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Machine learning and statistical models for predicting indoor air quality.

Wenjuan Wei1, Olivier Ramalho1, Laeticia Malingre1

  • 1Scientific and Technical Center for Building (CSTB), Health and Comfort Department, French Indoor Air Quality Observatory (OQAI), University of Paris-Est, Marne la Vallée Cedex 2, France.

Indoor Air
|June 21, 2019
PubMed
Summary

Statistical models show great potential for predicting indoor air quality (IAQ) in occupied spaces, unlike traditional mechanistic models. This review highlights the growing use of machine learning and statistical methods for IAQ assessment.

Keywords:
IAQartificial neural networksdata miningpartial least squaresparticulate matterregression

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Area of Science:

  • Environmental Science
  • Building Science
  • Data Science

Background:

  • Indoor air quality (IAQ) is crucial for health and is typically modeled using mechanistic or statistical approaches.
  • Statistical models offer advantages for analyzing IAQ in real-world, occupied environments and large datasets.
  • Mechanistic models are often limited to unoccupied or simulated scenarios.

Purpose of the Study:

  • To conduct the first literature review on the application of statistical models for predicting indoor air quality (IAQ).
  • To identify and discuss commonly used statistical modeling methods, their strengths, and weaknesses in the context of IAQ.
  • To highlight the recent emergence and trends in statistical modeling for IAQ assessment.

Main Methods:

  • Systematic literature search to identify studies applying statistical models to predict IAQ.
  • Review and synthesis of 37 publications from the past decade.
  • Analysis of the most frequently studied indoor air pollutants and statistical modeling techniques.

Main Results:

  • Thirty-seven relevant publications were identified, all published within the last 10 years.
  • Particulate matter (PM2.5 and PM10) concentrations were the most studied IAQ parameters.
  • Artificial neural networks, multiple linear regression, partial least squares, and decision trees were the most popular statistical models used.

Conclusions:

  • Statistical modeling, including machine learning, is a rapidly growing field for IAQ prediction.
  • These models are increasingly applied to real-world and occupied environments, offering valuable insights.
  • The review underscores the shift towards data-driven approaches in understanding and managing indoor air quality.