Machine learning and statistical models for predicting indoor air quality

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 (PM 2.5 and PM 10 ) 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. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

Automating the interpretation of PM 2.5 time‐resolved measurements using a data‐driven approach

“…24 While significant, these applications do not explore how to identity periods of emissions or differentiate between indoor and outdoor sources. 25 We refer the reader to 21,26,27 for a broad description of the approach. Here, we provide an overview.…”

“…However, only a few applications have been reported in IAQ studies for predicting indoor PM 2.5 concentration 22,23 and indoor radon 24 . While significant, these applications do not explore how to identity periods of emissions or differentiate between indoor and outdoor sources 25 …”

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

“…Indoor air pollutant concentrations may be estimated using an indoor-outdoor (I/O) ratio, indoor air quality model, a statistical model, and artificial intelligence such as machine learning [51,52]. The I/O ratio may generally be used to estimate the concentration of indoor air pollutants [47].…”

Indoor air quality prediction systems for smart environments: A systematic review

“…On the other hand, AI-based prediction systems can deliver prior information about upcoming critical changes in IAQ levels. Hence, building occupants can take preventive majors to avoid serious health impacts [5,125]. The research communities from the past years are exploring the potential of AI to design intelligent environments where building occupants get automatic, real-time updates about changing environmental conditions [24,95,135].…”