Proof-of-Concept Forecasting of Indoor Carbon Dioxide (〖CO〗_2) concentrations in a Small Industrial Bakery Using ARIMA

Year 2026, Volume: 10 Issue: 1, 62 - 68, 12.03.2026

Gbadebo İsmaila Olatona , Sherifdeen Mosebolatan Oyedokun , Shuaib Adisa

https://doi.org/10.34110/forecasting.1807051

https://izlik.org/JA85CJ46MH

Abstract

This study investigates short-term forecasting of indoor air quality in a small industrial environment, using a bakery as a case study. An AutoRegressive Integrated Moving Average (ARIMA) (5,1,0) model was applied to predict variations in carbon dioxide (CO₂) concentrations, a key proxy indicator for ventilation efficiency and occupational exposure, which was monitored over 57 days and modelled to capture short-term fluctuations. The model effectively captured temporal dependencies, with significant autoregressive terms (p < 0.05) highlighting the influence of past values on short-term forecasts. However, substantial unexplained variability was observed, suggesting that external drivers such as ventilation, operational schedules, and equipment use play a critical role in shaping indoor air quality. Although ARIMA is a well-established forecasting method, this study demonstrates its value as a low-cost, data-efficient tool for small industries in resource-limited settings where complex monitoring systems and large datasets may be unavailable. By forecasting CO₂ dynamics, facility operators can anticipate periods of elevated indoor exposure and adjust ventilation strategies to mitigate risks, thereby supporting worker health, safety, and productivity. The findings provide a proof-of-concept for integrating classical time-series forecasting into occupational environmental management. However, future research should extend this approach to multivariate or hybrid ARIMA–machine learning models, incorporate additional pollutants with direct public health implications e.g., PMs, NO₂, temperature, and relative humidity, then validate across multiple industrial contexts. This would enhance generalizability and strengthen the role of forecasting tools in informing ventilation policy, exposure mitigation, and regulatory compliance in small-scale industrial environments.

