52 citations as recorded by crossref.

1. Extreme Aerosol Events Over Eastern North America: 1. Characterizing and Simulating Historical Events Y. Guo et al. 10.1029/2020JD033758
2. 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
3. Spatial and temporal properties of a winter dust event in North China S. Shao et al. 10.1016/j.cacint.2020.100025
4. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth Y. Chu et al. 10.3390/atmos7100129
5. Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. 10.3390/rs15082064
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations T. Toth et al. 10.1016/j.atmosenv.2022.118979
15. 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
16. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
17. 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
18. 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
19. 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
20. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. 10.1016/j.atmosenv.2019.04.020
21. 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
22. 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
23. 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
24. Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. 10.1002/2016JD025798
25. 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
26. 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
27. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. 10.5194/amt-12-1739-2019
28. Temporal variability of aerosol optical thickness vertical distribution observed from CALIOP T. Toth et al. 10.1002/2015JD024668
29. Determining the Impact of Wildland Fires on Ground Level Ambient Ozone Levels in California R. Cisneros et al. 10.3390/atmos11101131
30. 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
31. 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
32. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth X. Jin et al. 10.5194/acp-19-295-2019
33. 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
34. A study of the impact of spatial resolution on the estimation of particle matter concentration from the aerosol optical depth retrieved from satellite observations L. Mei et al. 10.1080/01431161.2019.1601279
35. 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
36. Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data K. Bai et al. 10.1016/j.jenvman.2018.12.071
37. A New Satellite-Based Global Climatology of Dust Aerosol Optical Depth K. Voss & A. Evan 10.1175/JAMC-D-19-0194.1
38. Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model G. Geng et al. 10.1016/j.rse.2015.05.016
39. Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China Q. Xu et al. 10.3390/rs14225841
40. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications J. Wei et al. 10.1016/j.rse.2020.112136
41. Is the Air Too Polluted for Outdoor Activities? Check by Using Your Photovoltaic System as an Air-Quality Monitoring Device S. Lolli 10.3390/s21196342
42. Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem A. Raman et al. 10.1016/j.atmosenv.2020.118134
43. Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois M. Flesch et al. 10.1021/acsearthspacechem.2c00207
44. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
45. Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China X. Yan et al. 10.1016/j.atmosenv.2017.09.023
46. Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS J. Handschuh et al. 10.1016/j.rsase.2022.100716
47. 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
48. 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
49. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China W. Gong et al. 10.3390/rs9101038
50. 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
51. The relation between columnar and surface aerosol optical properties in a background environment D. Szczepanik & K. Markowicz 10.1016/j.apr.2017.10.001
52. 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