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Article

Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa

1
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
2
Faculty of Science, University of Basel, CH-4003 Basel, Switzerland
3
Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa
*
Author to whom correspondence should be addressed.
Academic Editor: Tiziana Schilirò
Int. J. Environ. Res. Public Health 2021, 18(7), 3374; https://doi.org/10.3390/ijerph18073374
Received: 31 January 2021 / Revised: 11 March 2021 / Accepted: 22 March 2021 / Published: 24 March 2021
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. View Full-Text
Keywords: air pollution; Random Forest; imputation; particulate matter; environmental exposure; South Africa air pollution; Random Forest; imputation; particulate matter; environmental exposure; South Africa
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MDPI and ACS Style

Arowosegbe, O.O.; Röösli, M.; Künzli, N.; Saucy, A.; Adebayo-Ojo, T.C.; Jeebhay, M.F.; Dalvie, M.A.; de Hoogh, K. Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa. Int. J. Environ. Res. Public Health 2021, 18, 3374. https://doi.org/10.3390/ijerph18073374

AMA Style

Arowosegbe OO, Röösli M, Künzli N, Saucy A, Adebayo-Ojo TC, Jeebhay MF, Dalvie MA, de Hoogh K. Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa. International Journal of Environmental Research and Public Health. 2021; 18(7):3374. https://doi.org/10.3390/ijerph18073374

Chicago/Turabian Style

Arowosegbe, Oluwaseyi O.; Röösli, Martin; Künzli, Nino; Saucy, Apolline; Adebayo-Ojo, Temitope C.; Jeebhay, Mohamed F.; Dalvie, Mohammed A.; de Hoogh, Kees. 2021. "Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa" Int. J. Environ. Res. Public Health 18, no. 7: 3374. https://doi.org/10.3390/ijerph18073374

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