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dc.contributor.authorHadeed, Steven J
dc.contributor.authorO'Rourke, Mary Kay
dc.contributor.authorBurgess, Jefferey L
dc.contributor.authorHarris, Robin B
dc.contributor.authorCanales, Robert A
dc.date.accessioned2020-07-06T19:57:14Z
dc.date.available2020-07-06T19:57:14Z
dc.date.issued2020-08-15
dc.identifier.citationHadeed, S. J., O'Rourke, M. K., Burgess, J. L., Harris, R. B., & Canales, R. A. (2020). Imputation methods for addressing missing data in short-term monitoring of air pollutants. Science of The Total Environment, 139140. https://doi.org/10.1016/j.scitotenv.2020.139140en_US
dc.identifier.issn0048-9697
dc.identifier.pmid32402974
dc.identifier.doi10.1016/j.scitotenv.2020.139140
dc.identifier.urihttp://hdl.handle.net/10150/641798
dc.description.abstractMonitoring of environmental contaminants is a critical part of exposure sciences research and public health practice. Missing data are often encountered when performing short-term monitoring (<24 h) of air pollutants with real-time monitors, especially in resource-limited areas. Approaches for handling consecutive periods of missing and incomplete data in this context remain unclear. Our aim is to evaluate existing imputation methods for handling missing data for real-time monitors operating for short durations. In a current field-study, realtime PM2.5 monitors were placed outside of 20 households and ran for 24-hours. Missing data was simulated in these households at four consecutive periods of missingness (20%, 40%, 60%, 80%). Univariate (Mean, Median, Last Observation Carried Forward, Kalman Filter, Random, Markov) and multivariate time-series (Predictive Mean Matching, Row Mean Method) methods were used to impute missing concentrations, and performance was evaluated using five error metrics (Absolute Bias, Percent Absolute Error in Means, R2 Coefficient of Determination, Root Mean Square Error, Mean Absolute Error). Univariate methods of Markov, random, and mean imputations were the best performingmethods that yielded 24-hour mean concentrations with the lowest error and highest R2 values across all levels of missingness. When evaluating error metrics minute-by-minute, Kalman filters, median, and Markov methods performed well at low levels of missingness (20-40%). However, at higher levels of missingness (60-80%), Markov, random, median, and mean imputation performed best on average. Multivariate methods were the worst performing imputation methods across all levels of missingness. Imputation using univariate methods may provide a reasonable solution to addressing missing data for short-term monitoring of air pollutants, especially in resource-limited areas. Further efforts are needed to evaluate imputation methods that are generalizable across a diverse range of study environments. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.rightsCopyright © 2020 Elsevier B.V. All rights reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectAmbient PM2.5en_US
dc.subjectImputationen_US
dc.subjectMissing Dataen_US
dc.subjectReal-time monitoringen_US
dc.titleImputation methods for addressing missing data in short-term monitoring of air pollutantsen_US
dc.typeArticleen_US
dc.identifier.eissn1879-1026
dc.contributor.departmentUniv Arizona, Mel & Enid Zuckerman Coll Publ Hlthen_US
dc.contributor.departmentUniv Arizona, Interdisciplinary Program Appl Mathen_US
dc.identifier.journalSCIENCE OF THE TOTAL ENVIRONMENTen_US
dc.description.note24 month embargo; published online: 3 May 2020en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleThe Science of the total environment
dc.source.volume730
dc.source.beginpage139140
dc.source.endpage
dc.source.countryNetherlands


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