Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model
As people are the ultimate arbiters of air quality in built environments, perceived air quality (PAQ) is receiving increasing attention. Odor is often designated as the main target of PAQ regulation, but due to the complex mechanism of cross-modal human perception under multi-pollutant coupling, the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Indoor Environments |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950362024000419 |
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| author | Yihui Yin Lei Zhao Ruoyu You Jingjing Pei Hanyu Li Junzhou He Yuexia Sun Xudong Yang Qingyan Chen |
| author_facet | Yihui Yin Lei Zhao Ruoyu You Jingjing Pei Hanyu Li Junzhou He Yuexia Sun Xudong Yang Qingyan Chen |
| author_sort | Yihui Yin |
| collection | DOAJ |
| description | As people are the ultimate arbiters of air quality in built environments, perceived air quality (PAQ) is receiving increasing attention. Odor is often designated as the main target of PAQ regulation, but due to the complex mechanism of cross-modal human perception under multi-pollutant coupling, the accuracy of odor perception evaluation and prediction in the real environment is limited. This study obtained passengers’ evaluation of their perception of cabin air quality (CAQ) and odor intensity (OI) in commercial aircraft cabins through on-board measurement of 36 flights and 878 supporting questionnaires. Although the CAQ was generally acceptable, 25 % of passengers were not satisfied, and odor complaints (OI ≥ 3) were captured on 6 flights. The odor concentration (OC) and OI in the aircraft cabin were calculated based on the olfactory threshold and the Weber-Fechner psychophysical model, and the total OC distribution in different flight phases ranged from 28.4 to 66.1. Aldehydes (especially long-chain) were most likely to be smelled directly. Limited by the two basic assumptions that VOC interaction was non-existent and that the odor intensity was only related to VOC, the accuracy of OI calculated by the existing model was about 0.4. In order to improve the accuracy of evaluation, a new data-driven model for human perception (CAQ and OI) prediction based on a knowledge-based BP neural network was proposed, and its prediction accuracy (R2: 0.81–0.87) and generalization (R2: 0.76–0.93) were verified. The new model is able to consider the interactions among individual differences, environmental factors and VOC concentrations, thus providing a method innovation for realizing people-oriented VOC control. |
| format | Article |
| id | doaj-art-bfd7a3c9eb214d27b516384dab7315f2 |
| institution | Kabale University |
| issn | 2950-3620 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Indoor Environments |
| spelling | doaj-art-bfd7a3c9eb214d27b516384dab7315f22024-12-13T11:09:50ZengElsevierIndoor Environments2950-36202024-12-0114100044Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based modelYihui Yin0Lei Zhao1Ruoyu You2Jingjing Pei3Hanyu Li4Junzhou He5Yuexia Sun6Xudong Yang7Qingyan Chen8Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, ChinaDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, ChinaDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; Corresponding author.Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaFaculty of Engineering Science, Kyushu University, JapanDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, ChinaTianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaDepartment of Building Science, Tsinghua University, Beijing 100084, ChinaDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, ChinaAs people are the ultimate arbiters of air quality in built environments, perceived air quality (PAQ) is receiving increasing attention. Odor is often designated as the main target of PAQ regulation, but due to the complex mechanism of cross-modal human perception under multi-pollutant coupling, the accuracy of odor perception evaluation and prediction in the real environment is limited. This study obtained passengers’ evaluation of their perception of cabin air quality (CAQ) and odor intensity (OI) in commercial aircraft cabins through on-board measurement of 36 flights and 878 supporting questionnaires. Although the CAQ was generally acceptable, 25 % of passengers were not satisfied, and odor complaints (OI ≥ 3) were captured on 6 flights. The odor concentration (OC) and OI in the aircraft cabin were calculated based on the olfactory threshold and the Weber-Fechner psychophysical model, and the total OC distribution in different flight phases ranged from 28.4 to 66.1. Aldehydes (especially long-chain) were most likely to be smelled directly. Limited by the two basic assumptions that VOC interaction was non-existent and that the odor intensity was only related to VOC, the accuracy of OI calculated by the existing model was about 0.4. In order to improve the accuracy of evaluation, a new data-driven model for human perception (CAQ and OI) prediction based on a knowledge-based BP neural network was proposed, and its prediction accuracy (R2: 0.81–0.87) and generalization (R2: 0.76–0.93) were verified. The new model is able to consider the interactions among individual differences, environmental factors and VOC concentrations, thus providing a method innovation for realizing people-oriented VOC control.http://www.sciencedirect.com/science/article/pii/S2950362024000419Perceived air quality (PAQ)Volatile organic compound (VOC)Odor activity value (OAV)FlightsBP network |
| spellingShingle | Yihui Yin Lei Zhao Ruoyu You Jingjing Pei Hanyu Li Junzhou He Yuexia Sun Xudong Yang Qingyan Chen Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model Indoor Environments Perceived air quality (PAQ) Volatile organic compound (VOC) Odor activity value (OAV) Flights BP network |
| title | Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model |
| title_full | Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model |
| title_fullStr | Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model |
| title_full_unstemmed | Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model |
| title_short | Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model |
| title_sort | prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network ann based model |
| topic | Perceived air quality (PAQ) Volatile organic compound (VOC) Odor activity value (OAV) Flights BP network |
| url | http://www.sciencedirect.com/science/article/pii/S2950362024000419 |
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