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

[1] D. Akal, S. Öztuna & M. K. Büyükak?n, “A review of hydrogen usage in internal combustion engines (gasoline-Lpg-diesel) from combustion performance aspect”, International Journal of Hydrogen Energy 45 (2020) 35257. https://doi.org/10.1016/j.ijhydene.2020.02.001. DOI: https://doi.org/10.1016/j.ijhydene.2020.02.001

[2] C. S. Sriharikota, K. Karuppasamy, V. Nagarajan, R. Sathyamurthy, B. Ramani, V. Muthu & S. Karuppiah, “Experimental investigation of the emission and performance characteristics of a DI diesel engine fueled with the vachellia nilotica seed oil methyl ester and diesel blends”, ACS Omega 6 (2021) 14068. https://doi.org/10.1021/acsomega.1c00437. DOI: https://doi.org/10.1021/acsomega.1c00437

[3] I. Abrar, T. Arora & R. Khandelwal, “Bioalcohols as an alternative fuel for transportation: Cradle to grave analysis”, Fuel Processing Technology 242 (2023) 107646. https://doi.org/10.1016/j.fuproc.2022.107646. DOI: https://doi.org/10.1016/j.fuproc.2022.107646

[4] A. Zerboni, T. Rossi, R. Bengalli, T. Catelani, C. Rizzi, M. Priola, S. Casadei & P. Mantecca, “Diesel exhaust particulate emissions and in vitro toxicity from Euro 3 and Euro 6 vehicles”, Environmental Pollution 297 (2022) 118767. https://doi.org/10.1016/j.envpol.2021.118767. DOI: https://doi.org/10.1016/j.envpol.2021.118767

[5] D. E. Schraufnagel, “The health effects of ultrafine particles”, Experimental & Molecular Medicine 52 (2020) 311. https://doi.org/10.1038/s12276-020-0403-3. DOI: https://doi.org/10.1038/s12276-020-0403-3

[6] S. Steiner, C. Bisig, A. Petri-Fink & B. Rothen-Rutishauser, “Diesel exhaust: current knowledge of adverse effects and underlying cellular mechanisms”, Archives of Toxicology 90 (2016) 1541. https://doi.org/10.1007/s00204-016-1736-5. DOI: https://doi.org/10.1007/s00204-016-1736-5

[7] W. A. Khan, F. Sharif, M. F. Khokhar, L. Shahzad, N. Ehsan & M. Jahanzaib, “Monitoring of ambient air quality patterns and assessment of air pollutants’ correlation and effects on ambient air quality of Lahore, Pakistan”, Atmosphere 14 (2023) 1257. https://doi.org/10.3390/atmos14081257. DOI: https://doi.org/10.3390/atmos14081257

[8] H. L. Brumberg & C. J. Karr, “Ambient air pollution: Health hazards to children”, Pediatrics 147 (2021) e2021051484. https://doi.org/10.1542/peds.2021-051484. DOI: https://doi.org/10.1542/peds.2021-051484

[9] J. Moya, L. Phillips, L. Schuda, P. Wood, A. Diaz, R. Lee, R. Clickner, R. J. Birch, N. Adjei, P. Blood, K. Chapman, R. de Castro & K. Mahaffey, “EPA’s exposure factors handbook (EFH)” (USEPA, 2011). [Online]. https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://rais.ornl.gov/documents/EFH_2011.pdf.

[10] M. Guarnieri & J. R. Balmes, “Outdoor air pollution and asthma”, The Lancet 383 (2014) 1581. https://doi.org/10.1016/S0140-6736(14)60617-6. DOI: https://doi.org/10.1016/S0140-6736(14)60617-6

[11] C. F. Oguejiofor, U. U. Eze, I. G. Eke, A. A. Eze, O. B. Onyejekwe & B. M. Anene, “Adverse effects of exposure to petrol-generator exhaust fumes on the reproductive hormones, testis and spermatozoa in male dogs”, Reproductive Toxicology 123 (2024) 108516. https://doi.org/10.1016/j.reprotox.2023.108516. DOI: https://doi.org/10.1016/j.reprotox.2023.108516

