 6º20ʹ30E to E6º53ʹ, and it is 24 km NNE from Onitsha main town. Aguleri, located in the River Anambra Basin, is an agrarian town with a population of about 300,000, as represented in Figure 1. The map was obtained from Google Earth using Arc View 3.0 software. Georeferencing of all data points in maps was done using GARMIN GPS 78 s chart plotting receivers. Nine locations were designated as sampling stations in Aguleri, and each designated sampling station was divided into four sampling points from where four samples were collected. The climate of Aguleri is tropical or savanna, with two distinct dry and wet seasons. It has an annual temperature of 87.66º, 1.46% higher than Nigeria’s average temperature, with annual precipitation of 8.92 inches and 71.12% of rain annually. Aguleri has an elevation of zero feet above sea level. The points in each station have been georeferenced to enable the application of GIS/GPS analysis parameters summarised in Table 1 and Table 2.

 

Table 1
Georeferenced Coordinates for the Rainy Season in Aguleri

Sampling Station	Coordinates
	Point 1	Point 2	Point 3	POINT 4
Aguleri Junction	N06º19.757ʹ E006º52.628ʹ	N06º19.755ʹ E006º52.601ʹ	N06º19.693ʹ E006º52.612ʹ	N06º19.689ʹ E006º52.640ʹ
Ifite Aguleri	N06º19.354ʹ E006º53.212ʹ	N06º19.371ʹ E006º53.188ʹ	N06º19.376ʹ E006º53.212ʹ	N06º19.356ʹ E006º53.194ʹ
Igboezuru Aguleri	N06º18.666ʹ E006º54.622ʹ	N06º18.666ʹ E006º54.650ʹ	N06º18.650ʹ E006º54.645ʹ	N06º18.677ʹ E006º54.631ʹ
Okpu Aguleri	N06º19.584ʹ E006º53.514ʹ	N06º19.563ʹ E006º53.547ʹ	N06º19.599ʹ E006º53.538ʹ	N06º19.562ʹ E006º53.524ʹ
Umuokpoto Aguleri	N06º19.758ʹ E006º52.933ʹ	N06º19.738ʹ E006º52.966ʹ	N06º19.736ʹ E006º52.949ʹ	N06º19.773ʹ E006º52.963ʹ
Enugu Otu Aguleri	N06º32.134ʹ E006º55.279ʹ	N06º32.087ʹ E006º55.300ʹ	N06º32.029ʹ E006º55.319ʹ	N06º31.366ʹ E006º55.344ʹ
Ezi Agulu Otu Aguleri	N06º21.480ʹ E006º51.499ʹ	N06º21.489ʹ E006º51.517ʹ	N06º21.496ʹ E006º51.492ʹ	N06º21.450ʹ E006º51.496ʹ
Umundeze Aguleri	N06º21.182ʹ E006º51.290ʹ	N06º21.168ʹ E006º51.289ʹ	N06º21.171ʹ E006º51.302ʹ	N06º21.171ʹ E006º51.274ʹ
Amaeze Aguleri	N06º20.570ʹ E006º50.711ʹ	N06º20.583ʹ E006º50.678ʹ	N06º20.565ʹ E006º50.661ʹ	N06º20.541ʹ E006º50.683ʹ

 

