54 citations as recorded by crossref.

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25. Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. 10.1002/2016JD025798
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27. 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
28. 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
29. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. 10.5194/amt-12-1739-2019
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43. 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
44. 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
45. Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem A. Raman et al. 10.1016/j.atmosenv.2020.118134
46. Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois M. Flesch et al. 10.1021/acsearthspacechem.2c00207
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48. 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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50. 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
51. 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
52. 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
53. The relation between columnar and surface aerosol optical properties in a background environment D. Szczepanik & K. Markowicz 10.1016/j.apr.2017.10.001
54. 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