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

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Warianty tytułu
PL
Równoważność niskokosztowych urządzeń do pomiaru stężenia PM10 z metodą referencyjną wykorzystującą różne funkcje korekcyjne
Języki publikacji
EN
Abstrakty
EN
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.
PL
Celem badań było zbudowanie modelu korekcyjnego, który można zastosować w analizowanych urządzeniach oraz ocena wpływu takiego modelu na wartości mierzonych stężeń. Nowością w pracy jest test równoważności z równoważną metodą referencyjną dla danych godzinowych. W pracy wykorzystano dane godzinowe stężeń pyłu PM10 zmierzonych w wybranym mieście w Polsce. Dane były zbierane z dwóch czujników PM10 i urządzenia referencyjnego umieszczonych w bliskiej odległości. Dodatkowo mierzono również temperaturę powietrza, wilgotność i prędkość wiatru. Spośród testowanych modeli wybrano model liniowy, który wykorzystując pierwotne pomiary PM10, temperatury, prędkości powietrza i wilgotności, najdokładniej przybliżał rzeczywisty poziom stężenia PM10. Wyniki analizy wykazały, że możliwe jest zbudowanie modeli matematycznych, które skutecznie przeliczają dane o stężeniach PM10 z badanych tanich elektronicznych urządzeń pomiarowych na stężenia uzyskane metodą referencyjną.
Rocznik
Tom
Strony
1--22
Opis fizyczny
Bibliogr. 48 tab., wykr.
Twórcy
  • Gdynia Maritime University, Faculty of Enterpreneurship and Quality Science, Gdynia, Poland
  • Warsaw University of Technology, Faculty of Building Services, Hydro and Environmental Engineering, Warsaw, Poland
  • Gdynia Maritime University, Faculty of Entrepreneurship and Quality Science, Gdynia, Poland
autor
  • Warsaw University of Technology, Faculty of Building Services, Hydro and Environmental Engineering, Warsaw, Poland
  • University of Gdansk, Faculty of Economics, Sopot, Poland, Armii Krajowej Street 119/121, 81-824 Sopot, Poland
Bibliografia
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  • 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
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  • 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).
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  • 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
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  • 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.
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  • 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
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  • 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2ccd5a07-0c39-4108-9641-1708667cdf05
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