Use of data imputation tools to reconstruct incomplete air quality datasets: a case-study in Temuco, Chile

Research output: Contribution to journalArticle

Authors

  • María Elisa Quinteros
  • Siyao Lu
  • Carola Blazquez
  • Juan Pablo Cárdenas-R
  • Ximena Ossa
  • Pablo Ruiz-Rudolph

External organisations

  • Department of Environmental Sciences/Centre of Excellence in Environmental Studies

Abstract

Missing data from air quality datasets is a common problem, but is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants worldwide occur in these settings and gaps in datasets hinder health studies that could later inform local and international policies. Here, we propose the use of imputation methods as a tool to reconstruct air quality datasets and have applied this approach to an air quality dataset in Temuco, a mid-size city in Chile as a case-study. We attempted to reconstruct the database comparing five approaches: mean imputation, conditional mean imputation, K-Nearest Neighbor imputation, multiple imputation and Bayesian Principal Component Analysis imputation. As a base for the imputation methods, linear regression models were fitted for PM2.5 against other air quality and meteorological variables. Methods were challenged against validation sets where data was removed artificially. Imputation methods were able to reconstruct the dataset with good performance in terms of completeness, errors, and bias, even when challenged against the validations sets. The performance improved when including covariates from a second monitoring station in Temuco. K-Nearest Neighbor imputation showed slightly better performance than multiple imputation for error (25% vs. 27%) and bias (2.1% vs. 3.9%), but presented lower completeness (70% vs. 100%). In summary, our results show that the imputation methods can be a useful tool in reconstructing air quality datasets in a real-life situation.

Details

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalAtmospheric Environment
Volume200
Early online date7 Dec 2018
Publication statusPublished - 1 Mar 2019

Keywords

  • Air pollution, Environmental epidemiology, Missing data, Multiple imputation, Single imputation, Wood-burning