2025, Vol. 6, Issue 2, Part D
Modelling vehicular exhaust emissions and their environmental impacts using machine learning in Ibadan, Nigeria
Author(s): Adebobola Ololade Agbeja, Olumuyiwa Olufisayo Ogunlaja, Olayinka Oderinde, Adetola Abiola Ajayi, Olubunmi Ayoni Ogundiran, Khairat Abiola Olaifa and Oyedoyin Bukola Oyedeji
Abstract: Ibadan faces growing air pollution due to increased vehicle use, old tokunbo cars with poor emission controls. These Diesel truck vehicles emit harmful gases, impacting urban air quality and health risks. This study investigated vehicular exhaust emissions and their environmental implications across nine major transport hubs in Ibadan, Southwest Nigeria. Using a handheld Kane 5-gas analyzer, real-time measurements were taken from 1,034 diesel-powered trucks to assess the concentrations of carbon monoxide (CO), carbon dioxide (CO₂), oxygen (O₂), and nitrogen oxides (NOₓ). The study employed descriptive and inferential statistical analyses, Euro IV compliance evaluation, and machine learning classification models to analyze emission patterns and regulatory conformity. Satellite-derived atmospheric parameters from the Atmospheric Infrared Sounder (AIRS) were integrated to explore the relationship between emissions and climatic variables. Results revealed that Iwo Road recorded the highest mean CO and CO₂ concentrations (0.24% and 2.33%, respectively), while Toll-Gate exhibited the highest NOₓ mean level (463.3 ppm). The Random Forest, k-Nearest Neighbors, and Neural Network models demonstrated superior predictive accuracy for emission compliance classification (AUC = 1.000). These findings highlight the substantial influence of vehicular emissions on local atmospheric dynamics and climate processes. The study underscores the urgent need for stringent emission regulations, clean transport technologies, and integrated air quality management strategies to mitigate urban air pollution and climate risks.
DOI: 10.22271/reschem.2025.v6.i2c.237
Pages: 282-286 | Views: 72 | Downloads: 35
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How to cite this article:
Adebobola Ololade Agbeja, Olumuyiwa Olufisayo Ogunlaja, Olayinka Oderinde, Adetola Abiola Ajayi, Olubunmi Ayoni Ogundiran, Khairat Abiola Olaifa, Oyedoyin Bukola Oyedeji. Modelling vehicular exhaust emissions and their environmental impacts using machine learning in Ibadan, Nigeria. J Res Chem 2025;6(2):282-286. DOI: 10.22271/reschem.2025.v6.i2c.237
