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Indoor Air Sensing: A Study in Cost, Energy, Reliability and Fidelity in Sensing

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Abstract

The rise in environmental pollution and degradation of air quality worldwide has dragged researchers’ attention due to its direct societal impact. Studies reveal that the indoor environment is more polluted than the outdoors. In this paper, a framework for indoor air quality monitoring has been presented. We have developed a portable and cost-effective air quality monitoring device. The device generates fine-grained data for a combination of different pollutants and meteorological sensors (humidity and temperature). In this work, an energy-aware Environment Monitoring Device (EMD) has been developed with an adaptive sampling rate. Different aspects of the EMD have been presented with an analysis of their power consumption. The proposed technique has reduced more than 45% of energy consumption. A proposed energy reduction technique has discussed a trade-off between the cost-effectiveness of developed EMD and its reliability. We proposed the calibration of the sensor to ensure the reliability of the sensed data. A soft-calibration technique has been proposed considering the classrooms’ Spatio-temporal nature to ensure the sensed data’s reliability by mitigating the sensor errors due to spatial factors and achieved \(\approx 6\%\) of error reduction compared to the baselines. Moreover, an energy-aware calibration technique has been proposed by providing a scheduling algorithm for re-calibration. The overall system significantly improves the lifetime and energy consumption of the sensors compared to that of normal conditions.

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Notes

  1. https://www.aeroqual.com/product/indoor-portable-monitor-starter-kit.

  2. https://www.aeroqual.com/product/outdoor-portable-monitor-starter-kit.

  3. https://www.amazon.in/PortaPow-Monitor-SmartCharge-Chargers-Panels/dp/B0713MTPHX.

References

  1. Aeroqual: Air quality monitoring equipment. https://www.aeroqual.com/

  2. Airbeam: Share & improve your air. https://www.kickstarter.com/projects/741031201/airbeam-share-and-improve-your-air

  3. Anastasi, G., Bruschi, P., & Marcelloni, F. (2014) U-sense, a cooperative sensing system for monitoring air quality in urban areas. In Smart Cities (p. 34).

  4. Annesi-Maesano, I., Hulin, M., Lavaud, F., Raherison, C., Kopferschmitt, C., de Blay, F., et al. (2012). Poor air quality in classrooms related to asthma and rhinitis in primary school children of the french 6 cities study. Thorax, 67(8), 682–688.

    Article  Google Scholar 

  5. Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., & Hueglin, C. (2018). Performance of no, no 2 low cost sensors and three calibration approaches within a real world application. Atmospheric Measurement Techniques, 11(6), 3717–3735.

    Article  Google Scholar 

  6. Chatzidiakou, L., Mumovic, D., & Summerfield, A. J. (2012). What do we know about indoor air quality in school classrooms? A critical review of the literature. Intelligent Buildings International, 4(4), 228–259.

    Article  Google Scholar 

  7. Chen, X., Zheng, Y., Chen, Y., Jin, Q., Sun, W., Chang, E., & Ma, W. Y. (2014). Indoor air quality monitoring system for smart buildings. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. (pp. 471–475). ACM.

  8. Chithra, V., & Shiva, N. S. (2018). A review of scientific evidence on indoor air of school building: Pollutants, sources, health effects and management. Asian Journal of Atmospheric Environment, 12(2), 87–108.

    Article  Google Scholar 

  9. Cordero, J. M., Borge, R., & Narros, A. (2018). Using statistical methods to carry out in field calibrations of low cost air quality sensors. Sensors and Actuators B: Chemical, 267, 245–254.

    Article  Google Scholar 

  10. De Vito, S., Piga, M., Martinotto, L., & Di Francia, G. (2009). Co, no2 and nox urban pollution monitoring with on-field calibrated electronic nose by automatic Bayesian regularization. Sensors and Actuators B: Chemical, 143(1), 182–191.

    Article  Google Scholar 

  11. Eranna, G., Joshi, B., Runthala, D., & Gupta, R. (2004). Oxide materials for development of integrated gas sensors—A comprehensive review. Critical Reviews in Solid State and Materials Sciences, 29(3–4), 111–188.

    Article  Google Scholar 

  12. Fonollosa, J., Fernandez, L., Gutiérrez-Gálvez, A., Huerta, R., & Marco, S. (2016). Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization. Sensors and Actuators B: Chemical, 236, 1044–1053.

    Article  Google Scholar 

  13. Ghaffarianhoseini, A., AlWaer, H., Omrany, H., Ghaffarianhoseini, A., Alalouch, C., Clements-Croome, D., & Tookey, J. (2018). Sick building syndrome: Are we doing enough? Architectural Science Review, 61(3), 99–121.

    Article  Google Scholar 

  14. Gottlicher, S., Gager, M., Mandl, N., & Mareckova, K. (2010) European union emission inventory report 1990–2008 under the unece convention on long-range transboundary air pollution (lrtap). Tech. rep.