Table 2
Georeferenced Coordinates for the Dry Season in Aguleri

Sampling Station	Coordinates
	Point 1	Point 2	Point 3	Point 4
Aguleri Junction	N06º19.736ʹ E006º52.632ʹ	N06º19.738ʹ E006º52.608ʹ	N06º19.689ʹ E006º52.611ʹ	N06º19.689ʹ E006º52.637ʹ
Ifite Aguleri	N06º19.351ʹ E006º53.213ʹ	N06º19.376ʹ E006º53.212ʹ	N06º19.370ʹ E006º53.193ʹ	N06º19.355ʹ E006º53.197ʹ
Igboezuru Aguleri	N06º18.671ʹ E006º54.631ʹ	N06º18.665ʹ E006º54.620ʹ	N06º18.648ʹ E006º54.654ʹ	N06º18.669ʹ E006º54.644ʹ
Okpu Aguleri	N06º19.557ʹ E006º53.520ʹ	N06º19.570ʹ E006º53.539ʹ	N06º19.586ʹ E006º53.535ʹ	N06º19.582ʹ E006º53.521ʹ
Umuokpoto Aguleri	N06º19.740ʹ E006º52.952ʹ	N060º19.741ʹ E006º52.965ʹ	N06º19.765ʹ E006º52.962ʹ	N06º19.756ʹ E006º52.947ʹ
Enugu Otu Aguleri	N06º33.717ʹ E006º54.104ʹ	N06º33.694ʹ E006º54.106ʹ	N06º33.657ʹ E006º54.115ʹ	N06º33.657ʹ E006º54.115ʹ
Ezi Agulu Otu Aguleri	N06º21.496ʹ E006º51.494ʹ	N06º21.490ʹ E006º51.507ʹ	N06º21.458ʹ E006º51.495ʹ	N06º21.470ʹ E006º51.504ʹ
Umundeze Aguleri	N06º21.166ʹ E006º51.291ʹ	N06º21.175ʹ E006º51.279ʹ	N06º21.165ʹ E006º51.292ʹ	N06º21.175ʹ E006º51.298ʹ
Amaeze Aguleri	N06º20.574ʹ E006º50.710ʹ	N06º20.562ʹ E006º50.662ʹ	N06º20.567ʹ E006º50.665ʹ	N06º20.543ʹ E006º50.684ʹ

 

Experimental Design

Sulphur dioxide (SO₂), nitrogen oxide (NO₂), particulate matter (PM₁₀), relative humidity, and wind speed were assessed in the study area. The data obtained were applied to create the various concentration contours and model polynomial equations (matrix algebra). MATLAB 7.9 fitting software was used to plot the graph of weighted coordinates against the mean concentrations in each location in Aguleri (Pilla and Broderick, 2015; Yorkor et al., 2017).

Data Acquisition

Data were acquired by measuring in situ ground-level concentrations of SO₂, NO₂, and PM10 using Crowcon Gas Monitors (Model CE 89/336/EEC) and a Crowcon Particulate Monitor (Model No. 1000, Serial No. 298621) from the Imo State Environmental Protection Agency. Wind speed, direction, and relative humidity were determined using a digital meter and Environmental Meter (Model AE.09605) from Rumsey Environmental LLC at the Federal University of Technology, Owerri.

Gas analysers and sensors, Model Lancom III, manufactured by Land Instrument International Pittsburgh, PA were operated using the Thermo Electron gas analysers procedure.

The sensor components were SnO₂, as used by Robert et al. (2011).

 

 

Calibration of Instruments Used

The sensors, initially factory calibrated, were recalibrated and stabilised using NO₂ and SO₂ gases, as detailed by Park et al. (2013) at the Imo State Environmental Protection Agency laboratories.

Air Quality Index (AQI)

The AQI indicates the pollution level in an area’s atmosphere by calculating the individual air quality index (IAQI) for each pollutant using Equation 1:

Here, IAQI_P represents the IAQI for pollutants (PM10, SO₂, NO₂), C_P stands for the daily mean concentration of the pollutant, BP_LO and BP_HI are the nearest and lowest values of C_P, and IAQI_LO and IAQI_HI are the IAQIs in terms of BP_HI and BP_LO as shown in Table 3. Table 3 shows that the IAQI maximum exceeds 100. After calculating the IAQI_P for each pollutant, the AQI is determined by selecting the maximum IAQI_P as expressed in Equation 2:

According to Anyika et al. (2018), Equation 2 shows that AQI evaluation is not the sum of all the pollutants involved but is the maximum value of IAQI obtained.

The AQI category and ratings prescribed by the United States Environmental Protection Agency (USEPA) for pollution evaluation are presented in Table 3.

MATLAB Model Set-Up for Pollution Analysis

The pollution characteristics model was generated by integrating the spatial and pollution attributes databases using a polynomial expression (Equation 3), with coordinates for points 1–4 forming the spatial database and the pollution index at any sampling station represented by function y, depending on pollutant concentrations, if the wind rose, and meteorological conditions, resulting in four simultaneous equations (Equations 4–7) for each station (Jiayu et al., 2018; Palomera et al., 2016; Raju et al., 2012). 

yj = k + k1x1 + k2x2 + k3x3 … + knxn	(3)
y1 = k + k1x1 + k2x2 + k3x3	(4)
y2 = k + k1x4 + k2x5 + k3x6	(5)
y3 = k +k1x7 + k2x8 + k3x9	(6)
y4 = k +k1x10 + k2x11 + k3x12	(7)

where y₁ = pollution index at a given coordinate, such as point 1, k is an empirical constant k₁, k₂, and k₃, are constants which modify the empirical pollutant concentrations and are the constants for the variables SO_2, NO₂ and PM₁₀, respectively.

