Abstract
In this paper, we develop an estimation model for carbon monoxide (CO) air pollution concentrations. CO is an important pollutant which is used to calculate an air quality index (AQI). AQI becomes less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents the Unscented Kalman filter (UKF) to predict missing data of atmospheric carbon monoxide concentrations using the time series data of monitoring stations.
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Metia, S., Oduro, S.D., Sinha, A.P. (2020). Pollutant Profile Estimation Using Unscented Kalman Filter. In: Basu, T., Goswami, S., Sanyal, N. (eds) Advances in Control, Signal Processing and Energy Systems. Lecture Notes in Electrical Engineering, vol 591. Springer, Singapore. https://doi.org/10.1007/978-981-32-9346-5_2
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DOI: https://doi.org/10.1007/978-981-32-9346-5_2
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