50 citations as recorded by crossref.

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4. Validation of the Atmospheric Boundary Layer Height Estimated from the MODIS Atmospheric Profile Data at an Equatorial Site S. Onyango et al. https://doi.org/10.3390/atmos11090908
5. Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia M. Mazeli et al. https://doi.org/10.1016/j.scitotenv.2023.162130
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14. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. https://doi.org/10.1016/j.atmosres.2017.04.019
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18. Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China W. Guo et al. https://doi.org/10.1016/j.apr.2021.101154
19. Estimation of Particulate Matter (PM2.5) Over Kolkata J. Singh et al. https://doi.org/10.1007/s00024-023-03418-4
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21. Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China X. Mao et al. https://doi.org/10.1016/j.apr.2017.04.002
22. Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates Y. Aliyu & J. Botai https://doi.org/10.1016/j.atmosenv.2018.09.001
23. Air Quality Assessment over Sudan using NASA Remote Sensing Satellites Data and MERRA-2 Model I. Muntasir et al. https://doi.org/10.30799/jespr.207.20060302
24. Assessment of CALIOP and MODIS aerosol products over Iran to explore air quality S. Zahedi Asl et al. https://doi.org/10.1007/s00704-018-2555-9
25. PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India P. Gogikar et al. https://doi.org/10.1007/s12652-020-02457-2
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27. Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India S. Chitranshi et al. https://doi.org/10.1007/s11869-014-0271-x
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33. Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters S. Sahu et al. https://doi.org/10.1016/j.chemosphere.2020.126969
34. Validation of MERIS/AATSR synergy algorithm for aerosol retrieval against globally distributed AERONET observations and comparison with MODIS aerosol product N. Benas et al. https://doi.org/10.1016/j.atmosres.2013.05.011
35. Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5 T. Narayan et al. https://doi.org/10.1007/s40010-020-00718-5
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37. Temporal and spatial variation of particulate matter and its correlation with other criteria of air pollutants in Lanzhou, China, in spring-summer periods M. Filonchyk et al. https://doi.org/10.1016/j.apr.2018.04.011
38. A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval W. You et al. https://doi.org/10.1016/j.atmosres.2015.09.008
39. Fall of oxidized while rise of reduced reactive nitrogen deposition in China L. Liu et al. https://doi.org/10.1016/j.jclepro.2020.122875
40. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China W. Song et al. https://doi.org/10.1016/j.rse.2014.08.008
41. Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models W. You et al. https://doi.org/10.1016/j.scitotenv.2014.11.024
42. COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity A. Gayen et al. https://doi.org/10.1016/j.uclim.2021.100786
43. Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes C. Lin et al. https://doi.org/10.3390/ijerph13060553
44. Calculation of near-surface atmospheric particulate matter (PM2.5) concentrations in China with the use of ACDL/DQ-1 Y. Zhang et al. https://doi.org/10.1016/j.atmosres.2025.108299
45. Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD W. You et al. https://doi.org/10.1007/s11356-015-6027-9
46. Years of Life Lost Attributable to Estimated Air PM 2.5 Using High-Resolution Satellite Data in a Region of Iran S. Soleimani et al. https://doi.org/10.1177/11786302251329853
47. Comparison of different models for assessing air quality in Krasnoyarsk using satellite data K. Krasnoshchekov et al. https://doi.org/10.1051/e3sconf/202022303022
48. WITHDRAWN: Prevention and control of motor vehicle exhaust pollution based on internet of things system and cloud computing Y. Zhang et al. https://doi.org/10.1016/j.micpro.2020.103373
49. Estimation of PM 2.5 using high-resolution satellite data and its mortality risk in an area of Iran G. Li et al. https://doi.org/10.1080/09603123.2024.2325629
50. Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT‐PM10 regression model with synoptic meteorology classification K. Leelasakultum & N. Kim Oanh https://doi.org/10.1002/2017GH000059