Articles | Volume 19, issue 1
https://doi.org/10.5194/acp-19-295-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-295-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2019
Research article |
| 09 Jan 2019
| 09 Jan 2019
Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth
Department of Earth and Environmental Sciences of Columbia University, New
York, NY, USA
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
Arlene M. Fiore
Department of Earth and Environmental Sciences of Columbia University, New
York, NY, USA
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
Gabriele Curci
Department of Physical and Chemical Sciences, University of L'Aquila,
L'Aquila, Italy
Center of Excellence for the Forecast of Severe Weather, University of
L'Aquila, L'Aquila, Italy
Alexei Lyapustin
NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
Kevin Civerolo
New York State Department of Environmental Conservation, Albany, NY, USA
Michael Ku
New York State Department of Environmental Conservation, Albany, NY, USA
Aaron van Donkelaar
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS,
Canada
Randall V. Martin
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS,
Canada
Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for
Astrophysics, Cambridge, MA, USA
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Cited
22 citations as recorded by crossref.
- A national crowdsourced network of low-cost fine particulate matter and aerosol optical depth monitors: results from the 2021 wildfire season in the United States E. Wendt et al. 10.1039/D3EA00086A
- High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method A. Chen et al. 10.1016/j.scitotenv.2022.159673
- Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty A. van Donkelaar et al. 10.1021/acs.est.1c05309
- Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring C. Lin et al. 10.1016/j.atmosenv.2020.117410
- Environmental Degradation and Public Opinion: The Case of Air Pollution in Vietnam S. Kim et al. 10.1177/1070496519888252
- Satellite mapping of PM<sub>2.5</sub> episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor R. Chatfield et al. 10.5194/acp-20-4379-2020
- A Development of PM Concentration Reanalysis Method using CMAQ with Surface Data Assimilation and MAIAC AOD in Korea Y. Koo et al. 10.5572/KOSAE.2020.36.4.558
- Aerosol Optical Thickness: Organic Composition, Associated Particle Water, and Aloft Extinction A. Christiansen et al. 10.1021/acsearthspacechem.8b00163
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements E. Wendt et al. 10.5194/amt-14-6023-2021
- Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality M. Alifa et al. 10.1016/j.envres.2022.113587
- Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation L. Li 10.3390/rs12030492
- Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. 10.1016/j.envint.2020.106057
- Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model T. Jiang et al. 10.1016/j.atmosres.2020.105146
- Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification Q. Pu & E. Yoo 10.1016/j.envpol.2021.116574
- Aerosol optical depth and water vapor variability assessed through autocorrelation analysis M. Franco et al. 10.1007/s00703-024-01011-5
- Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
- Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois M. Flesch et al. 10.1021/acsearthspacechem.2c00207
- Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm N. Meskhidze et al. 10.1016/j.atmosenv.2021.118250
- Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al. 10.1080/10962247.2019.1668498
- An episode of transboundary air pollution in the central Himalayas during agricultural residue burning season in North India S. Khanal et al. 10.1016/j.apr.2021.101270
- Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA X. Jin et al. 10.1088/1748-9326/ab2dcb
- Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo A. Damascena et al. 10.1016/j.atmosenv.2020.117949
22 citations as recorded by crossref.
- A national crowdsourced network of low-cost fine particulate matter and aerosol optical depth monitors: results from the 2021 wildfire season in the United States E. Wendt et al. 10.1039/D3EA00086A
- High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method A. Chen et al. 10.1016/j.scitotenv.2022.159673
- Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty A. van Donkelaar et al. 10.1021/acs.est.1c05309
- Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring C. Lin et al. 10.1016/j.atmosenv.2020.117410
- Environmental Degradation and Public Opinion: The Case of Air Pollution in Vietnam S. Kim et al. 10.1177/1070496519888252
- Satellite mapping of PM<sub>2.5</sub> episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor R. Chatfield et al. 10.5194/acp-20-4379-2020
- A Development of PM Concentration Reanalysis Method using CMAQ with Surface Data Assimilation and MAIAC AOD in Korea Y. Koo et al. 10.5572/KOSAE.2020.36.4.558
- Aerosol Optical Thickness: Organic Composition, Associated Particle Water, and Aloft Extinction A. Christiansen et al. 10.1021/acsearthspacechem.8b00163
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements E. Wendt et al. 10.5194/amt-14-6023-2021
- Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality M. Alifa et al. 10.1016/j.envres.2022.113587
- Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation L. Li 10.3390/rs12030492
- Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. 10.1016/j.envint.2020.106057
- Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model T. Jiang et al. 10.1016/j.atmosres.2020.105146
- Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification Q. Pu & E. Yoo 10.1016/j.envpol.2021.116574
- Aerosol optical depth and water vapor variability assessed through autocorrelation analysis M. Franco et al. 10.1007/s00703-024-01011-5
- Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
- Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois M. Flesch et al. 10.1021/acsearthspacechem.2c00207
- Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm N. Meskhidze et al. 10.1016/j.atmosenv.2021.118250
- Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al. 10.1080/10962247.2019.1668498
- An episode of transboundary air pollution in the central Himalayas during agricultural residue burning season in North India S. Khanal et al. 10.1016/j.apr.2021.101270
- Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA X. Jin et al. 10.1088/1748-9326/ab2dcb
- Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo A. Damascena et al. 10.1016/j.atmosenv.2020.117949
Discussed (preprint)
Latest update: 19 Apr 2024
Short summary
We use a forward geophysical approach to derive surface PM2.5 distribution from satellite AOD over the northeastern US by applying relationships between surface PM2.5 and column AOD from a regional air quality model (CMAQ). We use multi-platform surface, aircraft, and radiosonde measurements to quantify different sources of uncertainties. We highlight model representation of aerosol vertical distribution and speciation as major sources of uncertainties for satellite-derived PM2.5.
We use a forward geophysical approach to derive surface PM2.5 distribution from satellite AOD...
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