53 citations as recorded by crossref.

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26. Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. https://doi.org/10.1002/2016JD025798
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28. Assessment of MODIS, OMI, MISR and CALIOP Aerosol Products for Estimating Surface Visual Range: A Mathematical Model for Hong Kong M. Shahzad et al. https://doi.org/10.3390/rs10091333
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34. A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid M. Bilal et al. https://doi.org/10.3390/rs10030463
35. 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. https://doi.org/10.1016/j.atmosenv.2020.117949
36. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth X. Jin et al. https://doi.org/10.5194/acp-19-295-2019
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41. A New Satellite-Based Global Climatology of Dust Aerosol Optical Depth K. Voss & A. Evan https://doi.org/10.1175/JAMC-D-19-0194.1
42. Mapping 532 nm lidar ratios for CALIPSO-classified marine aerosols using MODIS AOD constrained retrievals and GOCART model simulations T. Toth et al. https://doi.org/10.5194/amt-18-6765-2025
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45. 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. https://doi.org/10.1016/j.rse.2020.112136
46. Is the Air Too Polluted for Outdoor Activities? Check by Using Your Photovoltaic System as an Air-Quality Monitoring Device S. Lolli https://doi.org/10.3390/s21196342
47. Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem A. Raman et al. https://doi.org/10.1016/j.atmosenv.2020.118134
48. Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois M. Flesch et al. https://doi.org/10.1021/acsearthspacechem.2c00207
49. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. https://doi.org/10.1016/j.earscirev.2023.104461
50. Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China X. Yan et al. https://doi.org/10.1016/j.atmosenv.2017.09.023
51. Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS J. Handschuh et al. https://doi.org/10.1016/j.rsase.2022.100716
52. On the opposite seasonality of MODIS AOD and surface PM2.5 over the Northern China plain J. Xu et al. https://doi.org/10.1016/j.atmosenv.2019.116909
53. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models H. Chu & M. Bilal https://doi.org/10.1007/s11356-018-3763-7