Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley
Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures...
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ftmdpi:oai:mdpi.com:/2072-4292/12/4/685/ 2023-08-20T03:59:11+02:00 Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley Meytar Sorek-Hamer Meredith Franklin Khang Chau Michael Garay Olga Kalashnikova agris 2020-02-19 application/pdf https://doi.org/10.3390/rs12040685 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12040685 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 4; Pages: 685 AERONET aerosol optical depth (AOD) fine particulate matter (PM 2.5 ) MISR DRAGON Text 2020 ftmdpi https://doi.org/10.3390/rs12040685 2023-07-31T23:08:17Z Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 12 4 685 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
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AERONET aerosol optical depth (AOD) fine particulate matter (PM 2.5 ) MISR DRAGON |
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AERONET aerosol optical depth (AOD) fine particulate matter (PM 2.5 ) MISR DRAGON Meytar Sorek-Hamer Meredith Franklin Khang Chau Michael Garay Olga Kalashnikova Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
topic_facet |
AERONET aerosol optical depth (AOD) fine particulate matter (PM 2.5 ) MISR DRAGON |
description |
Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications. |
format |
Text |
author |
Meytar Sorek-Hamer Meredith Franklin Khang Chau Michael Garay Olga Kalashnikova |
author_facet |
Meytar Sorek-Hamer Meredith Franklin Khang Chau Michael Garay Olga Kalashnikova |
author_sort |
Meytar Sorek-Hamer |
title |
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
title_short |
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
title_full |
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
title_fullStr |
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
title_full_unstemmed |
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley |
title_sort |
spatiotemporal characteristics of the association between aod and pm over the california central valley |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12040685 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 12; Issue 4; Pages: 685 |
op_relation |
https://dx.doi.org/10.3390/rs12040685 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12040685 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
4 |
container_start_page |
685 |
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1774718428349726720 |