Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke
•California has high variability in PM2.5 sources, meteorology and topography.•We used ensemble deep learning with multisource big data to improve PM2.5 estimates.•We reliably imputed missing satellite AOD and fused wildfire dispersion estimates.•Our model achieved high PM2.5 prediction performance...
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| Vydané v: | Environment international Ročník 145; s. 106143 |
|---|---|
| Hlavní autori: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Goddard Space Flight Center
Elsevier Ltd
01.12.2020
Elsevier |
| Predmet: | |
| ISSN: | 0160-4120, 1873-6750, 1873-6750 |
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| Abstract | •California has high variability in PM2.5 sources, meteorology and topography.•We used ensemble deep learning with multisource big data to improve PM2.5 estimates.•We reliably imputed missing satellite AOD and fused wildfire dispersion estimates.•Our model achieved high PM2.5 prediction performance with uncertainty estimates.
Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.
Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.
Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.
Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. |
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| AbstractList | •California has high variability in PM2.5 sources, meteorology and topography.•We used ensemble deep learning with multisource big data to improve PM2.5 estimates.•We reliably imputed missing satellite AOD and fused wildfire dispersion estimates.•Our model achieved high PM2.5 prediction performance with uncertainty estimates.
Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.
Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.
Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.
Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g. wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. Estimating PM₂.₅ concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.Using ensemble-based deep learning with big data fused from multiple sources we developed a PM₂.₅ prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM₂.₅ emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM₂.₅ was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.Ensemble deep learning to predict PM₂.₅ achieved an overall mean training RMSE of 1.54 μg/m³ (R²: 0.94) and test RMSE of 2.29 μg/m³ (R²: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM₂.₅ sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m³). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM₂.₅. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM₂.₅ has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. Introduction: Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. Methods: Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. Results: Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. Conclusion: Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.INTRODUCTIONEstimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.METHODSUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.RESULTSEnsemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.CONCLUSIONOur method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. |
| ArticleNumber | 106143 |
| Audience | PUBLIC |
| Author | Wu, Jun Franklin, Meredith Pavlovic, Nathan McClure, Crystal Breton, Carrie Girguis, Mariam Habre, Rima Li, Lianfa Oman, Luke D. Lurmann, Frederick Gilliland, Frank |
| AuthorAffiliation | 1. Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China 3. Sonoma Technology, Inc., Petaluma, CA, USA 5. Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD, USA 4. Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, USA |
| AuthorAffiliation_xml | – name: 3. Sonoma Technology, Inc., Petaluma, CA, USA – name: 1. Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – name: 5. Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD, USA – name: 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China – name: 4. Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, USA |
| Author_xml | – sequence: 1 givenname: Lianfa surname: Li fullname: Li, Lianfa email: lianfali@usc.edu organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 2 givenname: Mariam surname: Girguis fullname: Girguis, Mariam organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 3 givenname: Frederick surname: Lurmann fullname: Lurmann, Frederick organization: Sonoma Technology, Inc., Petaluma, CA, USA – sequence: 4 givenname: Nathan orcidid: 0000-0003-2127-3940 surname: Pavlovic fullname: Pavlovic, Nathan organization: Sonoma Technology, Inc., Petaluma, CA, USA – sequence: 5 givenname: Crystal orcidid: 0000-0001-7477-5528 surname: McClure fullname: McClure, Crystal organization: Sonoma Technology, Inc., Petaluma, CA, USA – sequence: 6 givenname: Meredith surname: Franklin fullname: Franklin, Meredith organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 7 givenname: Jun orcidid: 0000-0002-2693-7112 surname: Wu fullname: Wu, Jun organization: Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, USA – sequence: 8 givenname: Luke D. orcidid: 0000-0002-5487-2598 surname: Oman fullname: Oman, Luke D. organization: Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD, USA – sequence: 9 givenname: Carrie surname: Breton fullname: Breton, Carrie organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 10 givenname: Frank surname: Gilliland fullname: Gilliland, Frank organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 11 givenname: Rima orcidid: 0000-0003-2103-1706 surname: Habre fullname: Habre, Rima email: habre@usc.edu organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA |
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| Keywords | Wildfires High spatiotemporal resolution Air pollution exposure PM2.5 Machine learning California Remote sensing Air Quality Air Pollution Exposure Wildfire Pm2.5 Remote Sensing High Spatiotemporal Resolution Machine Learning |
| Language | English |
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| Notes | GSFC Goddard Space Flight Center ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Crystal McClure: Resource, Data curation Luke D. Oman: Resource, Data curation Rima Habre: Project administration, Investigation, Conceptualization, Writing - Review & Editing Frank Gilliland: Project administration, Writing - Review Carrie Breton: Project administration, Writing - Review Jun Wu: Resources, Data curation, Writing - Review & Editing Lianfa Li: Conceptualization, Methodology, Formal Analysis, Validation, Writing - Original Draft and Revising Mariam Girguis: Investigation, Data curation Frederick Lurmann: Conceptualization, Resource, Data curation, Writing - Review & Editing Nathan Pavlovic: Resource, Data curation, Writing - Review & Editing Meredith Franklin: Conceptualization, Resource, Writing - Review & Editing |
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| Snippet | •California has high variability in PM2.5 sources, meteorology and topography.•We used ensemble deep learning with multisource big data to improve PM2.5... Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies.... Estimating PM₂.₅ concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies.... Introduction: Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health... |
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| SubjectTerms | aerosols air pollution Air pollution exposure air temperature California carbon monoxide cloud cover Computer Programming And Software dust Earth Resources And Remote Sensing environment Environment Pollution Geosciences (General) High spatiotemporal resolution land cover Machine learning meteorology PM2.5 prediction relative humidity Remote sensing smoke snowpack spatial distribution topography traffic uncertainty Wildfires |
| Title | Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke |
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