70 citations as recorded by crossref.

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2. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China J. Wei et al. 10.1016/j.envint.2020.106290
3. 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
4. Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. 10.3390/rs14030623
5. 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
6. 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
7. Particulate air pollution in Durban: Characteristics and its relationship with 1 km resolution satellite aerosol optical depth B. Mahesh et al. 10.1016/j.asr.2022.04.053
8. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. 10.3390/rs14236113
9. Correlation between biomass burning and air pollution in China: Spatial heterogeneity and corresponding factors S. Wang et al. 10.1016/j.gloplacha.2022.103823
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11. 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
12. 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
13. Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war D. Jiadan et al. 10.1016/j.atmosenv.2023.120114
14. Generating Hourly Fine Seamless Aerosol Optical Depth Products by Fusing Multiple Satellite and Numerical Model Data B. Zou et al. 10.1109/TGRS.2024.3385397
15. Multi-spatiotemporal AOD trends and association with land use changes over the Guangdong-Hong Kong-Macao Greater Bay Area during 2001–2021 X. Yu et al. 10.1007/s11356-023-25451-5
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17. Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China Z. Shi et al. 10.3390/ijerph20021627
18. 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
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20. 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
21. Comprehensive evaluation of multisource aerosol optical depth gridded products over China D. Jiang et al. 10.1016/j.atmosenv.2022.119088
22. Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China J. Jiang et al. 10.3390/rs15010275
23. Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China S. Li et al. 10.1109/JSTARS.2023.3238713
24. 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
25. A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020 B. Chen et al. 10.1080/15481603.2022.2051382
26. Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. 10.1016/j.apr.2022.101579
27. National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) measurements: Supporting satellite aerosol product validation for different surface types over China T. Cheng et al. 10.1016/j.atmosres.2022.106508
28. A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
29. 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
30. Heterogeneous air pollution controls its correlation to urban heat island: A satellite perspective Y. Ding et al. 10.1016/j.asr.2022.03.027
31. 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
32. 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
33. 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
34. Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales X. Su et al. 10.1016/j.scitotenv.2023.163794
35. Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
36. Uncertainty Analysis of Premature Death Estimation Under Various Open PM2.5 Datasets J. Liu et al. 10.3389/fenvs.2022.934281
37. Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction H. Zhu et al. 10.1016/j.atmosres.2022.106374
38. Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale W. Li et al. 10.3390/rs14112544
39. Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods M. Bagherinia et al. 10.1080/10106049.2023.2169374
40. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. 10.1016/j.rse.2023.113514
41. Coupling Coordination Degree of AOD and Air Pollutants in Shandong Province from 2015 to 2020 P. Wang et al. 10.3390/atmos14040654
42. Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach M. Jiang et al. 10.1016/j.apr.2022.101394
43. Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran A. Mirzaei et al. 10.1007/s12145-022-00912-6
44. An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia M. Shaylor et al. 10.3390/rs14112664
45. Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 Q. He et al. 10.1016/j.jenvman.2023.118145
46. Assessment of aerosols over five cities of Angola based on MERRA–2 reanalysis data P. Campos et al. 10.1016/j.apr.2022.101569
47. Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020 Q. He et al. 10.1016/j.atmosres.2022.106481
48. Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
49. 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
50. A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. 10.1016/j.atmosenv.2022.119098
51. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
52. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
53. 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
54. Should There Be Industrial Agglomeration in Sustainable Cities?: A Perspective Based on Haze Pollution P. Dai & Y. Lin 10.3390/su13126609
55. Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China W. Ji et al. 10.1016/j.atmosres.2023.107106
56. Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
57. 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
58. High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
59. 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
60. Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. 10.1016/j.atmosenv.2023.120130
61. Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020 L. Yang et al. 10.3389/fpubh.2022.810098
62. Efforts in reducing air pollution exposure risk in China: State versus individuals B. Zou et al. 10.1016/j.envint.2020.105504
63. An Improved Aerosol Optical Depth Retrieval Algorithm for Multiangle Directional Polarimetric Camera (DPC) B. Ge et al. 10.3390/rs14164045
64. An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM2.5 Mapping N. Liu et al. 10.1109/TGRS.2023.3324393
65. An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs M. Bagherinia et al. 10.1016/j.apr.2023.102023
66. Assessment of spatiotemporal changes of ecological environment quality of the Yangtze River Delta urban agglomeration in China based on MRSEI Z. