Abstract
With the advancement of air pollution management, low-cost sensors are increasingly being used in air quality monitoring, but the data quality of these sensors is still a major source of concern. In this paper, a two-step model was created to refine the calibration process for low-cost PM sensors using data from five standard air monitoring stations in Sofia. At first, we calibrated five sensors located next to the standard instruments using five different supervised machine learning models and then the ANN-final model with anomaly detection completed the results. The ANN-final model improved the \(R^2\) values of the PM10 determined by low-cost sensors from 0.62 to 0.95 as compared to standard instruments, which confirmed the effectiveness of the calibration. This calibration model was further applied and evaluated on other sensors of the network, located in a 500m radius near the standard instruments.
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References
Brantley, H., Hagler, G., Kimbrough, E., Williams, R., Mukerjee, S., Neas, L.: Mobile air monitoring data-processing strategies and effects on spatial air pollution trends. Atmosp. Meas. Tech. 7(7), 2169–2183 (2014)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chin, J.Y., Steinle, T., Wehlus, T., Dregely, D., Weiss, T., Belotelov, V.I., Stritzker, B., Giessen, H.: Nonreciprocal plasmonics enables giant enhancement of thin-film faraday rotation. Nat. Commun. 4(1), 1–6 (2013)
Chow, J.C.: Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air Waste Manag. Assoc. 45(5), 320–382 (1995)
Collier-Oxandale, A., Casey, J.G., Piedrahita, R., Ortega, J., Halliday, H., Johnston, J., Hannigan, M.P.: Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments. Atmosp. Meas. Tech. 11(6), 3569–3594 (2018)
Considine, E.M., Reid, C.E., Ogletree, M.R., Dye, T.: Improving accuracy of air pollution exposure measurements: statistical correction of a municipal low-cost airborne particulate matter sensor network. Environ. Pollut. 268, 115833 (2021)
Geiß, A., Wiegner, M., Bonn, B., Schäfer, K., Forkel, R., Schneidemesser, E.v., Münkel, C., Chan, K.L., Nothard, R.: Mixing layer height as an indicator for urban air quality? Atmosp. Meas. Tech. 10(8), 2969–2988 (2017)
Gevrey, M., Dimopoulos, I., Lek, S.: Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model. 160(3), 249–264 (2003)
Hagan, D.H., Isaacman-VanWertz, G., Franklin, J.P., Wallace, L.M., Kocar, B.D., Heald, C.L., Kroll, J.H.: Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments. Atmosp. Meas. Tech. 11(1), 315–328 (2018)
Holstius, D.M., Pillarisetti, A., Smith, K., Seto, E.: Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmos. Meas. Tech. 7(4), 1121–1131 (2014)
Janssen, N., Fischer, P., Marra, M., Ameling, C., Cassee, F.: Short-term effects of pm2. 5, pm10 and pm2. 5–10 on daily mortality in The Netherlands. Sci. Total Environ. 463, 20–26 (2013)
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. Atmosp. Meas. Tech. 11(8), 4883–4890 (2018)
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., et al.: Community air sensor network (cairsense) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern united states. Atmosp. Meas. Tech. 9(11), 5281–5292 (2016)
Kampa, M., Castanas, E.: Human health effects of air pollution. Environ. Pollut. 151(2), 362–367 (2008)
Koehler, K.A., Peters, T.M.: New methods for personal exposure monitoring for airborne particles. Curr. Environ. Health Rep. 2(4), 399–411 (2015)
Kwok, S.W., Carter, C.: Multiple decision trees. In: Machine Intelligence and Pattern Recognition, vol. 9, pp. 327–335. Elsevier (1990)
Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S., Gao, J., et al.: Low-cost sensors as an alternative for long-term air quality monitoring. Environ. Res. 185, 109438 (2020)
Mead, M., Popoola, O., Stewart, G., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J., McLeod, M., Hodgson, T., Dicks, J., et al.: The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 70, 186–203 (2013)
Mouzourides, P., Kumar, P., Neophytou, M.K.A.: Assessment of long-term measurements of particulate matter and gaseous pollutants in south-east mediterranean. Atmos. Environ. 107, 148–165 (2015)
Mukherjee, A., Agrawal, M.: World air particulate matter: sources, distribution and health effects. Environ. Chem. Lett. 15(2), 283–309 (2017)
Murthy, B., Latha, R., Tiwari, A., Rathod, A., Singh, S., Beig, G.: Impact of mixing layer height on air quality in winter. J. Atmos. Solar Terr. Phys. 197, 105157 (2020)
Pope, C.A., III., Dockery, D.W.: Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manag. Assoc. 56(6), 709–742 (2006)
Qin, X., Hou, L., Gao, J., Si, S.: The evaluation and optimization of calibration methods for low-cost particulate matter sensors: inter-comparison between fixed and mobile methods. Sci. Total Environ. 715, 136791 (2020)
Rai, A.C., Kumar, P., Pilla, F., Skouloudis, A.N., Di Sabatino, S., Ratti, C., Yasar, A., Rickerby, D.: End-user perspective of low-cost sensors for outdoor air pollution monitoring. Sci. Total Environ. 607, 691–705 (2017)
Rasyid, A.R., Bhandary, N.P., Yatabe, R.: Performance of frequency ratio and logistic regression model in creating gis based landslides susceptibility map at lompobattang mountain, indonesia. Geoenviron. Disasters 3(1), 1–16 (2016)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)
Savage, D., Zhang, X., Yu, X., Chou, P., Wang, Q.: Anomaly detection in online social networks. Soc. Netw. 39, 62–70 (2014)
Spinelle, L., Aleixandre, M., Gerboles, M.: Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution. Publication Office of the European Union, Luxembourg (2013)
Sun, L., Wei, J., Duan, D., Guo, Y., Yang, D., Jia, C., Mi, X.: Impact of land-use and land-cover change on urban air quality in representative cities of china. J. Atmos. Solar Terr. Phys. 142, 43–54 (2016)
Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., Biswas, P.: Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Sci. Technol. 49(11), 1063–1077 (2015)
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., et al.: Trends in china’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18(19), 14095–14111 (2018)
Zikova, N., Masiol, M., Chalupa, D.C., Rich, D.Q., Ferro, A.R., Hopke, P.K.: Estimating hourly concentrations of pm2. 5 across a metropolitan area using low-cost particle monitors. Sensors 17(8), 1922 (2017)
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.: Calibration of low-cost particulate matter sensors: model development for a multi-city epidemiological study. Environ. Int. 134, 105329 (2020)
Acknowledgements
The author would like to thank air.bg, luftdaten.info, Sofia Municipality, Air for Health, and Air Solutions for cooperating with data for this research.
The work is supported by National Scientific Fund of Bulgaria under the grant DFNI KP-06-N52/5.
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Zhivkov, P. (2022). Improving Performance of Low-Cost Sensors Using Machine Learning Calibration with a 2-Step Model. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2021. Studies in Computational Intelligence, vol 1044. Springer, Cham. https://doi.org/10.1007/978-3-031-06839-3_21
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