Performance Comparison of Nonlinear Pre–Calibrate Low–Cost PM2.5 Sensors Using an SPS30 Reference
DOI:
https://doi.org/10.55003/ETH.420403Keywords:
Low–cost, Nonlinear calibration, PM2.5 sensors, Internet of Things (IoTs), Air quality monitoringAbstract
This research presents a performance comparison of low–cost particulate matter (PM2.5) sensors, widely used in Internet of Things (IoT) applications for air quality monitoring. Since sensor calibration is often costly, this study proposes a cost–reduction strategy by applying pre–calibration before full calibration. The SPS30 was selected as the primary reference device due to its combination of low cost and near–regulatory–grade performance. Unlike other low–cost sensors, the SPS30 benefits from factory calibration against reference instruments (e.g., TSI DustTrak DRX 8533, OPS 3330), and it has demonstrated very low intra–model variability (<1.5% for PM2.5) and strong correlations across all concentrations with Federal Equivalent Method (FEM) instruments. It is also MCERTS–certified (UK Environment Agency), confirming its compliance with PM2.5 monitoring standards. To validate the methodology, the SPS30’s accuracy was additionally examined using an air purifier in the test setup. A nonlinear mathematical model was then applied to calibrate commonly used sensors, including the Plantower PMS series (PMS7003, PMS5003, PMS3003) and SDS011. Experiments were conducted in an indoor environment at 33 ± 1°C and 69 ± 4% relative humidity. The results showed coefficient of determination values of 0.98, 0.98, 0.96, and 0.88, with root mean square error values of 1.2, 1.47, 1.84, and 3.26 for the PMS7003, PMS5003, PMS3003, and SDS011, respectively. The findings indicate that low–cost sensors, particularly the PMS7003 and PMS5003, can achieve high measurement accuracy when combined with appropriate pre–calibration and a suitable reference device. The SDS011 also demonstrated consistent performance. In addition, applying a nonlinear model reduces costs and enhances sensor reliability. For initial deployment, pre–calibration lowers expenses by approximately one–third compared to full calibration, while pairwise pre–calibration for recalibration can substantially reduce or even eliminate recurring calibration costs during long–term operation and maintenance. These results highlight the practicality of deploying low–cost sensors in air quality monitoring applications.
References
World Health Organization. “Ambient (outdoor) air pollution.”, www.who.int. https://www.who.int/news–room/fact–sheets/detail/ambient–(outdoor)–air–quality–and – health. (accessed: May 30,2025).
G. Crolly and F. GmbH, “Laser scattering – a brief introduction,” Fritsch GmbH Milling and Sizing, Idar-Oberstein Germany, Jun. 3, 2025. [Online]. Available: https://www.fritsch-international.com/ fileadmin/Redakteur/Downloads/Reports_sizing/Introduction_Laser_Scattering/Laser_Scattering_-__introduction.pdf
L. Spinelle, M. Gerboles, M. G. Villani, M. Aleixandre and F. Bonavitacola, “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, vol. 238, pp. 706–715, 2017, doi: 10.1016/j.snb.2016.07.036
J. Hua, Y. Zhang, B. de Foy, X. Mei, J. Shang, Y. Zhang, I. D. Sulaymon and D. Zhou, “Improved PM2.5 concentration estimates from low–cost sensors using calibration models categorized by relative humidity,” Aerosol Science and Technology, vol. 55, no. 5, pp. 600–613, 2021, doi: 10.1080/02786826.2021.1873911.
J. V. Jagatha, A. Klausnitzer, M. Chacón–Mateos, B. Laquai, E. Nieuwkoop, P. van der Mark, U. Vogt and C. Schneider, “Calibration Method for Particulate Matter Low–Cost Sensors Used in Ambient Air Quality Monitoring and Research,” sensors, vol. 21, no. 12, 2021, Art. no. 3960, doi: 10.3390/s21123960.
M. J. Alonso, H. Madsen, P. Liu, R. B. Jørgensen, T. B. Jørgensen, E. J. Christiansen, O. A. Myrvang, D. Bastien and H. M. Mathisen, “Evaluation of low–cost formaldehyde sensors calibration,” Building and Environment, vol. 222, 2022, Art. no. 109380, doi: 10.1016/j.buildenv.2022.109380.
R. Dejchanchaiwong, P. Tekasakul, A. Saejio, T. Limna, T. –C. Le, C. –J. Tsai, G. –Y. Lin and J. Morris, “Seasonal Field Calibration of Low–Cost PM2.5 Sensors in Different Locations with Different Sources in Thailand,” atmosphere, vol. 14, no. 3,2023, Art. no. 496, doi: 10.3390/atmos14030496.
N. H. Nguyen, H. X. Nguyen, T. T. B. Le and C. D. Vu, “Evaluating low–cost commercially available sensors for air quality monitoring and application of sensor calibration methods for improving accuracy,” Open Journal of Air Pollution, vol. 10, no. 1, pp. 1–17, 2021, doi: 10.4236/ojap.2021.101001.
