Equivalence of low-cost PM10 concentration measuring devices with a reference method using various correction functions


environmental pollution management
air quality
low-cost devices
measurement equivalence

How to Cite

Owczarek, T., Rogulski, M., Czechowski, P. O., Badyda, A., & Czermański, E. (2024). Equivalence of low-cost PM10 concentration measuring devices with a reference method using various correction functions . Economics and Environment, 87(4), 627, 1–22. https://doi.org/10.34659/eis.2023.87.4.627


The aim of the study was to build a corrective model that can be used in the analysed devices and to assess the impact of such a model on the values of the measured concentrations. The novelty of this study is the test of equivalence with the equivalent reference method for hourly data. The study used hourly data of PM10 concentrations measured in a chosen city in Poland. Data was collected from two PM10 sensors and a reference device placed in close proximity. In addition, air temperature, humidity and wind speed were also measured. Among the tested models, a linear model was selected that used primary measurements of PM10, temperature, air velocity, and humidity as the most accurate approximation of the actual PM10 concentration level. The results of the analysis showed that it is possible to build mathematical models that effectively convert PM10 concentration data from tested low-cost electronic measuring devices to concentrations obtained by the reference method.



Alhasa, K. M., Mohd Nadzir, M. S., Olalekan, P., Latif, M. T., Yusup, Y., Iqbal Faruque, M. R., Ahamad, F., Abd. Hamid, H. H., Aiyub, K., Md Ali, S. H., Khan, M. F., Abu Samah, A., Yusuff, I., Othman, M., Tengku Hassim, T. M. F., & Ezani, N. E. (2018). Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System. Sensors, 18(12), 4380. https://doi.org/10.3390/s18124380

Ayres, J., Maynard, E. L., & Richards, R. (2006). Air Pollution and Health: 1st edition. London: Imperial College Press.

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, 622. https://doi.org/10.1007/s42452-019-0630-1

Bisignano, A., Carotenuto, F., Zaldei, A., & Giovannini, L. (2022). Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway. Atmospheric Environment, 275, 119008. https://doi.org/10.1016/j.atmosenv.2022.119008

Buser, M. D., Parnell, C. B. Jr., Shaw, B. W., & Lacey, R. E. (2003). Particulate matter sampler errors due to the interaction of particle size and sampler performance characteristics: PM10 and PM2.5 ambient air samplers. American Society of Agricultural and Biological Engineers, 50(1). https://doi.org/10.13031/2013.22403

Chu, H. J., Ali, M. Z., & He, Y. C. (2020). Spatial calibration and PM2.5 mapping of low‑cost air quality sensors. Scientific Reports, 10, 22079. https://doi.org/10.1038/s41598-020-79064-w

Considine, E. M., Reid, C. E., Ogletree, M. R., & Dye, T. (2021). Improving Accuracy of Air Pollution Exposure Measurements: Statistical Correction of a Municipal Low-Cost Airborne Particulate Matter Sensor Network. Environmental Pollution, 268, 115833. https://doi.org/10.1016/j.envpol.2020.115833

Czechowski, P. O. (2013). New methods and models of data measurement quality in air pollution monitoring networks assessment. Gdynia: Gdynia Maritime University Press. (in Polish).

Dąbrowiecki, P., Mucha, D., Gayer, A., Adamkiewicz, Ł., & Badyda, A. (2015). Assessment of Air Pollution Effects on the Respiratory System Based on Pulmonary Function Tests Performed During Spirometry Days. Advances in Experimental Medicine and Biology, 873, 43-52. https://doi.org/10.1007/5584_2015_152

Dorozhovets, M. (2007). Proposals for extending the methods for determining the uncertainty of measurement results according to the GUM Guide. Pomiary Automatyka Robotyka, 1, 7-15. (in Polish).

Duvall, R. M., Hagler, G. S. W., Clements, A. L., Benedict, K., Barkjohn, K., Kilaru, V., Hanley, T., Watkins, N., Kaufman, A., Kamal, A., Reece, S., Fransioli, P., Gerboles, M., Gillerman, G., Habre, R., Hannigan, M., Ning, Z., Papapostolou, V., Pope, R., Quintana, P. J. E., & Lam Snyder, J. (2021). Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets. Atmospheric Environment, 246, 118099. https://doi.org/10.1016/j.atmosenv.2020.118099

Fernando, H. J. S., Mammarella, M. C., Grandoni, C., Fedele, P., di Marco, R., Dimitrova, R., & Hyde, P. (2012). Forecasting PM10 in metropolitan areas: Efficacy of neural networks. Environmental Pollution, 163, 62-67. https://doi.org/10.1016/j.envpol.2011.12.018

Gębicki, J., & Szymańska, K. (2011). Comparison of Tests for Equivalence of Methods for Measuring PM10 Dust in Ambient Air. Polish Journal of Environmental Studies, 20(6), 1465-1472.