[12] J. Valente, C. Pimentel, R. Tavares, J. Ferreira, C. Borrego, P. Carreiro-Martins, I. Caires, N. Neuparth & M. Lopes, “Individual exposure to air pollutants in a Portuguese urban industrialized area”, Journal of Toxicology and Environmental Health - Part A: Current Issues 77 (2014) 888. https://doi.org/10.1080/15287394.2014.910159. DOI: https://doi.org/10.1080/15287394.2014.910159

[13] P. Rossner, T. Cervena & M. Vojtisek-Lom, “In vitro exposure to complete engine emissions – a mini-review”, Toxicology 462 (2021) 152953. https://doi.org/10.1016/j.tox.2021.152953. DOI: https://doi.org/10.1016/j.tox.2021.152953

[14] A. Iqubal, M. Ahmed, S. Ahmad, C. R. Sahoo, M. K. Iqubal & S. E. Haque, “Environmental neurotoxic pollutants: review”, Environmental Science and Pollution Research 27 (2020) 41175. https://doi.org/10.1007/s11356-020-10539-z. DOI: https://doi.org/10.1007/s11356-020-10539-z

[15] L. Müller, J. Usemann, M. P. Alves & P. Latzin, “Diesel exposure increases susceptibility of primary human nasal epithelial cells to rhinovirus infection”, Physiological Reports 9 (2021) e14994. https://doi.org/10.14814/phy2.14994. DOI: https://doi.org/10.14814/phy2.14994

[16] G. Y. Kim, I. Jung, M. Park, K. Park, S. H. Lee & W. H. Kim, “Diesel exhaust particles induce human umbilical vein endothelial cells apoptosis by accumulation of autophagosomes and caspase-8 activation”, Scientific Reports 12 (2022) 16492. https://doi.org/10.1038/s41598-022-21044-3. DOI: https://doi.org/10.1038/s41598-022-21044-3

[17] X. Wu, C. Ciminieri, I. S. T. Bos, M. E. Woest, A. D’Ambrosi, R. Wardenaar, D. C. J. Spierings, M. Königshoff, M. Schmidt, L. E. M. Kistemaker & R. Gosens, “Diesel exhaust particles distort lung epithelial progenitors and their fibroblast niche”, Environmental Pollution 305 (2022) 119292. https://doi.org/10.1016/j.envpol.2022.119292. DOI: https://doi.org/10.1016/j.envpol.2022.119292

[18] Y. Sun, F. Bochmann, A. Nold & M. Mattenklott, “Diesel exhaust exposure and the risk of lung cancer-a review of the epidemiological evidence”, International Journal of Environmental Research and Public Health 11 (2014) 1312. https://doi.org/10.3390/ijerph110201312. DOI: https://doi.org/10.3390/ijerph110201312

[19] E. Long, C. Schwartz & C. Carlsten, “Controlled human exposure to diesel exhaust: a method for understanding health effects of traffic-related air pollution”, Particle and Fibre Toxicology 19 (2022) 15. https://doi.org/10.1186/s12989-022-00454-1. DOI: https://doi.org/10.1186/s12989-022-00454-1

[20] L. P. Wong, H. Alias, N. Aghamohammadi, A. Ghadimi & N. M. N. Sulaiman, “Control measures and health effects of air pollution: A survey among public transportation commuters in Malaysia”, Sustainability (Switzerland) 9 (2017) 1616. https://doi.org/10.3390/su9091616. DOI: https://doi.org/10.3390/su9091616

[21] P. E. Rosenfeld, K. R. Spaeth, R. Hallman, R. Bressler & G. C. Smith, “Cancer risk and diesel exhaust exposure among railroad workers”, Water, Air, and Soil Pollution 233 (2022) 171. https://doi.org/10.1007/s11270-022-05651-4. DOI: https://doi.org/10.1007/s11270-022-05651-4

[22] I. O. Abramova, “The population of Africa under the conditions of transformation of the world order”, Herald of the Russian Academy of Sciences 92 (2022) S1306. https://doi.org/10.1134/S0018151X21040131. DOI: https://doi.org/10.1134/S1019331622200023

[23] O. Rosnes & H. Vennemo, “The cost of providing electricity to Africa”, Energy Economics 34 (2012) 1318. https://doi.org/10.1016/j.eneco.2012.06.008. DOI: https://doi.org/10.1016/j.eneco.2012.06.008