 

Table 3
Air Quality Index, USEPA (2000)

AQI Category	AQI Rating
Very Good (0–15)	A
Good (16–31)	B
Moderate (32–49)	C
Poor (50–99)	D
Very Poor (100 or over)	E

 

In the particular case under study, x₁ was the SO₂ concentration at point 1, x₂ was the NO₂ concentration at point 1, and x₃ was the PM₁₀ concentration at point 1.

Then, x_4, x₅ and x₆ were the SO_2, NO_2, and PM₁₀ concentrations, respectively, at point 2. x_7, x₈ and x₉ were the SO_2, NO_2, and PM₁₀ concentrations, respectively, at point 3. x_10, x₁₁ andx₁₂ were the SO_2, NO_2, and PM₁₀ concentrations, respectively, at point 4.

The solution of the set of simultaneous equations (Jiayu et al., 2018; Park et al., 2013; Palomera et al., 2016; Raju et al., 2012) can be achieved using matrix algebra where k represents the constants to be determined after solving the set of four simultaneous equations by applying matrix algebra explicitly.

where X is, therefore, the 4 × 4 matrix of which the first column was constant (i.e., 1), the second column was for the SO₂ index, the third column was for the NO₂ index, the fourth column was for the PM₁₀ pollution index, y_i represents the value of the coordinates at the sampling stations, and then the INPUT was x_i, y_i.

The function results from the solution to the simultaneous equations, which inputs x_i and y_i values so that the MATLAB 7.9 notation results in Equation 9 and Equation 10.

G = in v (x)	(9)
k = G*yi	(10)

where G represents the variable that outputs the inverse of the matrix X and the solutions.

 

RESULTS

Air Pollutant Concentration in Aguleri during the Rainy Season

The values presented in Table 4 and Table 5 show the average for each of the four points.

Model Development

The values of k obtained from the matrix algebra are presented in Table 6, Table 7, Table 8 and Table 9.

MATLAB-Assisted Model Development

MATLAB-assisted fitted curves for variations in the concentration of SO₂, NO₂, and PM₁₀ as a function of coordinates in the Aguleri study area during rainy and dry seasons are shown in Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7 using the Aguleri Junction study location.

 

DISCUSSION

Evaluation of Pollution Models

The data generated revealed that the Ab Initio air pollution model developed from the fitted curves (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7) does not match constants from Ab Initio calculations closely enough in Table 6, Table 7, Table 8 and Table 9. This could be because Ab Initio calculations were an average of all variations at a station as a linear summation.

Constants from MATLAB fitted curves represent the effect of one parameter as a pollutant at a given time and across all locations of the study area.

Therefore, to a greater extent, the above plots represent a much more theoretical evaluation of pollution as a function of a single component and in tandem with the result of a non-point source event-based medium, which accounts for the simultaneous effects of all pollution indices.

 

Table 4
Summary of Pollutant Data in Aguleri during the Rainy Season

STATION	SO2 (µg/m3)	NO2 (µg/m3)	PM10 (µg/m3)	Relative Humidity %	Wind Speed ms-1	Wind Direction (O)
Aguleri Junction	12.95 ± 0.01	75.20 ± 0.03	61.25 ± 0.01	73.50 ± 0.01	1.88 ± 0.01	233.20 ± 0.02
Ifite Aguleri	7.85 ± 0.01	61.10 ± 0.01	32.00 ± 0.01	68.40 ± 0.01	1.47 ± 0.01	210.50 ± 0.02
Igboezuru Aguleri	4.15 ± 0.02	47.00 ± 0.02	22.00 ± 0.02	66.90 ± 0.01	1.65 ± 0.02	236.90 ± 0.01
Okpu Aguleri	3.00 ± 0.01	37.60 ± 0.01	20.00 ± 0.20	70.00 ± 0.02	1.50 ± 0.03	243.80 ± 0.01
Umuokpoto Aguleri	2.90 ± 0.02	42.30 ± 0.20	28.00 ± 0.01	63.80 ± 0.01	1.16 ± 0.01	240.90 ± 0.02
Enugu Otu Aguleri	0.97 ± 0.01	21.15 ± 0.01	10.25 ± 0.01	69.50 ± 0.02	1.25 ± 0.03	251.70 ± 0.02
Ezi Aguluotu Aguleri	0.95 ± 0.02	11.75 ± 0.02	5.00 ± 0.01	74.10 ± 0.01	1.07 ± 0.01	193.80 ± 0.02
Umundeze Aguleri	1.10 ± 0.02	14.10 ± 0.01	6.50 ± 0.01	68.30 ± 0.01	1.20 ± 0.02	175.50 ± 0.01
Amaeze Aguleri	2.35 ± 0.01	44.65 ± 0.01	25.50 ± 0.01	68.50 ± 0.20	1.64 ± 0.01	197.30 ± 0.20