Gębicki, J., & Szymańska, K. (2012). Comparative field test for measurement of PM10 dust in atmospheric air using gravimetric (reference) method and β-absorption method (Eberline FH 62-1). Atmospheric Environment, 54, 18-24. https://doi.org/10.1016/j.atmosenv.2012.02.068

Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., Beekmann, M., & Subramanian, R. (2021). From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors. Journal of Aerosol Science, 158, 105833. https://doi.org/10.1016/j.jaerosci.2021.105833

Green, D. C., Fuller, G. W., & Baker, T. (2009). Development and validation of the volatile correction model for PM10–An empirical method for adjusting TEOM measurements for their loss of volatile particulate matter. Atmospheric Environment, 43(13), 2132-2141. https://doi.org/10.1016/j.atmosenv.2009.01.024

Grubbs, F. E. (1950). Sample criteria for testing outlying observations. Annals of Mathematical Statistics, 21(1), 27-58. https://doi.org/10.1214/aoms/1177729885

EC Working Group. (2010). Guide to the demonstration of equivalence of ambient air monitoring methods. https://www.aces.su.se/reflab/wp-content/uploads/2016/11/Demonstration_of_Equivalence_of_Ambient_Air_Monitoring.pdf

GUM. (1999). Expressing measurement uncertainty. Guide. Warsaw: Główny Urząd Miar. (in Polish).

Hodoli, H. C., Coulon, F., & Mead, M. I. (2020). Applicability of factory calibrated optical particle counters for high-density air quality monitoring networks in Ghana. Heliyon, 6(6), E04206. https://doi.org/10.1016/j.heliyon.2020.e04206

Jaffe, D. A., Miller, C., Thompson, K., Finley, B., Nelson, M., Ouimette, J., & Andrews, E. (2022). An evaluation of the U.S. EPA’s correction equation for Purple Air Sensor data in smoke, dust and wintertime urban pollution events. Atmospheric Measurement Techniques, 16(5), 1311-1322. https://doi.org/10.5194/amt-16-1311-2023

Jędrak, J., Konduracka, E., Badyda, A., & Dąbrowiecki, P. (2017). The impact of air pollution on health. Kraków: Krakowski Alarm Smogowy. (in Polish).

John, A. C., Quass, U., & Kuhlbusch, T. A. J. (2004). Comparison study of the chemical composition of PM10 for days with high mass concentrations in three regions in Germany. Journal of Aerosol Science, 35, 797-808. https://doi.org/10.1016/j.jaerosci.2004.06.011

Kureshi, R. R., Mishra, B. K., Thakker, D., John, R., Walker, A., Simpson, S., Thakkar, N., & Wante, A. K. (2022). Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring. Sensors, 22, 1093. https://doi.org/10.3390/s22031093

Lin, Y., Dong, W., & Chen, Y. (2018). Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 1-18. https://doi.org/10.1145/3191750

Maag, B., Zhou, Z., & Thiele, L. (2019). Enhancing Multi-hop Sensor Calibration with Uncertainty Estimates. Proceedings of 2019 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, Leicester, UK, 618-625.

Maag, B., Zhou, Z., & Thiele, T. (2018). A Survey on Sensor Calibration in Air Pollution Monitoring Deployments. IEEE Internet of Things Journal, 5(6), 4857-4870.

GIOŚ. (2020). Measurement of particulate matter in the air. http://powietrze.gios.gov.pl/pjp/content/show/1000919 (in Polish).

Myers, R. H. (1990). Classical and modern regression with applications. Pacific Grove: Duxbury Thomson Learning.

Notardonato, I., Manigrasso, M., Pierno, L., Settimo, G., Protano, C., Vitali, M., Mattei, V., Martellucci, S., & Fiore, C. D. (2018). The importance of measuring ultrafine particles in urban air quality monitoring in small cities. Geographica Pannonica, 23(4), 347-358. https://doi.org/10.5937/gp23-24447

Paschalidou, A. K., Karakitsios, S., Kleanthous, S., & Kassomenos, P. A. (2011). Forecasting hourly PM10 concentrationin Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environmental Science and Pollution Research, 18(2), 316-327. https://doi.org/10.1007/s11356-010-0375-2

Popescu, M., Ilie, C., Panaitescu, L., Lungu, M. L., Ilie, M., & Lungu, D. (2013). Artificial neural networks forecasting of the PM10 quantity in London considering the Harwell and Rochester Stoke PM10 measurements. Journal of Environmental Protection and Ecology, 14(4), 1473-1481.