C. –H. Huang, J. He, E. Austin, E. Seto and I. Novosselov, “Assessing the value of complex refractive index and particle density for calibration of low–cost particle matter sensor for size–resolved particle count and PM2.5 measurements,” PLoS ONE, vol.16, no. 11, 2021, Art. no. e0259745, doi: 10.1371/journal.pone.0259745.
W. Chanapromma and W. Wongdocmai, “Calibration based on Polynomial Function for Low–Cost Sensors: A Case Study of Air Purifier Filter,” in 2024 12th International Electrical Engineering Congress (iEECON), Pattaya, Thailand, Mar. 6–8, 2024, pp. 1–4, doi: 10.1109/ieecon60677.2024.10537897.
C. -M. Huang, Y. -J. Liu, Y. -J. Hsieh, W. -L. Lai, C. -Y. Juan and S. -Y. Chen, “A multi-gas sensing system for air quality monitoring,” in 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan, Apr. 13–17, 2018, pp. 834–837, doi: 10.1109/ICASI.2018.8394393.
C. R. Reddy et al., “Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors,” in 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK, Aug. 31–3, 2020, pp.1–7, doi: 10.1109/PIMRC48278.2020.9217109.
Z. Liu, G. Wang, L. Zhao and G. Yang, “Multi–points indoor air quality monitoring based on internet of things,” IEEE Access, vol. 9, pp. 70479–70492, 2021, doi: 10.1109/ACCESS.2021.3073681.
N. Otanasap, S. Tadsuan and C. Chalermsuk, “An AIoT–based Air Quality Monitoring System for Real–Time PM2.5 Prediction in Urban Environments,” ASEAN Journal of Scientific and Technological Reports, vol. 28, no. 1, 2024, Art. no. e255168, doi: 10.55164/ajstr.v28i1.255168.
Datasheet SPS30: particulate matter sensor for air quality monitoring and control, Sensirion AG, Jun. 6, 2025. [Online]. Available: https://sensirion.com/ products/catalog/SPS30
SDS011 sensor laser PM2.5 sensor specification, Nova Fitness Co., Ltd., Oct. 9, 2015. [Online]. Available: https://www-sd-nf.oss-cn-beijing.aliyuncs.com/%E5%AE%98%E7%BD%91%E4%B8%8B%E8%BD%BD/SDS011%20laser%20PM2.5%20sensor%20specification-V1.4.pdf
Digital universal particle concentration sensor PMS3003 series data manual, Nanchang Panteng Technology Co., Ltd., Jun. 1, 2016. [Online]. Available:https://download.kamami.pl/p563980-PMS3003%20series%20data%20manual_English_V2.5.pdf
Digital universal particle concentration sensor PMS5003 series data manual, Nanchang Panteng Technology Co., Ltd., Jun. 1, 2016. [Online]. Available: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf
Digital universal particle concentration sensor PMS7003 series data manual, Nanchang Panteng Technology Co., Ltd., Jun. 1, 2016. [Online]. Available: https://download.kamami.pl/p564008-PMS7003%20series%20data%20manua_English_V2.5.pdf
South Coast Air Quality Management District (SCAQMD), “Field Evaluation Sensirion SPS30 Evaluation Kit,” South Coast Air Quality Management District, Diamond Bar, CA, USA, Draft report, 2019.
Sensirion, “AG, Product Conformity Certificate: SPS30 Particulate Matter Sensor with SEK Eval Kit,” Sira Certification Service, Flintshire, Wales, Sira MC200350/01, Jan. 7, 2025. [Online]. Available: https://sensirion.com/media/documents/3A3BF572/67F658F3/SPS3x_MCERTS_Certificate_MC20035001.pdf.
V. Nonthakanok, “Inhalation Exposure to Particle–bound Polycyclic Aromatic Hydrocarbons and Health Risk Assessment of Workers at Religion Place in Bangkok,” M.S. thesis, Dept. of Environmental Science, Chulalongkorn Univ., Bangkok, Thailand, 2013.
PMS9003M, Nanchang Panteng Technology Co., Ltd., [Online]. Available: https://www.plantower.com/en/products_33/99.html, [Accessed: Sep. 09,2025].
T. Sayahi, A. Butterfield and K. E. Kelly, “Long–term field evaluation of the Plantower PMS low–cost particulate matter sensors,” Environmental Pollution, vol. 245, pp. 932–940, 2019, doi: 10.1016/j.envpol.2018.11.065.
J. Li, S. K. Mattewal, S. Patel and P. Biswas, “Evaluation of Nine Low–cost–sensor–based Particulate Matter Monitors,” Aerosol and Air Quality Research, vol. 20, no. 2, pp. 254–270, 2020, doi: 10.4209/aaqr.2018.12.0485.
P. Gäbel and E. Hertig, “Recalibration of low–cost air pollution sensors: Is it worth it?,” preprint, EGUsphere, Jul. 2025. [Online]. Available: https://doi.org/10.5194/egusphere-2025-2677.
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