Literature DB >> 21456572

Modeling exposure close to air pollution sources in naturally ventilated residences: association of turbulent diffusion coefficient with air change rate.

Kai-Chung Cheng1, Viviana Acevedo-Bolton, Ruo-Ting Jiang, Neil E Klepeis, Wayne R Ott, Oliver B Fringer, Lynn M Hildemann.   

Abstract

For modeling exposure close to an indoor air pollution source, an isotropic turbulent diffusion coefficient is used to represent the average spread of emissions. However, its magnitude indoors has been difficult to assess experimentally due to limitations in the number of monitors available. We used 30-37 real-time monitors to simultaneously measure CO at different angles and distances from a continuous indoor point source. For 11 experiments involving two houses, with natural ventilation conditions ranging from <0.2 to >5 air changes per h, an eddy diffusion model was used to estimate the turbulent diffusion coefficients, which ranged from 0.001 to 0.013 m² s⁻¹. The model reproduced observed concentrations with reasonable accuracy over radial distances of 0.25-5.0 m. The air change rate, as measured using a SF₆ tracer gas release, showed a significant positive linear correlation with the air mixing rate, defined as the turbulent diffusion coefficient divided by a squared length scale representing the room size. The ability to estimate the indoor turbulent diffusion coefficient using two readily measurable parameters (air change rate and room dimensions) is useful for accurately modeling exposures in close proximity to an indoor pollution source.

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Year:  2011        PMID: 21456572     DOI: 10.1021/es103080p

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  7 in total

1.  Stochastic modeling of short-term exposure close to an air pollution source in a naturally ventilated room: an autocorrelated random walk method.

Authors:  Kai-Chung Cheng; Viviana Acevedo-Bolton; Ruo-Ting Jiang; Neil E Klepeis; Wayne R Ott; Peter K Kitanidis; Lynn M Hildemann
Journal:  J Expo Sci Environ Epidemiol       Date:  2013-09-25       Impact factor: 5.563

2.  Modeling Social Distancing and Quantifying Epidemic Disease Exposure in a Built Environment.

Authors:  Chaitra Hegde; Ali Bahrami Rad; Reza Sameni; Gari D Clifford
Journal:  IEEE J Sel Top Signal Process       Date:  2022-01-25       Impact factor: 7.695

3.  Air change rates and interzonal flows in residences, and the need for multi-zone models for exposure and health analyses.

Authors:  Liuliu Du; Stuart Batterman; Christopher Godwin; Jo-Yu Chin; Edith Parker; Michael Breen; Wilma Brakefield; Thomas Robins; Toby Lewis
Journal:  Int J Environ Res Public Health       Date:  2012-12-12       Impact factor: 3.390

4.  Covid-19 Exposure Assessment Tool (CEAT): Easy-to-use tool to quantify exposure based on airflow, group behavior, and infection prevalence in the community.

Authors:  Brian J Schimmoller; Nídia S Trovão; Molly Isbell; Chirag Goel; Benjamin F Heck; Tenley C Archer; Klint D Cardinal; Neil B Naik; Som Dutta; Ahleah Rohr Daniel; Afshin Beheshti
Journal:  medRxiv       Date:  2022-03-16

5.  COVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior.

Authors:  Brian J Schimmoller; Nídia S Trovão; Molly Isbell; Chirag Goel; Benjamin F Heck; Tenley C Archer; Klint D Cardinal; Neil B Naik; Som Dutta; Ahleah Rohr Daniel; Afshin Beheshti
Journal:  Sci Adv       Date:  2022-09-30       Impact factor: 14.957

6.  Experimental investigation of indoor aerosol dispersion and accumulation in the context of COVID-19: Effects of masks and ventilation.

Authors:  Yash Shah; John W Kurelek; Sean D Peterson; Serhiy Yarusevych
Journal:  Phys Fluids (1994)       Date:  2021-07-21       Impact factor: 3.521

7.  Modeling turbulent transport of aerosols inside rooms using eddy diffusivity.

Authors:  Akula Venkatram; Jeffrey Weil
Journal:  Indoor Air       Date:  2021-07-12       Impact factor: 6.554

  7 in total

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