Owczarek, T., & Rogulski, M. (2018). Uncertainty of PM10 concentration measurement on the example of an optical measuring device. SHS Web of Conferences, 57, 02008. https://doi.org/10.1051/shsconf/20185702008

Owczarek, T., Rogulski, M., & Badyda, A. J. (2018). Preliminary comparative assessment and elements of equivalence of air pollution measurement results of portable monitoring stations with using stochastic models. E3S Web of Conferences, 28, 01028. https://doi.org/10.1051/e3sconf/20182801028

Owczarek, T., Rogulski, M., & Czechowski, P. O. (2020). Assessment of the Equivalence of Low-Cost Sensors with the Reference Method in Measuring PM10 Concentration Using Selected Correction Functions. Sustainability, 12(13), 5368. https://doi.org/10.3390/su12135368

Rogulski, M., & Badyda, A. J. (2018). Application of the Correction Function to Improve the Quality of PM Measurements with Low-Cost Devices. SHS Web of Conferences, 57, 02009. https://doi.org/10.1051/shsconf/20185702009

Shahraiyni, H. T., & Sodoudi, S. (2016). Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. Atmospfere, 7(2), 15. https://doi.org/10.3390/atmos7020015

Shin, S. E., Jung, C. H., & Kim, Y. P. (2011). Analysis of the measurement difference for the PM10 concentrations between beta-ray absorption and gravimetric methods at Gosan. Aerosol Air Quality Research, 11(7), 846-853. https://doi.org/10.4209/aaqr.2011.04.0041

Simon, E., Molnar, V. E., Tothmeresz, B., & Szabo, S. (2020). Ecological Assessment of Particulate Material (PM5 and PM10) in Urban Habitats. Atmosphere, 11(6), 559. https://doi.org/10.3390/atmos11060559

Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti, A., Kalkavouras, P., Fameli, K. M., Hatzianastassiou, N., Mihalopoulos, N., & Gerasopoulos, E. (2020). Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece. Atmosphere, 11, 926. https://doi.org/10.3390/atmos11090926

Sówka, I., Chlebowska-Styś, A., Pachurka, P., & Rogula-Kozłowska, W. (2018). Seasonal variations of PM2.5 and PM10 concentrations and inhalation exposure from PM-bound metals (As, Cd, Ni): first studies in Poznań. Archives of Environmental Protection, 44(4), 86-95. https://doi.org/10.24425/aep.2018.122305

Szyszkowicz, M., Willey, J. B., Grafstein, E., Rowe, B. H., & Colman, I. (2010). Air Pollution and Emergency Department Visits for Suicide Attempts in Vancouver, Canada. Environmental Health Insights, 4, 79-86. https://doi.org/10.4137/EHI.S5662

Wang-Li, L., Parnell, C. B., Shaw, B. W., Lacey, R. E., Buser, M. D., Goodrich, L. B., & Capareda, S. C. (2005). Correcting PM10 over-sampling problems for agricultural particulate matter emissions: Preliminary study. American Society of Agricultural and Biological Engineers, 48(2), 749-755. https://doi.org/10.13031/2013.18317

Weir, K. (2012). Smog in our brains. American Psychological Association, 43(7), 32.

Wielogosiński, G., & Zarzycki, R. (2018). Technologies and processes of air protection. Warszawa: Wydawnictwo Naukowe PWN. (in Polish).

Zhang, X., Chen, X., & Zhang, X. (2018). The impact of exposure to air pollution on cognitive performance. National Academy of Sciences of the United States of America, 115(37), 9193-9197. https://doi.org/10.1073/pnas.1809474115

Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & Subramanian, R. (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. https://doi.org/10.5194/amt-11-291-2018

Zusman, M., Schumacher, C. S., Gassett, A. J., Spalt, E. W., Austin, E., Larson, T. V., Carvlin, G., Seto, E., Kaufman, J. D., & Sheppard, L. (2020). Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study. Environment International, 134, 105329. https://doi.org/10.1016/j.envint.2019.105329

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