4.7 Article

An experiment-based impulse response method to characterize airborne pollutant sources in a scaled multi-zone building

Journal

ATMOSPHERIC ENVIRONMENT
Volume 251, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2021.118272

Keywords

Indoor air; Source identification; Multi-zone model; Inverse problem; Airborne transmission

Funding

  1. China National Key R&D Program `Energy-saving design and key technical equipment development for clean air-conditioning plants' [2018YFC0705201]
  2. National Natural Science Foundation of China [51708286]
  3. China Postdoctoral Science Foundation [2019M651818]

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This study successfully established a method for estimating source terms in multi-zone buildings using experiment-based impulse response, and conducted uncertainty analysis by combining errors from sensors and systematic responses.
Promptly locating the airborne pollutant sources in indoor environments is of great importance for improving air quality and ensuring indoor safety, especially in the context of controlling airborne pathogens. However, most existing studies focused on short-range airborne transmission in close contact and identified pollution sources based on numerical simulations. In this study, an experiment-based impulse response method was established to estimate the source term in a multi-zone building, and uncertainty analysis (UA) was conducted by combining the errors from the sensors and systematic responses. An acrylic scaled multi-zone building was built to investigate and validate the proposed method. The prior impulse response vectors between the potential sources and sensor network were obtained through impulse response experiments. Next, four sets of representative experiments were conducted with the scaled model to evaluate the source identification method. The results showed that the source release rate and source location could be identified using the impulse response method, and most of the relative errors of the source release rate were within 30%. For source location identification, the location probabilities of the constant and dynamic real sources were 44% and 100%, respectively. This study provides a novel perspective for source term estimation (STE) in multi-zone environments.

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