| dc.contributor.author | Metia S. | |
| dc.contributor.author | Oduro S.D. | |
| dc.contributor.author | Sinha A.P. | |
| dc.date.accessioned | 2022-10-31T15:05:25Z | |
| dc.date.available | 2022-10-31T15:05:25Z | |
| dc.date.issued | 2020 | |
| dc.identifier.issn | 18761100 | |
| dc.identifier.other | 10.1007/978-981-32-9346-5_2 | |
| dc.identifier.uri | http://41.74.91.244:8080/handle/123456789/416 | |
| dc.description | Metia, S., Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia; Oduro, S.D., Department of Mechanical and Automotive Technology Education College of Technology Education Kumasi, University of Education Winneba, Kumasi, Ghana; Sinha, A.P., Department of Electronics and Communication Engineering, BIT Sindri, Dhanbad, India | en_US |
| dc.description.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. � 2020, Springer Nature Singapore Pte Ltd. | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Air quality index (AQI) | en_US |
| dc.subject | Carbon monoxide (CO) | en_US |
| dc.subject | Unscented Kalman filter (UKF) | en_US |
| dc.title | Pollutant Profile Estimation Using Unscented Kalman Filter | en_US |
| dc.type | Conference Paper | en_US |
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