Articles | Volume 14, issue 12
https://doi.org/10.5194/acp-14-6049-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-14-6049-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Impact of data quality and surface-to-column representativeness on the PM2.5 / satellite AOD relationship for the contiguous United States
T. D. Toth
Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND, USA
Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND, USA
J. R. Campbell
Aerosol and Radiation Sciences Section, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
E. J. Hyer
Aerosol and Radiation Sciences Section, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
J. S. Reid
Aerosol and Radiation Sciences Section, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND, USA
D. L. Westphal
Aerosol and Radiation Sciences Section, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
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- Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS J. Handschuh et al. 10.1016/j.rsase.2022.100716
- On the opposite seasonality of MODIS AOD and surface PM2.5 over the Northern China plain J. Xu et al. 10.1016/j.atmosenv.2019.116909
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- Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017 B. Lyu et al. 10.1021/acs.est.9b01117
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49 citations as recorded by crossref.
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- Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States K. Kaku et al. 10.1029/2017JD028074
- Spatial and temporal properties of a winter dust event in North China S. Shao et al. 10.1016/j.cacint.2020.100025
- A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth Y. Chu et al. 10.3390/atmos7100129
- Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. 10.3390/rs15082064
- Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke L. Li et al. 10.1016/j.envint.2020.106143
- Characterizing a persistent Asian dust transport event: Optical properties and impact on air quality through the ground-based and satellite measurements over Nanjing, China Y. Han et al. 10.1016/j.atmosenv.2015.05.048
- Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements A. Baron et al. 10.5194/acp-20-6749-2020
- Atmospheric aerosol variability above the Paris Area during the 2015 heat wave - Comparison with the 2003 and 2006 heat waves P. Chazette et al. 10.1016/j.atmosenv.2017.09.055
- PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China N. Chen et al. 10.3390/ijgi10010031
- Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements I. Rogozovsky et al. 10.1016/j.atmosenv.2020.118163
- Evaluation of the representativeness of ground-based visibility for analysing the spatial and temporal variability of aerosol optical thickness in China Z. Zhang et al. 10.1016/j.atmosenv.2016.09.060
- Impact of biogenic emissions of organic matter from a cool-temperate forest on aerosol optical properties A. Müller et al. 10.1016/j.atmosenv.2020.117413
- Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations T. Toth et al. 10.1016/j.atmosenv.2022.118979
- Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products T. Toth et al. 10.5194/amt-11-499-2018
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke H. Preisler et al. 10.1016/j.envpol.2015.06.018
- Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States V. Buchard et al. 10.1016/j.atmosenv.2015.11.004
- Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia F. Tan et al. 10.5194/acp-15-3755-2015
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- Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data K. Bai et al. 10.1016/j.envpol.2019.04.104
- Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data M. Al-Hamdan et al. 10.1007/s10661-019-7414-3
- Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset Q. Chen et al. 10.1016/j.apr.2019.07.016
- Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. 10.1002/2016JD025798
- Spatial characteristics and temporal evolution of the relationship between PM2.5 and aerosol optical depth over the eastern USA during 2003–2017 Q. Jin et al. 10.1016/j.atmosenv.2020.117718
- Assessment of MODIS, OMI, MISR and CALIOP Aerosol Products for Estimating Surface Visual Range: A Mathematical Model for Hong Kong M. Shahzad et al. 10.3390/rs10091333
- Influence of cloud, fog, and high relative humidity during pollution transport events in South Korea: Aerosol properties and PM2.5 variability T. Eck et al. 10.1016/j.atmosenv.2020.117530
- A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. 10.5194/amt-12-1739-2019
- Temporal variability of aerosol optical thickness vertical distribution observed from CALIOP T. Toth et al. 10.1002/2015JD024668
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- A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid M. Bilal et al. 10.3390/rs10030463
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- Autoregressive spatially varying coefficients model for predicting daily PM<sub>2.5</sub> using VIIRS satellite AOT E. Schliep et al. 10.5194/ascmo-1-59-2015
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- Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter B. Ford & C. Heald 10.5194/acp-16-3499-2016
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- Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China X. Yan et al. 10.1016/j.atmosenv.2017.09.023
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- On the opposite seasonality of MODIS AOD and surface PM2.5 over the Northern China plain J. Xu et al. 10.1016/j.atmosenv.2019.116909
- PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models H. Chu & M. Bilal 10.1007/s11356-018-3763-7
3 citations as recorded by crossref.
- Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017 B. Lyu et al. 10.1021/acs.est.9b01117
- The relation between columnar and surface aerosol optical properties in a background environment D. Szczepanik & K. Markowicz 10.1016/j.apr.2017.10.001
- How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States? J. Li et al. 10.1016/j.atmosenv.2014.12.010
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