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The Deployment of Carbon Monoxide Wireless Sensor Network (CO-WSN) for Ambient Air Monitoring

Author

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  • Chaichana Chaiwatpongsakorn

    (Department of Biomedical, Chemical, and Environmental Engineering, University of Cincinnati, 2901 Woodside Dr., Cincinnati, OH 45221, USA)

  • Mingming Lu

    (Department of Biomedical, Chemical, and Environmental Engineering, University of Cincinnati, 2901 Woodside Dr., Cincinnati, OH 45221, USA)

  • Tim C. Keener

    (Department of Biomedical, Chemical, and Environmental Engineering, University of Cincinnati, 2901 Woodside Dr., Cincinnati, OH 45221, USA)

  • Soon-Jai Khang

    (Department of Biomedical, Chemical, and Environmental Engineering, University of Cincinnati, 2901 Woodside Dr., Cincinnati, OH 45221, USA)

Abstract

Wireless sensor networks are becoming increasingly important as an alternative solution for environment monitoring because they can reduce cost and complexity. Also, they can improve reliability and data availability in places where traditional monitoring methods are difficult to site. In this study, a carbon monoxide wireless sensor network (CO-WSN) was developed to measure carbon monoxide concentrations at a major traffic intersection near the University of Cincinnati main campus. The system has been deployed over two weeks during Fall 2010, and Summer 2011–2012, traffic data was also recorded by using a manual traffic counter and a video camcorder to characterize vehicles at the intersection 24 h, particularly, during the morning and evening peak hour periods. According to the field test results, the 1 hr-average CO concentrations were found to range from 0.1–1.0 ppm which is lower than the National Ambient Air Quality Standards (NAAQS) 35 ppm on a one-hour averaging period. During rush hour periods, the traffic volume at the intersection varied from 2,067 to 3,076 vehicles per hour with 97% being passenger vehicles. Furthermore, the traffic volume based on a 1-h average showed good correlation (R 2 = 0.87) with the 1-h average CO-WSN concentrations for morning and evening peak time periods whereas CO-WSN results provided a moderate correlation (R 2 = 0.42) with 24 hours traffic volume due to fluctuated changes of meteorological conditions. It is concluded that the performance and the reliability of wireless ambient air monitoring networks can be used as an alternative method for real time air monitoring.

Suggested Citation

  • Chaichana Chaiwatpongsakorn & Mingming Lu & Tim C. Keener & Soon-Jai Khang, 2014. "The Deployment of Carbon Monoxide Wireless Sensor Network (CO-WSN) for Ambient Air Monitoring," IJERPH, MDPI, vol. 11(6), pages 1-19, June.
  • Handle: RePEc:gam:jijerp:v:11:y:2014:i:6:p:6246-6264:d:37094
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    Cited by:

    1. Dora Cama-Pinto & Miguel Damas & Juan Antonio Holgado-Terriza & Francisco Gómez-Mula & Alejandro Cama-Pinto, 2019. "Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse," IJERPH, MDPI, vol. 16(10), pages 1-15, May.

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