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

International Journal of Environmental Research and Public Health

Mohamed F. Jeebhay¹  Mohammed Aqiel Dalvie¹  Nino Künzli²  Martin Röösli²  Keesde Hoogh²  Oluwaseyi Olalekan Arowosegbe²  Apolline Saucy²  Temitope Christina Adebayo-Ojo² 

[1] Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa;Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland;

关键词: air pollution;    Random Forest;    imputation;    particulate matter;    environmental exposure;    South Africa;   

DOI  :  10.3390/ijerph18073374

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown