Appraising raw exhaust pollutant gases emissions from industrial generators using statistics and machine learning approaches

Authors

  • O. Oderinde
    Department of Chemistry, Nile University of Nigeria, Abuja FCT 900001 Nigeria
  • C. L. Mgbechidinma
    School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China,
  • A. O. Agbeja
    Department of Chemistry, Faculty of Natural and Applied Sciences, Lead City University, Ibadan, Nigeria
  • A. A. Ajayi
    Department of Chemistry, Faculty of Natural and Applied Sciences, Lead City University, Ibadan, Nigeria
  • A. O. Ogundiran
    Department of Chemistry, Faculty of Natural and Applied Sciences, Lead City University, Ibadan, Nigeria
  • O. O. Olaide
    Department of Chemical and Pharmaceutical Chemistry, University of South Wales, 3, Wood Road, Treforest Pontypridd, CF37 1RG, United Kingdom
  • O. A. Orelaja
    Department of Mechanical Engineering, School of Engineering, Moshood Abiola Polytechnic Abeokuta Nigeria
  • C. A. Mgbechidimma
    Department of Computer Science, University of Ibadan, Ibadan, Nigeria
  • C. O. Ajanaku
    Department of Chemistry, Landmark University, PMB 1001, Omu Aran, Nigeria
  • K. D. Oyeyemi
    Applied Geophysics Unit, Department of Physics, Covenant University, PMB 1023, Ota, Nigeria

Keywords:

Industrial emissions, Exhaust gases, Statistical modelling, Machine Learning

Abstract

Industrial generators, widely used for backup power generation, emit significant levels of pollutant gases such as carbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (HC), and nitrogen oxides (NOx). These emissions exacerbate air pollution and climate change, while their inhalation adversely impacts human health, leading to respiratory/cardiovascular diseases and increased mortality rates. Raw exhausts of CO, CO2, HC, NOx, and O2 from industrial generators were assessed using a portable analyser. Thereafter, the obtained dataset was analysed using multiple linear regression and Pearson’s correlation to quantify the synergistic impact of generator characteristics, while the study equally trained 70% of the dataset using machine learning (ML) classification models. The result showed that generators’ age and capacity impacted considerably on exhaust concentrations as the diesel-powered generators exhibited higher CO2 and NOx emissions at 76.1% and 7393ppm, respectively, compared to gas-powered generators. For diesel-powered generators, there was a moderate negative correlation at -0.49142 and p-value of 0.03281 for CO and NOx. For the gas-powered generators, the correlation is statistically significant for CO and HC, while there was an inverse association between NOx and O2. The employed ML models achieved high prediction accuracy range of 80.6?93.5 % for exhaust pollutant gases for OGEPA classification status. Based on this study, policy frameworks should be implemented up to impose stringent generator emissions standards to reduce air pollution, invest in expanding/upgrading the national electricity grid to reduce reliance, provide low-interest green loans to finance renewable energy systems, as well as access climate finance mechanisms to subsidise clean energy projects.

Dimensions

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Published

2025-08-18

How to Cite

Appraising raw exhaust pollutant gases emissions from industrial generators using statistics and machine learning approaches. (2025). Journal of the Nigerian Society of Physical Sciences, 7(4), 2725. https://doi.org/10.46481/jnsps.2025.2725

Issue

Volume 7, Issue 4, November 2025

Section

Earth Sciences
Copyright & Licensing

Copyright (c) 2025 O. Oderinde, C. O. Ajanaku, C. A. Mgbechidimma, O. A. Orelaja, O. O. Olaide, A. O. Ogundiran, A. A. Ajayi, A. O. Agbeja, C. L. Mgbechidinma, K. D. Oyeyemi (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Appraising raw exhaust pollutant gases emissions from industrial generators using statistics and machine learning approaches. (2025). Journal of the Nigerian Society of Physical Sciences, 7(4), 2725. https://doi.org/10.46481/jnsps.2025.2725

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