 

Table 5
Summary of Pollutant Data in Aguleri during the Dry Season

STATION	SO2 (µg/m3)	NO2 (µg/m3)	PM10 (µg/m3)	Relative Humidity %	Wind Speed ms-1	Wind Direction (O)
Aguleri Junction	37.27 ± 0.01	131.60 ± 0.01	171.25 ± 0.01	62.90 ± 0.02	1.30 ± 0.01	202.75 ± 0.02
Ifite Aguleri	18.30 ± 0.01	103.40 ± 0.01	104.50 ± 0.01	54.70 ± 0.01	1.79 ± 0.01	200.50 ± 0.02
Igboezuru Aguleri	12.45 ± 0.02	63.45 ± 0.03	78.50 ± 0.02	53.50 ± 0.20	1.20 ± 0.01	224.75 ± 0.01
Okpu Aguleri	10.45 ± 0.01	54.05 ± 0.01	59.50 ± 0.01	56.00 ± 0.02	1.31 ± 0.01	232.25 ± 0.01
Umuokpoto Aguleri	11.15 ± 0.02	75.20 ± 0.03	69.75 ± 0.02	49.10 ± 0.01	0.76 ± 0.01	238.00 ± 0.02
Enugu Otu Aguleri	6.82 ± 0.01	42.30 ± 0.01	30.00 ± 0.01	50.70 ± 0.01	0.64 ± 0.01	218.25 ± 0.03
Ezi Aguluotu Aguleri	7.17 ± 0.02	47.00 ± 0.01	38.50 ± 0.01	52.60 ± 0.01	0.45 ± 0.01	168.50 ± 0.02
Umundeze Aguleri	6.85 ± 0.01	63.45 ± 0.01	47.00 ± 0.01	54.60 ± 0.01	1.02 ± 0.01	136.00 ± 0.01
Amaeze Aguleri	12.45 ± 0.01	98.70 ± 0.01	64.50 ± 0.01	52.70 ± 0.01	1.57 ± 0.01	124.00 ± 0.01

 

Table 6
Summary of Explicit Polynomials Obtained as Solutions (Aguleri, rainy season)

Station	Pollution index model
Aguleri junction	0.6023–9.4×10–5SO2+1.6×10–5NO2+9.4×10–6PM10
Ifite Aguleri	0.6039+3.5×10–5SO2+4.02×10–6NO2+9.6×10–6PM10
Igboezuru Aguleri	0.6109+9.1×10–5SO2+3.0×10–6NO2–2.7×10–5PM10
Okpu Aguleri	0.6575+2.52×10–3SO2–4.011×10–1NO2+3.6×10–4PM10
Umuokpoto Aguleri	0.6044–2.5×10–4SO2+1.9×10–5NO2+5.0×10–5PM10
Enugu Otu Aguleri	0.7385–2.0×10–4SO2–5.6×10–4NO2+3.6×10–5PM10
Ezi Agulu Otu Aguleri	0.6107–1.5×10–3SO2–8.5×10–5NO2+6.0×10–5PM10
Umundeze	0.6049–5.0×10–4SO2–3.7×10–5NO2
Amaeze	0.5944+1.2×10–4SO2–3.0×10–6NO2–3.3×10–5PM10

 

Table 7
Summary of Explicit Polynomials Obtained as Solutions (Aguleri, dry season)

