Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays-Reference-Cited by-同舟云学术

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Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays

Published:2023-06-28 Issue:27 Volume:57 Page:10030-10038
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Du Bowen¹²,Siegel Jeffrey A.¹³^ORCID

Affiliation:

1. Department of Civil and Mineral Engineering, University of Toronto, Toronto, Canada M5S 1A4

2. School of Architecture, Civil and Environmental Engineering, École Polytechnique Fedérale de Lausanne, 1015 Lausanne, Switzerland

3. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada M5T 1R4

Funder

Canada Foundation for Innovation

Ontario Research Foundation

Government of Canada

Ontario Trillium Foundation

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c00756

Reference49 articles.

1. A cross-sectional study of schools for compliance to ventilation rate requirements

2. Exploring the possibility of using CO₂ as a proxy for exhaled particles to predict the risk of indoor exposure to pathogens

3. Impact of natural ventilation on exposure to SARS-CoV 2 in indoor/semi-indoor terraces using CO2 concentrations as a proxy

4. Indoor CO 2 concentrations and cognitive function: A critical review

5. Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments

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1. Estimating indoor airborne concentrations of SARS-CoV-2 RNA using quantitative filter forensics;Building and Environment;2024-07

2. Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods;Sensors;2024-05-27

3. Understanding Human-building Interactions Using Computing;XRDS: Crossroads, The ACM Magazine for Students;2024-03

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