[24] R. O. Adesola, E. Opuni, I. Idris, O. J. Okesanya, O. Igwe, M. D. Abdulazeez & D. E. Lucero-Prisno, “Navigating Nigeria’s health landscape: population growth and its health implications”, Environmental Health Insights 18 (2024). https://doi.org/10.1177/11786302241250211. DOI: https://doi.org/10.1177/11786302241250211

[25] “Nigeria - country commercial guide- electricity”, Power Systems and Renewable Energy. International Trade Administration, (2023). [Online]. https://www.trade.gov/country-commercial-guides/electricity-power-systems-and-renewable-energy.

[26] Ministry of Power. [Online]. Available at https://power.gov.ng/.

[27] M. Izuaka, “Nigeria suffers power outage as grid collapses”, Premium Times, (2024). [Online]. https://www.premiumtimesng.com/news/top-news/760693-nigeria-suffers-power-outage-as-grid-collapses.html.

[28] “National power grid collapses again, 12th time this year alone”, This Day, (2024). [Online]. https://www.thisdaylive.com/index.php/2024/12/12/national-power-grid-collapses-again-12th-time-this-year-alone/#google_vignette.

[29] D. Olawin, D. Aina, C. Azubuike, A. Amshi & R. Ede, “Nationwide blackout as grid suffers 10th collapse in 2024”, Punch Newspaper, (2024). [Online]. https://punchng.com/nationwide-blackout-as-grid-suffers-10th-collapse-in-2024/.

[30] Shivakumar, P. Srinivasa Pai & B. R. Shrinivasa Rao, “Artificial neural network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings”, Applied Energy 88 (2011) 2344. https://doi.org/10.1016/j.apenergy.2010.12.030. DOI: https://doi.org/10.1016/j.apenergy.2010.12.030

[31] J. Liu, Q. Huang, C. Ulishney & C. E. Dumitrescu, “Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine”, Applied Energy 300 (2021) 117413. https://doi.org/10.1016/j.apenergy.2021.117413. DOI: https://doi.org/10.1016/j.apenergy.2021.117413

[32] B. Ochieng, F. Onyango, P. Kuria, M. Wanjiru, B. Maake & M. Awuor, “AI-driven carbon emissions ]tracking and mitigation model”, 2024 IST-Africa Conf. (IEEE, 2024), pp. 1. https://doi.org/10.23919/IST-Africa63983.2024.10569242. DOI: https://doi.org/10.23919/IST-Africa63983.2024.10569242

[33] T. F. Yusaf, D. R. Buttsworth, K. H. Saleh & B. F. Yousif, “CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network”, Applied Energy 87 (2010) 1661. https://doi.org/10.1016/j.apenergy.2009.10.009. DOI: https://doi.org/10.1016/j.apenergy.2009.10.009

[34] T. Van Hung, H. H. Alkhamis, A. F. Alrefaei, Y. Sohret & K. Brindhadevi, “Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks”, Applied Nanoscience 13 (2023) 433. https://doi.org/10.1007/s13204-021-01781-z. DOI: https://doi.org/10.1007/s13204-021-01781-z

[35] M. B. Sanjeevannavar, N. R. Banapurmath, V. D. Kumar, A. M. Sajjan, I. A. Badruddin, C. Vadlamudi, S. Krishnappa, S. Kamangar, R. U. Baig & T. M. Y. Khan, “Machine learning prediction and optimization of performance and emissions characteristics of IC Engine”, 15 (2023) 13825. https://doi.org/10.3390/su151813825. DOI: https://doi.org/10.3390/su151813825

[36] P. Ganesan, S. Rajakarunakaran, M. Thirugnanasambandam & D. Devaraj, “Artificial neural network model to predict the diesel electric generator performance and exhaust emissions”, Energy 83 (2015) 115.

https://doi.org/10.1016/j.energy.2015.02.094. DOI: https://doi.org/10.1016/j.energy.2015.02.094

[37] K. Zeinalipour, L. Barriere, D. Ghelardi & M. Gori, “Application of machine learning models for carbon monoxide and nitrogen oxides emission prediction in gas turbines”, arXiv, (2025). [Online]. https://doi.org/https://doi.org/10.48550/arXiv.2501.17865.

