Safety and Health at Work

  • 제10권4호
  • /
  • Pages.452-460
  • /
  • 2019
  • /
  • 2093-7911(pISSN)
  • /
  • 2093-7997(eISSN)
산업안전보건연구원 (Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency)
권호 표지
DOI QR코드

DOI QR Code

Calibration of Portable Particulate Mattere-Monitoring Device using Web Query and Machine Learning

  • Loh, Byoung Gook (Department of Applied IT Engineering, Hansung University) ;
  • Choi, Gi Heung (Department of Mechanical Systems Engineering, Hansung University)
  • 투고 : 2019.05.08
  • 심사 : 2019.08.09
  • 발행 : 2019.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Background: Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

키워드

참고문헌

  1. Boldo E, Medina S, Le Tertre A, Hurley F, Mucke H-G, Ballester F, et al. Apheis: health impact assessment of long-term exposure to PM 2.5 in 23 European cities. Eur J Epidemiol 2006;21(6):449-58. https://doi.org/10.1007/s10654-006-9014-0
  2. Bonn G. Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide. World Health Organization; 2003. p. 7-9.
  3. Grant WB. Air pollution in relation to US cancer mortality rates: an ecological study; likely role of carbonaceous aerosols and polycyclic aromatic hydrocarbons. Anticanc Res 2009;29(9):3537-45.
  4. Manikonda A, Zikova N, Hopke PK, Ferro AR. Laboratory assessment of lowcost PM monitors. J Aerosol Sci 2016;102:29-40. https://doi.org/10.1016/j.jaerosci.2016.08.010
  5. Sorensen M, Daneshvar B, Hansen M, Dragsted LO, Hertel O, Knudsen L, et al. Personal PM2. 5 exposure and markers of oxidative stress in blood. Environ Health Perspect 2003;111(2):161-6. https://doi.org/10.1289/ehp.5646
  6. Agency USEP. NAAQS Table; 2019. Available from: https://www.epa.gov/criteria-air-pollutants/naaqs-table.html.
  7. Organization WH. Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. World Health Organization; 2006.
  8. Agency EE. Air quality in Europe - 2018 report. EEA Rep 2018;(12):60.
  9. Proceedings of the 12th ACM conference on embedded network sensor systems. In: Cheng Y, Li X, Li Z, Jiang S, Li Y, Jia J, et al., editors. AirCloud: a cloudbased air-quality monitoring system for everyone. ACM; 2014.
  10. Holstius DM, Pillarisetti A, Smith K, Seto E. Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmos Meas Tech 2014;7(4):1121-31. https://doi.org/10.5194/amt-7-1121-2014
  11. Loh BG, Choi GH. Development of IoT-based PM2. 5 measuring device. J Korean Soc Saf 2017;32(1):21-6. https://doi.org/10.14346/JKOSOS.2017.32.1.21
  12. 2015 IEEE international wireless symposium (IWS 2015). In: Yang Q, Zhou G, Qin W, Zhang B, Chiang PY, editors. Air-kare: a Wi-Fi based, multi-sensor, real-time indoor air quality monitor. IEEE; 2015.
  13. 2017 13th IEEE International Conference on Control & Automation (ICCA). In: Yang X, Yang L, Zhang J, editors. AWiFi-enabled indoor air quality monitoring and control system: the design and control experiments. IEEE; 2017.
  14. Proceedings of the 2015 workshop on pervasive wireless healthcare. In: Zhuang Y, Lin F, Yoo E-H, Xu W, editors. Airsense: a portable context-sensing device for personal air quality monitoring. ACM; 2015.
  15. International conference on electrical, electronics and system engineering (ICEESE); 2013. In: Saad SM, Saad ARM, Kamarudin AMY, Zakaria A, Shakaff AYM, editors. Indoor air quality monitoring system using wireless sensor network (WSN) with web interface. IEEE; 2013.
  16. Chen L-J, Ho Y-H, Lee H-C, Wu H-C, Liu H-M, Hsieh H-H, et al. An open framework for participatory PM2. 5 monitoring in smart cities. IEEE Access 2017;5:14441-54. https://doi.org/10.1109/ACCESS.2017.2723919
  17. Sousan S, Koehler K, Thomas G, Park JH, Hillman M, Halterman A, et al. Intercomparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Sci Technol 2016;50(5):462-73. https://doi.org/10.1080/02786826.2016.1162901
  18. Sharp. Device specification for PM2.5 sensor module model No. DN7C3CA006. Electronic components and devices division; 2014.
  19. Cao TT, Jonathan E. Portable, ambient PM2.5 sensor for human and/or animal exposure studies. Anal Lett 2017;50(4):712-23. https://doi.org/10.1080/00032719.2016.1190736
  20. Perez X. MQTT topic naming convention; 2017. Available from: https://tinkerman.cat/mqtt-topic-naming-convention.
  21. AmazonWebServices. Amazon EC2 - secure and resizable compute capacity in the cloud; 2019. Available from: https://aws.amazon.com/ec2/.
  22. Eclipse-Foundation. IoT standards; 2019. Available from: https://iot.eclipse.org/standards/.
  23. Texas-Instrument. HDC1000 low power, high accuracy digital humidity sensor with temperature sensor; 2016. Available from: http://www.ti.com/lit/ds/symlink/hdc1000.pdf.
  24. Allen G, Sioutas C, Koutrakis P, Reiss R, Lurmann FW, Roberts PT. Evaluation of the TEOM method for measurement of ambient particulate mass in urban areas. J Air Waste Manag Assoc 1997;47(6):682-9. https://doi.org/10.1080/10473289.1997.10463923
  25. Badura M, Batog P, Drzeniecka-Osiadacz A, Modzel P. Evaluation of low-cost sensors for ambient PM2. 5 monitoring. J Sens 2018;2018.
  26. Crilley LR, Shaw M, Pound R, Kramer LJ, Price R, Young S, et al. Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmos Meas Tech 2018:709-20.
  27. Jayaratne R, Liu X, Thai P, Dunbabin M, Morawska L. The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmos Meas Tech 2018;11(8):4883-90. https://doi.org/10.5194/amt-11-4883-2018
  28. Wanjura JD, Shaw BW, Parnell C, Lacey RE, Capareda SC. Comparison of continuous monitor (TEOM) and gravimetric sampler particulate matter concentrations. Trans ASABE 2008;51(1):251-7. https://doi.org/10.13031/2013.24218
  29. Scikit-learn. Cross-validation: evaluating estimator performance; 2018. Available from: https://scikit-learn.org/stable/modules/cross_validation.html.

피인용 문헌

  1. Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods vol.13, pp.4, 2019, https://doi.org/10.5194/amt-13-1693-2020
  2. Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure vol.17, pp.22, 2019, https://doi.org/10.3390/ijerph17228602
  3. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges vol.197, 2019, https://doi.org/10.1016/j.envres.2021.111163
  4. Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges vol.12, pp.9, 2021, https://doi.org/10.3390/atmos12091192