Keywords

Indoor air quality , Carbon dioxide , ARIMA , Occupational health , Bakery

References

• Akaike, H. (1978). On the likelihood of a time series model. Journal of the Royal Statistical Society: Series D (The Statistician), 27(3-4), pp.217-235.
• Anderson, D.R. and Burnham, K.P. (2002). Avoiding pitfalls when using information-theoretic methods. The Journal of Wildlife Management, pp.912-918.
• Anderson, D.R., Burnham, K.P. and White, G.C. (1994). AIC model selection in overdispersed capture‐recapture data. Ecology, 75(6), pp.1780-1793.
• Bawa, J.A., (2023). Evaluation of the Indoor Air Quality in the Production Area of Pharmaceutical Factory Buildings in Southwest Nigeria. TWIST, 18(4), pp.37-41.
• Box, G.E.P. and Jenkins, G.M. (1994). Time Series Analysis: Forecasting and Control. (5th ed.). John Wiley & Sons.
• Chakrabarti, A. and Ghosh, J.K. (2011). AIC, BIC and recent advances in model selection. Philosophy of statistics, pp.583-605.
• Chatfield, C. (2016). The analysis of time series: An introduction (6th ed.). Chapman and Hall/CRC.
• Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R. and Feigin, V. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), pp.1907-1918.
• Díaz-Robles, L.A., Ortega, J.C., Fu, J.S., Reed, G.D., Chow, J.C., Watson, J.G. and Moncada-Herrera, J.A. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42(35), pp.8331-8340.
• Drastyana, S.F. and Uktutias, S.A.M., (2021). Risk assessment of exposure to carbon monoxide in a residential area around tofu manufacturing. EEA. (2020). Air quality in Europe — 2020 report. Publications Office of the EU.
• Holmes, N.S. and Morawska, L., 2006. A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available. Atmospheric environment, 40(30), pp.5902-5928.
• Hyndman, R.J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
• Kajtár, L. and Herczeg, L., 2012. Influence of carbon-dioxide concentration on human well-being and intensity of mental work. QJ Hung. Meteorol. Serv, 116(2), pp.145-169.
• Kaur, J., Parmar, K.S. and Singh, S. (2023). Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research, 30(8), pp.19617-19641.
• Kumar, P., Pratap, V., Kumar, A., Choudhary, A., Prasad, R., Shukla, A., Singh, R.P. and Singh, A.K. (2020). Assessment of atmospheric aerosols over Varanasi: physical, optical and chemical properties and meteorological implications. Journal of Atmospheric and Solar-Terrestrial Physics, 209, p.105424.
• Lee, S.A., Liao, C.H. and Lin, T.Y. (2018). Size-selective Assessment of Respirator Protection against Airborne Fungi and (1→ 3)-β-D-glucan in Farms. Aerosol and Air Quality Research, 18(5), pp.1270-1281.
• Listyarini, S., Warlina, L. and Sambas, A., 2022. The Air Quality Monitoring Tool Based on Internet of Things to Monitor Pollution Emissions Continuously. Environment and Ecology Research, 10(6), pp.824-829.
• Liu, X. and Guo, H., (2022). Air quality indicators and AQI prediction coupling long-short term memory (LSTM) and sparrow search algorithm (SSA): A case study of Shanghai. Atmospheric Pollution Research, 13(10), p.101551.
• Manisalidis, I., Stavropoulou, E., Stavropoulos, A. and Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: a review. Frontiers in public health, 8, p.14.
• Ouyang, W., Gao, B., Cheng, H., Hao, Z. and Wu, N. (2018). Exposure inequality assessment for PM2. 5 and the potential association with environmental health in Beijing. Science of the Total Environment, 635, pp.769-778.
• Seinfeld, J.H. and Pandis, S.N. (2016). Atmospheric chemistry and physics: from air pollution to climate change. John Wiley & Sons
• Siew, L.Y., Chin, L.Y. and Wee, P.M.J. (2008). ARIMA and integrated ARFIMA models for forecasting the air pollution index in Shah Alam, Selangor. Malaysian Journal of Analytical Sciences, 12(1), pp.257-263.
• Skön, J., Johansson, M., Raatikainen, M., Leiviskä, K. and Kolehmainen, M. (2012). Modelling indoor air carbon dioxide (CO2) concentration using neural network. methods, 14(15), p.16.
• Sun, J., Shen, Z., Zhang, Y., Zhang, Q., Lei, Y., Huang, Y., Niu, X., Xu, H., Cao, J., Ho, S.S.H. and Li, X. (2019).
• Characterisation of PM2. 5 source profiles from typical biomass burning of maize straw, wheat straw, wood branch, and their processed products (briquette and charcoal) in China. Atmospheric Environment, 205, pp.36-45.
• Wagenmakers, E.J. and Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic bulletin & review, 11, pp.192-196.
• Wang, D., Chen, G., Song, C., Liu, Y., He, W., Zeng, T. and Liu, J. (2019). Experimental study on the coupling effect of indoor air temperature and radiant temperature on human thermal comfort in a non-uniform thermal environment. Building and Environment, 165, p.106387.
• WHO. (2021). Ambient (outdoor) air pollution. [Online]. Available at: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
• Xu, D., Zhang, Q., Ding, Y. and Zhang, D. (2022). Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting. Environmental Science and Pollution Research, 29(3), pp.4128-4144.
• Yin, W., Yuan, Y., Chen, F., Wang, H., Qiao, L., Chen, T., Cheng, H., Xu, X., Chen, C., Liu, W. and Li, Z. (2022). High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater. Journal of Cleaner Production, 341, p.130875.
• Zhao, Q., Yu, P., Mahendran, R., Huang, W., Gao, Y., Yang, Z., Ye, T., Wen, B., Wu, Y., Li, S. and Guo, Y., (2022). Global climate change and human health: Pathways and possible solutions. Eco-Environment & Health, 1(2), pp.53-62.You can use the link below for APA style examples.