[38] R. Potts, R. Hackney & G. Leontidis, “Tabular machine learning methods for predicting gas turbine emissions”, Machine Learning and Knowledge Extraction 5 (2023) 1055. https://doi.org/10.3390/make5030055. DOI: https://doi.org/10.3390/make5030055

[39] S. Moonsammy, T. D. T. T. Oyedotun, O. Oderinde, M. Durojaiye & A. Durojaye, “Exhaust determination and air-to-fuel ratio performance of end-of-life vehicles in a developing African country: a case study of Nigeria”, Transportation Research Part D: Transport and Environment 91 (2021) 102705. https://doi.org/10.1016/j.trd.2021.102705. DOI: https://doi.org/10.1016/j.trd.2021.102705

[40] P. K. Alimo, G. Lartey-Young, S. Agyeman, T. Y. Akintunde, E. Kyere-Gyeabour, F. Krampah, A. Awomuti, O. Oderinde, A. O. Agbeja & O. G. Afolabi, “Spatial distribution and policy implications of the exhaust emissions of two-stroke motorcycle taxis: a case study of southwestern state in Nigeria”, Journal of the Nigerian Society of Physical Sciences 6 (2024) 1898. https://doi.org/10.46481/jnsps.2024.1898. DOI: https://doi.org/10.46481/jnsps.2024.1898

[41] T. M. Mitchell, “Does machine learning really work?”, AI Magazine 18 (1997) 5. https://doi.org/10.1609/aimag.v18i3.1303.

[42] “MATLAB and Statistics Toolbox Version: 24.1.7 (R2024a)”, The MathWorks, Inc., Natick, Massachusetts, United States, (2024). [Online]. https://www.mathworks.com.

[43] A. Re?ito?lu, K. Altini?ik & A. Keskin, “The pollutant emissions from diesel-engine vehicles and exhaust aftertreatment systems”, Clean Technologies and Environmental Policy 17 (2015) 15. https://doi.org/10.1007/s10098-014-0793-9. DOI: https://doi.org/10.1007/s10098-014-0793-9

[44] Ogun State Environmental Protection Agency (OGEPA). [Online]. Available ar https://ogepa.og.gov.ng/.

[45] P. Brijesh & S. Sreedhara, “Exhaust emissions and its control methods in compression ignition engines: a review”, International Journal of Automotive Technology 14 (2013) 195. https://doi.org/10.1007/s12239-013-0022-2. DOI: https://doi.org/10.1007/s12239-013-0022-2

[46] Q. Malé, N. Barléon, S. Shcherbanev, B. Dharmaputra & N. Noiray, “Numerical study of nitrogen oxides chemistry during plasma assisted combustion in a sequential combustor”, Combustion and Flame 260 (2024) 113206. https://doi.org/10.1016/j.combustflame.2023.113206. DOI: https://doi.org/10.1016/j.combustflame.2023.113206

[47] V. E. Kozlov & N. S. Titova, “Simulation of the emission of nitrogen and carbon oxides during the turbulent combustion of a partially mixed methane–air mixture”, High Temperature 60 (2022) S67. https://doi.org/10.1134/S0018151X21040131. DOI: https://doi.org/10.1134/S0018151X21040131

[48] G. M. Hasan Shahariar, M. Sajjad, K. A. Suara, M. I. Jahirul, T. Chu-Van, Z. Ristovski, R. J. Brown & T. A. Bodisco, “On-road CO2 and NOx emissions of a diesel vehicle in urban traffic”, Transportation Research Part D: Transport and Environment 107 (2022) 103326. https://doi.org/10.1016/j.trd.2022.103326. DOI: https://doi.org/10.1016/j.trd.2022.103326

[49] X. Qin, P. Xie & C. Liao, “Study on the synergistic effect of NOx and CO2 emission reduction in the industrial sector of Guangzhou”, Frontiers in Environmental Science 13 (2025) 1497121. https://doi.org/10.3389/fenvs.2025.1497121. DOI: https://doi.org/10.3389/fenvs.2025.1497121

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 (In Progress)

Section

Earth Sciences
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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)

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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