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Article: Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling
Title | Satellite-based mapping of daily high-resolution ground PM<inf>2.5</inf> in China via space-time regression modeling |
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Authors | |
Keywords | Aerosol optical depth China Geographically and temporally weighted regression (GTWR) Interior point algorithm (IPA) PM 2.5 |
Issue Date | 2018 |
Citation | Remote Sensing of Environment, 2018, v. 206, p. 72-83 How to Cite? |
Abstract | The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM2.5 for days without PM2.5-AOD paired samples and to generate historical PM2.5 estimates was demonstrated. As a result, fine-scale PM2.5 maps over China were generated, and several PM2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM2.5 observations. |
Persistent Identifier | http://hdl.handle.net/10722/329485 |
ISSN | 2023 Impact Factor: 11.1 2023 SCImago Journal Rankings: 4.310 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | He, Qingqing | - |
dc.contributor.author | Huang, Bo | - |
dc.date.accessioned | 2023-08-09T03:33:07Z | - |
dc.date.available | 2023-08-09T03:33:07Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Remote Sensing of Environment, 2018, v. 206, p. 72-83 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329485 | - |
dc.description.abstract | The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM2.5 for days without PM2.5-AOD paired samples and to generate historical PM2.5 estimates was demonstrated. As a result, fine-scale PM2.5 maps over China were generated, and several PM2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM2.5 observations. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing of Environment | - |
dc.subject | Aerosol optical depth | - |
dc.subject | China | - |
dc.subject | Geographically and temporally weighted regression (GTWR) | - |
dc.subject | Interior point algorithm (IPA) | - |
dc.subject | PM 2.5 | - |
dc.title | Satellite-based mapping of daily high-resolution ground PM<inf>2.5</inf> in China via space-time regression modeling | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.rse.2017.12.018 | - |
dc.identifier.scopus | eid_2-s2.0-85038869847 | - |
dc.identifier.volume | 206 | - |
dc.identifier.spage | 72 | - |
dc.identifier.epage | 83 | - |
dc.identifier.isi | WOS:000427342700006 | - |