Implementing Bayesian inference on a stochastic CO2-based grey-box model
The COVID-19 pandemic brought global attention to indoor air quality (IAQ), which increases public’s awareness on monitoring indoor ventilation conditions significantly. Indoor CO2 monitoring has been widely accepted as an effective way for indicating IAQ conditions, attributed to its close relation...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Indoor Environments |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950362025000086 |
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| author | Shujie Yan Jiwei Zou Chang Shu Justin Berquist Vincent Brochu Marc Veillette Danlin Hou Caroline Duchaine Liang (Grace) Zhou Zhiqiang (John) Zhai Liangzhu (Leon) Wang |
| author_facet | Shujie Yan Jiwei Zou Chang Shu Justin Berquist Vincent Brochu Marc Veillette Danlin Hou Caroline Duchaine Liang (Grace) Zhou Zhiqiang (John) Zhai Liangzhu (Leon) Wang |
| author_sort | Shujie Yan |
| collection | DOAJ |
| description | The COVID-19 pandemic brought global attention to indoor air quality (IAQ), which increases public’s awareness on monitoring indoor ventilation conditions significantly. Indoor CO2 monitoring has been widely accepted as an effective way for indicating IAQ conditions, attributed to its close relationships with indoor air change rates. However, real-time estimation of air change rates or CO2 emission rates from CO2 measurement data remains challenging due to uncertainties in factors like random air movements, dynamic conditions (e.g., weather and occupancy), and the limitations of deterministic equations. This study addresses these challenges by applying Bayesian inference to a stochastic CO2-based grey-box model, enabling the accurate estimation of ventilation and CO2 emission rates while accounting for uncertainty. The model’s accuracy and robustness were validated through CO2 tracer gas experiments, employing constant injection and decay methods in a large-scale aerosol chamber. Both prior and posterior predictive checks (PPC) were performed to verify this approach. The approach proposed by this study improves the interpretation of CO2 monitoring data, thereby facilitating the future real-time IAQ management. |
| format | Article |
| id | doaj-art-2decc8099c8f43eab58e0165bc26aa5e |
| institution | Kabale University |
| issn | 2950-3620 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Indoor Environments |
| spelling | doaj-art-2decc8099c8f43eab58e0165bc26aa5e2025-02-07T04:48:38ZengElsevierIndoor Environments2950-36202025-03-0121100079Implementing Bayesian inference on a stochastic CO2-based grey-box modelShujie Yan0Jiwei Zou1Chang Shu2Justin Berquist3Vincent Brochu4Marc Veillette5Danlin Hou6Caroline Duchaine7Liang (Grace) Zhou8Zhiqiang (John) Zhai9Liangzhu (Leon) Wang10Dept. of Building, Civil & Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec, CanadaDept. of Building, Civil & Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec, Canada; Construction Research Centre, Engineering Division, National Research Council of Canada, M-24, 1200 Montreal Road, Ottawa, Ontario, CanadaConstruction Research Centre, Engineering Division, National Research Council of Canada, M-24, 1200 Montreal Road, Ottawa, Ontario, CanadaConstruction Research Centre, Engineering Division, National Research Council of Canada, M-24, 1200 Montreal Road, Ottawa, Ontario, CanadaDépartement de biochimie, de microbiologie et de bio-informatique, Faculté́ des sciences et de génie, Université́ Laval, Québec, CanadaDépartement de biochimie, de microbiologie et de bio-informatique, Faculté́ des sciences et de génie, Université́ Laval, Québec, CanadaDept. of Building, Civil & Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec, CanadaDépartement de biochimie, de microbiologie et de bio-informatique, Faculté́ des sciences et de génie, Université́ Laval, Québec, CanadaConstruction Research Centre, Engineering Division, National Research Council of Canada, M-24, 1200 Montreal Road, Ottawa, Ontario, CanadaDepartment of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, USADept. of Building, Civil & Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec, Canada; Corresponding author.The COVID-19 pandemic brought global attention to indoor air quality (IAQ), which increases public’s awareness on monitoring indoor ventilation conditions significantly. Indoor CO2 monitoring has been widely accepted as an effective way for indicating IAQ conditions, attributed to its close relationships with indoor air change rates. However, real-time estimation of air change rates or CO2 emission rates from CO2 measurement data remains challenging due to uncertainties in factors like random air movements, dynamic conditions (e.g., weather and occupancy), and the limitations of deterministic equations. This study addresses these challenges by applying Bayesian inference to a stochastic CO2-based grey-box model, enabling the accurate estimation of ventilation and CO2 emission rates while accounting for uncertainty. The model’s accuracy and robustness were validated through CO2 tracer gas experiments, employing constant injection and decay methods in a large-scale aerosol chamber. Both prior and posterior predictive checks (PPC) were performed to verify this approach. The approach proposed by this study improves the interpretation of CO2 monitoring data, thereby facilitating the future real-time IAQ management.http://www.sciencedirect.com/science/article/pii/S2950362025000086Indoor air qualityBayesian inferenceStochastic CO2 modelGrey-box model |
| spellingShingle | Shujie Yan Jiwei Zou Chang Shu Justin Berquist Vincent Brochu Marc Veillette Danlin Hou Caroline Duchaine Liang (Grace) Zhou Zhiqiang (John) Zhai Liangzhu (Leon) Wang Implementing Bayesian inference on a stochastic CO2-based grey-box model Indoor Environments Indoor air quality Bayesian inference Stochastic CO2 model Grey-box model |
| title | Implementing Bayesian inference on a stochastic CO2-based grey-box model |
| title_full | Implementing Bayesian inference on a stochastic CO2-based grey-box model |
| title_fullStr | Implementing Bayesian inference on a stochastic CO2-based grey-box model |
| title_full_unstemmed | Implementing Bayesian inference on a stochastic CO2-based grey-box model |
| title_short | Implementing Bayesian inference on a stochastic CO2-based grey-box model |
| title_sort | implementing bayesian inference on a stochastic co2 based grey box model |
| topic | Indoor air quality Bayesian inference Stochastic CO2 model Grey-box model |
| url | http://www.sciencedirect.com/science/article/pii/S2950362025000086 |
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