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A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Author

Listed:
  • Jierui Dong

    (Sustainable Building Innovation Lab., School of Property, Construction and Project Management, RMIT University, Melbourne, VIC 3000, Australia
    HEAL National Research Network, Canberra, ACT 2601, Australia)

  • Nigel Goodman

    (Sustainable Building Innovation Lab., School of Property, Construction and Project Management, RMIT University, Melbourne, VIC 3000, Australia
    HEAL National Research Network, Canberra, ACT 2601, Australia
    National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT 2601, Australia)

  • Priyadarsini Rajagopalan

    (Sustainable Building Innovation Lab., School of Property, Construction and Project Management, RMIT University, Melbourne, VIC 3000, Australia
    HEAL National Research Network, Canberra, ACT 2601, Australia)

Abstract

Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM 2.5 and CO 2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

Suggested Citation

  • Jierui Dong & Nigel Goodman & Priyadarsini Rajagopalan, 2023. "A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools," IJERPH, MDPI, vol. 20(15), pages 1-18, July.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:15:p:6441-:d:1201906
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    References listed on IDEAS

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