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Article: Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

TitleEstimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model
Authors
KeywordsBeijing
Nested spatiotemporal statistical model
PM2.5
VIIRS IP AOD
Issue Date2019
PublisherMDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/remotesensing/
Citation
Remote Sensing, 2019, v. 11 n. 7, p. article no. 841 How to Cite?
AbstractSatellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset. © 2019 by the authors.
Persistent Identifierhttp://hdl.handle.net/10722/276375
ISSN
2018 Impact Factor: 4.118
2015 SCImago Journal Rankings: 1.269
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYao, F-
dc.contributor.authorWu, J-
dc.contributor.authorLi, W-
dc.contributor.authorPeng, J-
dc.date.accessioned2019-09-10T03:01:55Z-
dc.date.available2019-09-10T03:01:55Z-
dc.date.issued2019-
dc.identifier.citationRemote Sensing, 2019, v. 11 n. 7, p. article no. 841-
dc.identifier.issn2072-4292-
dc.identifier.urihttp://hdl.handle.net/10722/276375-
dc.description.abstractSatellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset. © 2019 by the authors.-
dc.languageeng-
dc.publisherMDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/remotesensing/-
dc.relation.ispartofRemote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBeijing-
dc.subjectNested spatiotemporal statistical model-
dc.subjectPM2.5-
dc.subjectVIIRS IP AOD-
dc.titleEstimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model-
dc.typeArticle-
dc.identifier.emailLi, W: wfli@hku.hk-
dc.identifier.authorityLi, W=rp01507-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/rs11070841-
dc.identifier.scopuseid_2-s2.0-85069887334-
dc.identifier.hkuros302457-
dc.identifier.volume11-
dc.identifier.issue7-
dc.identifier.spagearticle no. 841-
dc.identifier.epagearticle no. 841-
dc.identifier.isiWOS:000465549300106-
dc.publisher.placeSwitzerland-
dc.identifier.issnl2072-4292-

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