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Laboratory/Field evaluation and calibration method of low-cost PM sensor for indoor PM2.5, PM10 measurement

실내 미세먼지 측정을 위한 저가형 PM 센서의 실험실/현장 평가 및 보정 방법

  • Doheon, Kim (Department of Mechanical Engineering, Yonsei University) ;
  • Dongmin, Shin (Department of Mechanical Engineering, Yonsei University) ;
  • Jungho, Hwang (Department of Mechanical Engineering, Yonsei University)
  • 김도헌 (연세대학교 기계공학부) ;
  • 신동민 (연세대학교 기계공학부) ;
  • 황정호 (연세대학교 기계공학부)
  • Received : 2022.08.29
  • Accepted : 2022.10.25
  • Published : 2022.12.31

Abstract

Recently, low-cost particulate matter (PM) sensors have been widely used in monitoring mass concentration. Maintaining the accuracy of the sensors is important and requires rigorous performance evaluation and calibration. In this study, two commercial low-cost PM sensors(LCS), Plantower PMS3003 and Plantower PMS7003, were evaluated in the laboratory and field with a reference-grade PM monitor (GRIMM 11-D). Laboratory evaluation was conducted with single/mixed particles of PSL (Poly Styrene Latex) in an acrylic chamber at 20℃ and relative humidity of 20%. Field evaluation was conducted inside a building of Yonsei University (Shinchon) from February 12 to March 31, 2022. In both evaluations, LCS measured values became different from reference measured values when the relative humidity was high or the outdoor air PM10/PM2.5 ratio was high. Based on the field evaluation, the LCS measured values were corrected through four different regression analysis models. As a result, the multivariate polynomial regression analysis model showed highest matching with the reference PM monitor (PM2.5 >0.9, PM10 >0.85). In this model, the PM10/PM2.5 ratio and relative humidity were chosen as independent variables.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구과제입니다. (No. 20181110200170)

References

  1. Badura, M., Batog, P., Drzeniecka-Osiadacz, A., & Modzel, P. (2018). Evaluation of low-cost sensors for ambient PM2. 5 monitoring. Journal of Sensors, 2018.
  2. Badura, M., Batog, P., Drzeniecka-Osiadacz, A., & Modzel, P. (2019). Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. SN Applied Sciences, 1(6), 1-11. https://doi.org/10.1007/s42452-018-0001-3
  3. Barkjohn, K. K., Bergin, M. H., Norris, C., Schauer, J. J., Zhang, Y., Black, M., ... & Zhang, J. (2020). Using low-cost sensors to quantify the effects of air filtration on indoor and personal exposure relevant PM2. 5 concentrations in Beijing, China. Aerosol and Air Quality Research, 20(2), 297-313. https://doi.org/10.4209/aaqr.2018.11.0394
  4. Crilley, L. R., Singh, A., Kramer, L. J., Shaw, M. D., Alam, M. S., Apte, J. S., ... & Pope, F. D. (2020). Effect of aerosol composition on the performance of low-cost optical particle counter correction factors. Atmospheric Measurement Techniques, 13(3), 1181-1193. https://doi.org/10.5194/amt-13-1181-2020
  5. Friedlander, S.K. (2000). Smoke, Dust and Haze. Oxford University Press, New York. w York.
  6. Gao, M., Cao, J., & Seto, E. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi'an, China. Environmental Pollution, 199, 56-65. https://doi.org/10.1016/j.envpol.2015.01.013
  7. Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., ... & Subramanian, R. (2021). From low-cost sensors to high-quality data: A s㎍mary of challenges  and best practices for effectively calibrating low-cost particulate matter mass sensors. Journal of Aerosol Science, 158, 105833.
  8. Jayaratne, R., Liu, X., Thai, P., Dunbabin, M., & Morawska, L. (2018). The influence of h㎍idity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmospheric Measurement Techniques, 11(8), 4883-4890.
  9. Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D., ... & Butterfield, A. (2017). Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491-500.
  10. Li, J., & Biswas, P. (2017). Optical characterization studies of a low-cost particle sensor. Aerosol and Air Quality Research, 17(7), 1691-1704. https://doi.org/10.4209/aaqr.2017.02.0085
  11. Magi, B. I., Cupini, C., Francis, J., Green, M., & Hauser, C. (2020). Evaluation of PM2. 5 measured in an urban setting using a low-cost optical particle counter and a Federal Equivalent Method Beta Attenuation Monitor. Aerosol Science and Technology, 54(2), 147-159. https://doi.org/10.1080/02786826.2019.1619915
  12. Malings, C., Tanzer, R., Hauryliuk, A., Saha, P. K., Robinson, A. L., Presto, A. A., & Subramanian, R. (2020). Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation. Aerosol Science and Technology, 54(2), 160-174. https://doi.org/10.1080/02786826.2019.1623863
  13. Sayahi, T., Butterfield, A., & Kelly, K. E. (2019). Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors. Environmental pollution, 245, 932-940.
  14. Shao, C., Paynabar, K., Kim, T. H., Jin, J. J., Hu, S. J., Spicer, J. P., ... & Abell, J. A. (2013). Feature selection for manufacturing process monitoring using cross-validation. Journal of Manufacturing Systems, 32(4), 550-555.
  15. Tvedskov, T. F., Meretoja, T. J., Jensen, M. B., Leidenius, M., & Kroman, N. (2014). Cross-validation of three predictive tools for non-sentinel node metastases in breast cancer patients with micro-metastases or isolated tumor cells in the sentinel node. European Journal of Surgical Oncology (EJSO), 40(4), 435-441. https://doi.org/10.1016/j.ejso.2014.01.014
  16. Zheng, T., Bergin, M. H., Johnson, K. K., Tripathi, S. N., Shirodkar, S., Landis, M. S., ... & Carlson, D. E. (2018). Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments. Atmospheric Measurement Techniques, 11(8), 4823-4846.