^{1(a, b)}Department of Physics, University of Calabar, Calabar, Nigeria.

Authors Contact Details: E-mail Addresses ✉: a = [soudo95@yahoo.com]; b = [chineloonwuka2007@yahoo.com]

^{2(a, b)*}Directorate of strategy, research and statistics, Maritime Academy of Nigeria, Oron, Akwa Ibom State, Nigeria.

Authors Contact Details: E-mail Addresses ✉: = a: [mfonslago@yahoo.com; mfonslago20@gmail.com; Mobile Phone no ☎: +234(0) 8023719490]; b: udohevans@gmail.com

³Department of Physics, Cross River State University of Technology, Calabar, Nigeria. P.M.B. 1123 Calabar, Cross River State, Nigeria.

Authors’ Contact Detail: E-mail Address ✉: steveewona2007@yahoo.com

*Corresponding Author

Accepted September 27, 2020

The present investigation was carried out for the Characterization of Sulphur dioxide (SO₂) in Calabar, Nigeria. The measured concentration of SO₂ as recorded in this research is above the World Health Organization (WHO) standard. For air quality index, Marina Resort station was found to be “moderately polluted” with AQI of 157.20. The remaining five (5) stations were classified as “poor” with the AQI ranging from 214 to 229. Basin Authority showed the highest positive correlation of SO₂ and relative humidity, with R = 0.753. This shows that at Basin Authority station, relative humidity had the greatest effect on SO₂. The highest correlation between SO₂ and wind speed was obtained at basin authority with R = 0.609. Wind speed also had effect on SO₂ in that station. Marina resort recorded the highest negative correlation of SO₂ and temperature, with R = -0.643. Basin Authority recorded the highest positive correlation with R = 0.631. Hence the best model for the prediction of SO₂ for Calabar is 5 – 10 – 1. This translates to, five (5) input variables that include; SO₂, wind speed, temperature, relative humidity and total suspended particulate matter (TSP), ten (10) hidden neurons in the neural network and one (1) target variable which is SO₂.

Key words: Sulphur dioxide, relative humidity, correlation, neural network.

Full Text PDF (365 KB)