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

Graduate Institute of Environmental Engineering and Management

Research output: Contribution to conference › Paper › peer-review

Abstract

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.

Original language	English
Pages	167-172
Number of pages	6
State	Published - 2014
Event	13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014 - Hong Kong, Hong Kong Duration: 7 Jul 2014 → 12 Jul 2014

Conference

Conference	13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014
Country/Territory	Hong Kong
City	Hong Kong
Period	7/07/14 → 12/07/14

Keywords

Indoor air quality model
Particulate matter
Wireless Sensoring Network

Cite this

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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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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 PM_2.5 prediction model

Abstract

Conference

Keywords

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