The development of air quality Wireless Sensor Network for indoor PM10 and PM2.5 prediction model

Yu Ju Huang; Chao Heng Tseng; Chung Chin Yang; Sy Yuan Kang; Huang Chin Wang

The development of air quality Wireless Sensor Network for indoor PM₁₀ and PM_2.5 prediction model

Yu Ju Huang
, Chao Heng Tseng
, Chung Chin Yang
, Sy Yuan Kang
, Huang Chin Wang

環境工程與管理研究所

研究成果: 會議貢獻 › 文本 › 同行評審

摘要

This study developed a system for Air Quality Wireless Sensoring Network (AQWSN), which is used to monitor the PM_2.5 and PM₁₀ concentration from indoor and outdoor. And this study built an Indoor Air Quality Model (IAQM) to predict indoor PM_2.5 and PM₁₀ by monitoring the real sites for the concentration of PM_2.5 and PM₁₀. After that we use the mean absolute percentage error (MAPE) and American society for testing and materials (ASTM) to verify the accuracy of IAQM. The results show that using the model of hourly prediction with the discontinuous time parameter have an excellent result of prediction for indoor PM_2.5 and PM₁₀. On the other side, the model of daily prediction with the continuous time parameter and hourly average ventilation rate has an excellent result in real space.

原文	English
頁面	167-172
頁數	6
出版狀態	Published - 2014
事件	13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014 - Hong Kong, Hong Kong 持續時間: 7 7月 2014 → 12 7月 2014

Conference

Conference	13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014
國家/地區	Hong Kong
城市	Hong Kong
期間	7/07/14 → 12/07/14

其它文件與鏈接

Link to publication in Scopus

引用此

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title = "The development of air quality Wireless Sensor Network for indoor PM10 and PM2.5 prediction model",

abstract = "This study developed a system for Air Quality Wireless Sensoring Network (AQWSN), which is used to monitor the PM2.5 and PM10 concentration from indoor and outdoor. And this study built an Indoor Air Quality Model (IAQM) to predict indoor PM2.5 and PM10 by monitoring the real sites for the concentration of PM2.5 and PM10. After that we use the mean absolute percentage error (MAPE) and American society for testing and materials (ASTM) to verify the accuracy of IAQM. The results show that using the model of hourly prediction with the discontinuous time parameter have an excellent result of prediction for indoor PM2.5 and PM10. On the other side, the model of daily prediction with the continuous time parameter and hourly average ventilation rate has an excellent result in real space.",

keywords = "Indoor air quality model, Particulate matter, Wireless Sensoring Network",

author = "Huang, \{Yu Ju\} and Tseng, \{Chao Heng\} and Yang, \{Chung Chin\} and Kang, \{Sy Yuan\} and Wang, \{Huang Chin\}",

year = "2014",

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pages = "167--172",

note = "13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014 ; Conference date: 07-07-2014 Through 12-07-2014",

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TY - CONF

T1 - The development of air quality Wireless Sensor Network for indoor PM10 and PM2.5 prediction model

AU - Huang, Yu Ju

AU - Tseng, Chao Heng

AU - Yang, Chung Chin

AU - Kang, Sy Yuan

AU - Wang, Huang Chin

PY - 2014

Y1 - 2014

N2 - This study developed a system for Air Quality Wireless Sensoring Network (AQWSN), which is used to monitor the PM2.5 and PM10 concentration from indoor and outdoor. And this study built an Indoor Air Quality Model (IAQM) to predict indoor PM2.5 and PM10 by monitoring the real sites for the concentration of PM2.5 and PM10. After that we use the mean absolute percentage error (MAPE) and American society for testing and materials (ASTM) to verify the accuracy of IAQM. The results show that using the model of hourly prediction with the discontinuous time parameter have an excellent result of prediction for indoor PM2.5 and PM10. On the other side, the model of daily prediction with the continuous time parameter and hourly average ventilation rate has an excellent result in real space.

AB - This study developed a system for Air Quality Wireless Sensoring Network (AQWSN), which is used to monitor the PM2.5 and PM10 concentration from indoor and outdoor. And this study built an Indoor Air Quality Model (IAQM) to predict indoor PM2.5 and PM10 by monitoring the real sites for the concentration of PM2.5 and PM10. After that we use the mean absolute percentage error (MAPE) and American society for testing and materials (ASTM) to verify the accuracy of IAQM. The results show that using the model of hourly prediction with the discontinuous time parameter have an excellent result of prediction for indoor PM2.5 and PM10. On the other side, the model of daily prediction with the continuous time parameter and hourly average ventilation rate has an excellent result in real space.

KW - Indoor air quality model

KW - Particulate matter

KW - Wireless Sensoring Network

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AN - SCOPUS:84924702866

SP - 167

EP - 172

T2 - 13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014

Y2 - 7 July 2014 through 12 July 2014

ER -

The development of air quality Wireless Sensor Network for indoor PM10 and PM2.5 prediction model

摘要

Conference

其它文件與鏈接

指紋

引用此

The development of air quality Wireless Sensor Network for indoor PM₁₀ and PM_2.5 prediction model