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Input-Adaptive Proxy for Black Carbon as a Virtual Sensor

1
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, FI-00560 Helsinki, Finland
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Helsinki Region Environmental Services Authority (HSY), P.O. Box 100, FI-00066 Helsinki, Finland
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Atmospheric Composition Research, Finnish Meteorological Institute, FI-00560 Helsinki, Finland
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Pegasor Oy, FI-33100 Tampere, Finland
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Department of Computer Science, University of Helsinki, FI-00560 Helsinki, Finland
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Department of Physics, The University of Jordan, Amman 11942, Jordan
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(1), 182; https://doi.org/10.3390/s20010182
Received: 2 December 2019 / Revised: 23 December 2019 / Accepted: 25 December 2019 / Published: 28 December 2019
(This article belongs to the Section Physical Sensors)
Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20–80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86–0.94; urban background: adjR2 = 0.74–0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future. View Full-Text
Keywords: input-adaptive proxy; black carbon; robust linear regression; air quality; street canyon; urban background; virtual sensor input-adaptive proxy; black carbon; robust linear regression; air quality; street canyon; urban background; virtual sensor
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MDPI and ACS Style

Fung, P.L.; Zaidan, M.A.; Sillanpää, S.; Kousa, A.; Niemi, J.V.; Timonen, H.; Kuula, J.; Saukko, E.; Luoma, K.; Petäjä, T.; Tarkoma, S.; Kulmala, M.; Hussein, T. Input-Adaptive Proxy for Black Carbon as a Virtual Sensor. Sensors 2020, 20, 182. https://doi.org/10.3390/s20010182

AMA Style

Fung PL, Zaidan MA, Sillanpää S, Kousa A, Niemi JV, Timonen H, Kuula J, Saukko E, Luoma K, Petäjä T, Tarkoma S, Kulmala M, Hussein T. Input-Adaptive Proxy for Black Carbon as a Virtual Sensor. Sensors. 2020; 20(1):182. https://doi.org/10.3390/s20010182

Chicago/Turabian Style

Fung, Pak L., Martha A. Zaidan, Salla Sillanpää, Anu Kousa, Jarkko V. Niemi, Hilkka Timonen, Joel Kuula, Erkka Saukko, Krista Luoma, Tuukka Petäjä, Sasu Tarkoma, Markku Kulmala, and Tareq Hussein. 2020. "Input-Adaptive Proxy for Black Carbon as a Virtual Sensor" Sensors 20, no. 1: 182. https://doi.org/10.3390/s20010182

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