Abstract:
:Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM2.5 and PM10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.
journal_name
Indoor Airjournal_title
Indoor airauthors
Wei W,Ramalho O,Malingre L,Sivanantham S,Little JC,Mandin Cdoi
10.1111/ina.12580subject
Has Abstractpub_date
2019-09-01 00:00:00pages
704-726issue
5eissn
0905-6947issn
1600-0668journal_volume
29pub_type
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