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Comparing Methods to Impute Missing Daily Ground-Level PM 10 Concentrations between 2010–2017 in South Africa

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  • Oluwaseyi Olalekan Arowosegbe

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Martin Röösli

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Nino Künzli

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Apolline Saucy

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Temitope Christina Adebayo-Ojo

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Mohamed F. Jeebhay

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa)

  • Mohammed Aqiel Dalvie

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa)

  • Kees de Hoogh

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

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 (PM 10 ) 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 PM 10 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 PM 10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM 10 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 PM 10 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.

Suggested Citation

  • Oluwaseyi Olalekan Arowosegbe & Martin Röösli & Nino Künzli & Apolline Saucy & Temitope Christina Adebayo-Ojo & Mohamed F. Jeebhay & Mohammed Aqiel Dalvie & Kees de Hoogh, 2021. "Comparing Methods to Impute Missing Daily Ground-Level PM 10 Concentrations between 2010–2017 in South Africa," IJERPH, MDPI, vol. 18(7), pages 1-13, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3374-:d:523488
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Abioye O. Fayiga & Mabel O. Ipinmoroti & Tait Chirenje, 2018. "Environmental pollution in Africa," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(1), pages 41-73, February.
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    Cited by:

    1. Temitope Christina Adebayo-Ojo & Janine Wichmann & Oluwaseyi Olalekan Arowosegbe & Nicole Probst-Hensch & Christian Schindler & Nino Künzli, 2022. "Short-Term Joint Effects of PM 10 , NO 2 and SO 2 on Cardio-Respiratory Disease Hospital Admissions in Cape Town, South Africa," IJERPH, MDPI, vol. 19(1), pages 1-19, January.
    2. Oluwaseyi Olalekan Arowosegbe & Martin Röösli & Temitope Christina Adebayo-Ojo & Mohammed Aqiel Dalvie & Kees de Hoogh, 2021. "Spatial and Temporal Variations in PM 10 Concentrations between 2010–2017 in South Africa," IJERPH, MDPI, vol. 18(24), pages 1-12, December.

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