Station	Pollution index model
Aguleri junction	0.6020–1.5×10–5SO2+8.5×10–6NO2+1.2×10–6PM10
Ifite Aguleri	0.4609+7.9×10–3SO2–6.1×10–6NO2+3.9×10–6PM10
Igboezuru Aguleri	0.6092+4.0×10–6SO2+1.4×10–5NO2–9.0×10–5PM10
Okpu Aguleri	0.6100–3.8×10–5SO2+6.0×10–6NO2–1.4×10–4PM10
Umuokpoto Aguleri	0.6053+7.2×10–5SO2+2.0×10–6NO2–5.0×10–6PM10
Enugu Otu Aguleri	0.7319+1.15×10–4SO2–2.5×10–5NO2–8.0×10–5PM10
Ezi Agulu Otu Aguleri	0.6090+1.4×10–4SO2+2.4×10–5NO2–8.0×10–5PM10
Umundeze	0.6054–7.4×10–4SO2–2.1×10–5NO2–5.0×10–5PM10
Amaeze	0.5869–5.8×10–5SO2–5.3×10–6NO2+1.25×10–4PM10

 

Table 8
k Values obtained as simulated linearisation of pollution indices (Aguleri, rainy season)

Station	K	k1	k2	k3
Aguleri junction	0.6023	–0.000094	0.000016	0.0000094
Ifite Aguleri	0.6039	0.000035	0.00000402	0.0000096
Igboezuru Aguleri	0.6109	0.000091	0.000003	–0.000027
Okpu	0.6575	0.00252	–0.4011	0.00036
Umuokpoto Aguleri	0.6044	–0.00025	0.000019	0.00005
Enugu Otu Aguleri	0.7385	–0.0002	–0.00056	0.000036
Ezi Agulu Otu Aguleri	0.6107	–0.0015	–0.000085	0.00006
Umundeze	0.6049	–0.0005	–0.000037	0
Amaeze	0.5944	0.00012	–0.000003	–0.000033

 

Table 9
k Values obtained as simulated linearisation of pollution indices (Aguleri, dry season)

Station	K	k1	k2	k3
Aguleri junction	0.6020	–0.000015	0.0000085	0.0000012
Ifite Aguleri	0.4609	0.0079	–0.0000061	0.0000039
Igboezuru Aguleri	0.6092	0.000004	0.000014	0.000009
Okpu Aguleri	0.6100	–0.000038	0.000006	–0.000014
Umuokpoto Aguleri	0.6053	0.000072	0.000002	–0.000005
Enugu Otu Aguleri	0.7319	0.000151	–0.000025	–0.00008
Ezi Agulu Out Aguleri	0.6090	0.00014	0.000024	–0.00008
Umundeze Aguleri	0.6054	–0.00074	–0.000021	–0.00005
Amaeze Aguleri	0.5869	–0.000058	–0.0000053	0.000125

 

 

 

 

 

 

 

In order to achieve this, the pollution indices were treated as objects in geographical space and location and their respective positions were monitored by the application of GIS/GPS contour mapping of concentration densities, as shown below.

Application of GIS/GPS Mapping of Concentration Densities of Pollutants

The concentration range of pollutants in Aguleri was 50%, as presented typically for Point 1 in Figure 8, Figure 9 and Figure 10. The corresponding three-dimensional (3D) surface plot in Figure 8 of the Aguleri junction was mononodal, and the area of very low NO₂ concentration was clearly shown in the colour scheme. The surface plot in Figure 10 for PM₁₀ at the same point in Aguleri was significant because the concentration of PM₁₀ was shown to be very low at 6.43ºN and 6.92E.

The difference between the two GIS plots of NO₂ was that the GIS plot of Figure 8 is two-dimensional, while the GIS surface plot of Figure 9 is 3D and gives a clearer view of the pollution vector density at the sampling stations for NO₂ for Aguleri at point 1 for the rainy season. It gives the view of real life.

Effect of pollutant characteristics as a function of meteorological parameters

Air pollutant concentrations as a function of meteorological parameters, as shown in Figure 11 and Figure 12, revealed that over 50% of relative humidity affects the dispersion (concentrations) of the selected pollutants in all the nine sampling stations, and the variations were in the order of  for the rainy season but  for the dry season.

 

 

 

 

The wind speed was observed to be very low (< 10 m/s) and had little or no impact on the selected pollutants.

However, the effect of wind speed was more pronounced in the dry season than in the rainy season. Relative humidity varies directly with elevation; therefore, lower elevation gives lower relative humidity, less dispersion, and higher pollutant concentrations.

Many meteorological parameters vary inversely with air pollutant concentrations (Anyika et al., 2018; Rahman et al., 2006)

 

 

 

AQI

The results of the AQI, as presented in Table 10 and Table 11, Figure 13 and Figure 14, show that all the locations in both the rainy and dry seasons were below a 50 AQI rating.

