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:: Volume 8, Issue 3 (Summer 2021) ::
Environ. health eng. manag. 2021, 8(3): 215-226 Back to browse issues page
Application of imputation methods for missing values of PM10 and O3 data: Interpolation, moving average and K-nearest neighbor methods
Parisa Saeipourdizaj , Parvin Sarbakhsh , Akbar Gholampour
Corresponding author: Health and Environment Research Center, Tabriz University of Medical Sciences, Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran , p.sarbakhsh@gmail.com
Abstract:   (262 Views)
Background: PIn air quality studies, it is very often to have missing data due to reasons such as machine failure or human error. The approach used in dealing with such missing data can affect the results of the analysis. The main aim of this study was to review the types of missing mechanism, imputation methods, application of some of them in imputation of missing of PM10 and O3 in Tabriz, and compare their efficiency.
Methods: Methods of mean, EM algorithm, regression, classification and regression tree, predictive mean matching (PMM), interpolation, moving average, and K-nearest neighbor (KNN) were used. PMM was investigated by considering the spatial and temporal dependencies in the model. Missing data were randomly simulated with 10, 20, and 30% missing values. The efficiency of methods was compared using coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE).
Results: Based on the results for all indicators, interpolation, moving average, and KNN had the best performance, respectively. PMM did not perform well with and without spatio-temporal information.
Conclusion: Given that the nature of pollution data always depends on next and previous information, methods that their computational nature is based on before and after information indicated better performance than others, so in the case of pollutant data, it is recommended to use these methods.
Keywords: Air pollution, Algorithms, Environmental pollutants, Spatio-temporal analysis, Humans
eprint link: http://eprints.kmu.ac.ir/id/eprint/38224
Full-Text [PDF 1133 kb]   (132 Downloads)    
Type of Study: Original Article | Subject: General
Received: 2021/09/19 | Accepted: 2021/08/1 | Published: 2021/09/26
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Saeipourdizaj P, Sarbakhsh P, Gholampour A. Application of imputation methods for missing values of PM10 and O3 data: Interpolation, moving average and K-nearest neighbor methods. Environ. health eng. manag.. 2021; 8 (3) :215-226
URL: http://ehemj.com/article-1-815-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 8, Issue 3 (Summer 2021) Back to browse issues page
مجله مدیریت و مهندسی بهداشت محیط Environmental Health Engineering And Management Journal
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