  15. Grace, S., Mohan Lal, D., & Sharmeela, C. (2004). Demand controlled systems with fuzzy controllers to maintain indoor air quality-an energy saving approach. International Journal of Ventilation, 3(1), 79–86.

    Article  Google Scholar 

  16. Gunn, S. R., et al. (1998). Support vector machines for classification and regression. ISIS Technical Report, 14(1), 5–16.

    Google Scholar 

  17. Hernández, N., Talavera, I., Biscay, R. J., Porro, D., & Ferreira, M. M. (2009). Support vector regression for functional data in multivariate calibration problems. Analytica Chimica Acta, 642(1–2), 110–116.

    Article  Google Scholar 

  18. Hess-Kosa, K. (2018). Indoor air quality: The latest sampling and analytical methods. London: CRC Press.

    Book  Google Scholar 

  19. Houtman, I., Douwes, M., Jong, T. D., Meeuwsen, J., Jongen, M., Brekelmans, F., Nieboer-Op de Weegh, M., Brouwer, D., Bossche, S., Zwetsloot, G., et al. (2008). New forms of physical and psychosocial health risks at work. European Parliament.

  20. Huynh, C. (2010). Building energy saving techniques and indoor air quality—A dilemma. International Journal of Ventilation, 9(1), 93–98.

    Article  Google Scholar 

  21. Ionascu, M. E., Castell, N., Boncalo, O., Schneider, P., Darie, M., & Marcu, M. (2021). Calibration of co, no2, and o3 using airify: A low-cost sensor cluster for air quality monitoring. Sensors, 21(23), 7977.

    Article  Google Scholar 

  22. Indoor air quality (2017). https://www.eea.europa.eu/signals/signals-2013/articles/indoor-air-quality

  23. India 2020—energy policy review (2020)

  24. Jeffery, S. R., Alonso, G., Franklin, M. J., Hong, W., & Widom, J. (2006). Declarative support for sensor data cleaning. In International conference on pervasive computing (pp. 83–100). Springer.

  25. Jelicic, V., Magno, M., Brunelli, D., Paci, G., & Benini, L. (2012). Context-adaptive multimodal wireless sensor network for energy-efficient gas monitoring. IEEE Sensors Journal, 13(1), 328–338.

    Article  Google Scholar 

  26. Khedo, K. K., & Chikhooreeah, V. (2017). Low-cost energy-efficient air quality monitoring system using wireless sensor network. In Wireless sensor networks-insights and innovations. IntechOpen.

  27. Kularatna, N., & Sudantha, B. (2008). An environmental air pollution monitoring system based on the ieee 1451 standard for low cost requirements. IEEE Sensors Journal, 8(4), 415–422.

    Article  Google Scholar 

  28. Martani, C., Lee, D., Robinson, P., Britter, R., & Ratti, C. (2012). Enernet: Studying the dynamic relationship between building occupancy and energy consumption. Energy and Buildings, 47, 584–591.

    Article  Google Scholar 

  29. Measure pm and co2, temp, humidity with airveda monitors: Breathe well. http://www.airveda.com/

  30. Martins, N. R., & da Graça, G. C. (2018). Impact of pm2.5 in indoor urban environments: A review. Sustainable Cities and Society, 42, 259–275.

    Article  Google Scholar 

  31. McConnell, R., Islam, T., Shankardass, K., Jerrett, M., Lurmann, F., Gilliland, F., et al. (2010). Childhood incident asthma and traffic-related air pollution at home and school. Environmental Health Perspectives, 118(7), 1021–1026.

    Article  Google Scholar 

  32. Meng, Q. Y., Turpin, B. J., Korn, L., Weisel, C. P., Morandi, M., Colome, S., et al. (2005). Influence of ambient (outdoor) sources on residential indoor and personal pm 2.5 concentrations: Analyses of riopa data. Journal of Exposure Science and Environmental Epidemiology, 15(1), 17.

    Article  Google Scholar 

  33. Nielsen, P. V., Lin, C. H., Phillips, D., Al-Alusi, T., Chen, Y., Srebric, J., Dols, S., Walton, G., Lorenzetti, D., Musser, A., et al. (2005). Indoor environmental modelling: Chapter 34 in ashrae handbook, fundamentals.

  34. Ostertagová, E. (2012). Modelling using polynomial regression. Procedia Engineering, 48, 500–506.

    Article  Google Scholar 

  35. Parkinson, T., Parkinson, A., & de Dear, R. (2019). Continuous ieq monitoring system: Context and development. Building and Environment, 149, 15–25.

    Article  Google Scholar 

  36. Patel, M. M., & Miller, R. L. (2009). Air pollution and childhood asthma: Recent advances and future directions. Current Opinion in Pediatrics, 21(2), 235.

    Article  Google Scholar 

  37. Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398.

    Article  Google Scholar 

  38. Persily, A., & de Jonge, L. (2017). Carbon dioxide generation rates for building occupants. Indoor Air, 27(5), 868–879.

    Article  Google Scholar 

  39. Plume labs: Be empowered against air pollution. https://flow.plumelabs.com/

  40. Revel, G. M., Arnesano, M., Pietroni, F., Frick, J., Reichert, M., Schmitt, K., et al. (2015). Cost-effective technologies to control indoor air quality and comfort in energy efficient building retrofitting. Environmental Engineering and Management Journal, 14(7), 1487–1494.

    Article  Google Scholar 

  41. Sarigiannis, D. A., Gotti, A., & Karakitsios, S. P. (2019). Indoor air and public health. In Management of emerging public health issues and risks (pp. 3–29). Elsevier.

  42. Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1–16.

    Article  MathSciNet  Google Scholar 

  43. Shaban, K. B., Kadri, A., & Rezk, E. (2016). Urban air pollution monitoring system with forecasting models. IEEE Sensors Journal, 16(8), 2598–2606.

    Article  Google Scholar 

  44. Sharma, P. K., Poddar, B., Dey, S., Nandi, S., De, T., Saha, M., Mondal, S., & Saha, S. (2017). On detecting acceptable air contamination in classrooms using low cost sensors. In 2017 9th international conference on communication systems and networks (COMSNETS) (pp. 484–487). IEEE.

  45. Spachos, P., & Hatzinakos, D. (2015). Real-time indoor carbon dioxide monitoring through cognitive wireless sensor networks. IEEE Sensors Journal, 16(2), 506–514.

    Article  Google Scholar 

  46. Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2017). Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. part b: No, co and co2. Sensors and Actuators B: Chemical, 238, 706–715.

    Article  Google Scholar 

  47. Standard, A. A. (2012). Standard guide for using indoor carbon dioxide concentrations to evaluate indoor air quality and ventilation. West Conshohocken: American Society for Testing and Materials.

    Google Scholar 

  48. Suriano, D., Cassano, G., & Penza, M. (2020). Design and development of a flexible, plug-and-play, cost-effective tool for on-field evaluation of gas sensors. Journal of Sensors 2020

  49. Tsujita, W., Yoshino, A., Ishida, H., & Moriizumi, T. (2005). Gas sensor network for air-pollution monitoring. Sensors and Actuators B: Chemical, 110(2), 304–311.

    Article  Google Scholar 

  50. Tu, Z. X., Hong, C. C., & Feng, H. (2017). Emacs: Design and implementation of indoor environment monitoring and control system. In 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS) (pp. 305–309). IEEE.

  51. Vakiloroaya, V., Samali, B., Fakhar, A., & Pishghadam, K. (2014). A review of different strategies for hvac energy saving. Energy Conversion and Management, 77, 738–754.

    Article  Google Scholar 

  52. Vesitara, R., & Surahman, U. (2019). Sick building syndrome: Assessment of school building air quality. Journal of Physics: Conference Series, 1375, 012087.

    Google Scholar 

  53. Wei, C., & Li, Y. (2011) Design of energy consumption monitoring and energy-saving management system of intelligent building based on the internet of things. In 2011 international conference on electronics, communications and control (ICECC) (pp. 3650–3652). IEEE.

  54. Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., & Aberer, K. (2012) Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In 2012 16th international symposium on wearable computers (pp. 17–24). IEEE.

  55. Zimmerman, N., Presto, A. A., Kumar, S. P., Gu, J., Hauryliuk, A., Robinson, E. S., et al. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11(1), 291–313.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the anonymous reviewers for constructive suggestions and insightful comments which greatly helped to improve the quality of the manuscript. This publication is an outcome of the R &D work undertaken in the (a) Council of Scientific & Industrial Research (CSIR), India (Grant No. 09/973(0014)/2016-EMR-1), a premier national R &D organisation (b) Project IntAirSense funded by Department of Science & Technology (DST), West Bengal, India for funding our research work in parts (Grant No. 228(Sanc.)/ST/P/S&T/6G-9/2018).

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Authors and Affiliations

  1. National Institute Technology Durgapur, Durgapur, West Bengal, India

    Praveen Kumar Sharma, Bidyut Dalal, Sandip Mondal, Tanmay De & Sujoy Saha

  2. Indian Institute of Technology, Jodhpur, Jodhpur, Rajasthan, India

    Ananya Mondal

  3. Indian Institute of Technology, Kharagpur, Kharagpur, West Bengal, India

    Argha Sen

  4. Ramkrishna Mahato Government Engineering College, Purulia, Purulia, West Bengal, India

    Amartya Banerjee

  5. ITER, Siksha ‘O’ Anusandhan, Bhubaneswar, India

    Praveen Kumar Sharma

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  1. Praveen Kumar Sharma
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Correspondence to Praveen Kumar Sharma.

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Sharma, P.K., Dalal, B., Mondal, A. et al. Indoor Air Sensing: A Study in Cost, Energy, Reliability and Fidelity in Sensing. Sens Imaging 24, 7 (2023). https://doi.org/10.1007/s11220-023-00412-x

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