A cursory look at the air quality of the study locations using the rating by USEPA (2000) for determining ambient air quality in Table 3 showed that the AQI rating for all the stations in the Aguleri study area for both rainy and dry seasons was very good (A category) with the exception of Aguleri Junction and Ifite Aguleri which had an AQI rating of good (B category).

The good AQI ratings of two areas suggest that these areas have fewer anthropogenic activities, while the very good AQI ratings of other areas suggest these areas have very few anthropogenic activities from very few vehicles and the absence of industries.

The AQI rating of good indicates no general health effect on the general public, but extreme measures must be taken to avoid incidences of hazardous activities.

 

Table 10
Air quality index of Aguleri (rainy season)

Sampling Stations	Point 1	Point 2	Point 3	Point4	Description
Aguleri Junction	28.00	27.20	25.40	23.40	good
Ifite Aguleri	22.00	20.30	20.30	19.42	good
Igboezuru Aguleri	7.00	8.00	6.80	7.80	very good
Okpu Aguleri	5.00	6.00	5.60	6.60	very good
Umuokpoto Aguleri	3.00	5.00	5.50	5.45	very good
Enugu-Out Aguleri	3.50	4.00	4.00	4.40	very good
Ezi Agulu Out Aguleri	3.70	3.00	3.00	3.00	very good
Umundeze Aguleri	2.50	2.90	2.50	2.50	very good
Amaeze Aguleri	2.00	2.00	2.00	2.00	very good

 

 

Table 11
Air quality index of Aguleri (dry season)

Sampling Stations	Point 1	Point 2	Point 3	Point4	Description
Aguleri Junction	32.00	31.70	32.50	29.80	good
Ifite Aguleri	29.00	28.50	26.00	25.00	good
Igboezuru Aguleri	8.50	8.50	6.00	7.50	very good
Okpu Aguleri	6.50	6.50	6.00	6.90	very good
Umuokpoto Aguleri	6.50	6.50	5.60	6.60	very good
Enugu-Out Aguleri	3.50	3.50	2.00	2.50	very good
Ezi Agulu Out Aguleri	5.2	5.20	2.00	4.20	very good
Umundeze Aguleri	7.1	7.10	7.00	6.10	very good
Amaeze Aguleri	5.7	5.70	7.00	4.70	very good

 

 

CONCLUSIONS

This study has shown that the evaluation of pollution models generated from ab initio constants obtained as an average of all variations at a station in a linear summation was more reliable than constants from fitted curves, which were a function of a single component. It has been shown that GIS contour surface plots used to obtain air pollution characteristics on surfaces gave more reliable data than tabulated values.

This study reveals that GIS vector density plots for air pollution characteristics can be used to predict air pollution as a function of industrial clustering. The solution of model polynomials representing air pollution characteristics can be used to predict pollution attributes as a function of data space. The predictor constants generated by solving the model simultaneous equations using MATLAB 7.9 representing modifiers of air pollution were efficient. The study has demonstrated the predictive power of GIS/GPS in the rendering of air pollution in terms of objects in data space and their interaction with meteorological parameters. The meteorological variables like relative humidity serve as effective scavengers for SO₂, NO₂ and PM₁₀ pollutants and vary in both rainy and dry seasons. The information obtained from this study could lead to best environmental management practices and the establishment of efficient pollution control departments, as in many developed and advanced countries. However, some limitations encountered in this study were predominantly difficulties in the collection of the samples due to the hostility of youth in Aguleri and difficulties measuring with instruments and digital sensors from the various environmental agencies. In future air pollution assessments, this study recommends using more mathematical analysis involving polynomial equations as formulated, which should be performed by iteration.

 

Author Contributions: Conceptualization by ALC and OC; methodology by ALC; analysis by ALC; investigation by ALC; resources ALC; data curation by ALC and OC; writing by ALC and OC, review by OC. All authors declare that they have read and approved the publication of the manuscript in this present form.

Funding: There was no external funding for this study.

Acknowledgments: The authors deeply appreciate the assistance of the Department of Mechanical Engineering, Federal University of Technology Owerri for providing apparatuses used for this sophisticated study and the staff and management Imo State Environmental Protection Agency (ISEPA) for providing gas monitors.

Conflicts of Interest: The authors declare non-existence of any interest(s).

 

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Academic Editor: Dr. Iuliana MOTRESCU

 

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