Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa
Journal Article
2021-03-24
Permanent link to this Item
Authors
Journal Title
International Journal of Environmental Research and Public Health
Link to Journal
Journal ISSN
Volume Title
Publisher
Publisher
Series
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<sub>10</sub>) 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<sub>10</sub> 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<sub>10</sub> data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM<sub>10</sub> 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<sub>10</sub> 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.
Description
Reference:
International Journal of Environmental Research and Public Health 18 (7): 3374 (2021)
International Journal of Environmental Research and Public Health 18 (7): 3374 (2021)