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.
Int J Environ Res Public Health. 2023 Jul 25;20(15):6441. doi: 10.3390/ijerph20156441.
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.
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.
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 and CO 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.
This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.
学校室内空气质量 (IAQ) 会影响居住者，尤其是儿童的表现和健康。在 COVID-19 大流行和丛林大火期间，公众对室内空气质量的关注度增加，推动了数据驱动模型（如人工神经网络 (ANNs)）的发展和应用，这些模型可用于预测污染物和室内暴露水平。
本综述总结了室内空气污染物 (IAP) 的类型和来源以及室内空气质量的指标。随后，系统评估了 ANN 作为学校室内空气质量预测模型，包括预测神经网络算法和建模过程。文章选择和纳入的方法遵循系统的四步流程：识别、筛选、资格和纳入。
经过筛选和选择，本综述纳入了 9 篇预测性论文。传统的人工神经网络使用最频繁，而递归神经网络 (RNN) 模型更好地分析了室内空气质量等时间序列问题。同时，目前的预测研究主要集中在使用室内 PM 和 CO 浓度作为学校输出变量，并未涵盖常见空气污染物。尽管研究强调了学校建筑参数和占用参数对室内空气质量的影响，但很难将其纳入预测模型。
本综述介绍了室内空气质量预测模型的现状，并确定了学校预测模型的局限性和未来研究方向。
