27 citations as recorded by crossref.

1. Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis X. Liu et al. 10.1016/j.jclepro.2020.121709
2. Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 Q. He et al. 10.1016/j.envint.2021.106726
3. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China J. Wei et al. 10.1016/j.envint.2020.106290
4. Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies J. Wu et al. 10.1016/j.envpol.2021.117138
5. Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
6. Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. 10.3390/rs14030623
7. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China L. He et al. 10.1016/j.atmosenv.2021.118265
8. An episode of transboundary air pollution in the central Himalayas during agricultural residue burning season in North India S. Khanal et al. 10.1016/j.apr.2021.101270
9. Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data D. Lu et al. 10.3390/rs13081423
10. Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea P. Ettehadi Osgouei et al. 10.1016/j.atmosenv.2021.118784
11. 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
12. Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017 S. Li et al. 10.1016/j.scitotenv.2019.135755
13. Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
14. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
15. Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging Y. Wang et al. 10.1016/j.atmosenv.2020.117548
16. Should There Be Industrial Agglomeration in Sustainable Cities?: A Perspective Based on Haze Pollution P. Dai & Y. Lin 10.3390/su13126609
17. A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T. Li et al. 10.1109/JSTARS.2020.2977668
18. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8 T. Su et al. 10.1016/j.rse.2020.112093
19. Validation of the aerosol optical property products derived by the GRASP/Component approach from multi-angular polarimetric observations X. Zhang et al. 10.1016/j.atmosres.2021.105802
20. High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
21. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
22. Efforts in reducing air pollution exposure risk in China: State versus individuals B. Zou et al. 10.1016/j.envint.2020.105504
23. Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2) M. Nair et al. 10.1016/j.apr.2021.101259
24. Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks L. Zhang et al. 10.3390/rs13193834
25. The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth Q. He et al. 10.5194/acp-21-18375-2021
26. Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data Q. He et al. 10.1016/j.envint.2020.105536
27. What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018 Q. He et al. 10.1016/j.envint.2022.107110