Arowosegbe, O. O. and Röösli, M. and Künzli, N. and Saucy, A. and Adebayo-Ojo, T. C. and Jeebhay, M. F. and Dalvie, M. A. and de Hoogh, K.. (2021) Comparing methods to impute missing daily ground-level PM10 concentrations between 2010-2017 in South Africa. Int J Environ Res Public Health, 18 (7). p. 3374.
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Official URL: https://edoc.unibas.ch/88904/
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Abstract
Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.
Faculties and Departments: | 03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Air Pollution and Health (Künzli) 09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Former Units within Swiss TPH > Air Pollution and Health (Künzli) 09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) 09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Environmental Exposures and Health Systems Research > Physical Hazards and Health (Röösli) |
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UniBasel Contributors: | Arowosegbe, Oluwaseyi Olalekan and Röösli, Martin and Künzli, Nino and Saucy, Apolline and Adebayo, Temitope and de Hoogh, Kees |
Item Type: | Article, refereed |
Article Subtype: | Research Article |
ISSN: | 1660-4601 (Electronic)1660-4601 (Linking) |
Note: | Publication type according to Uni Basel Research Database: Journal article |
Language: | English |
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Last Modified: | 19 Dec 2022 11:40 |
Deposited On: | 19 Dec 2022 11:40 |
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