Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data
Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In thi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412023001733 |
| _version_ | 1797840014794555392 |
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| author | Justin Y.Y. Lee Yanhao Miao Ricky L.T. Chau Mark Hernandez Patrick K.H. Lee |
| author_facet | Justin Y.Y. Lee Yanhao Miao Ricky L.T. Chau Mark Hernandez Patrick K.H. Lee |
| author_sort | Justin Y.Y. Lee |
| collection | DOAJ |
| description | Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM2.5 and PM10) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %–80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time. |
| first_indexed | 2024-04-09T16:07:33Z |
| format | Article |
| id | doaj.art-809697ed62de403a9c479c065a3b32ca |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-04-09T16:07:33Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-809697ed62de403a9c479c065a3b32ca2023-04-25T04:07:42ZengElsevierEnvironment International0160-41202023-04-01174107900Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor dataJustin Y.Y. Lee0Yanhao Miao1Ricky L.T. Chau2Mark Hernandez3Patrick K.H. Lee4School of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region, ChinaSchool of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region, ChinaSchool of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region, ChinaCivil, Environmental and Architectural Engineering Department, Environmental Engineering Program, University of Colorado, Boulder, CO, USASchool of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region, China; State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong Special Administrative Region, China; Corresponding author at: B5423, Yeung Kin Man Academic Building, School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, China.Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM2.5 and PM10) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %–80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.http://www.sciencedirect.com/science/article/pii/S0160412023001733Indoor air qualityBioaerosolsMonitoringArtificial intelligenceSensors |
| spellingShingle | Justin Y.Y. Lee Yanhao Miao Ricky L.T. Chau Mark Hernandez Patrick K.H. Lee Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data Environment International Indoor air quality Bioaerosols Monitoring Artificial intelligence Sensors |
| title | Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| title_full | Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| title_fullStr | Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| title_full_unstemmed | Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| title_short | Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| title_sort | artificial intelligence based prediction of indoor bioaerosol concentrations from indoor air quality sensor data |
| topic | Indoor air quality Bioaerosols Monitoring Artificial intelligence Sensors |
| url | http://www.sciencedirect.com/science/article/pii/S0160412023001733 |
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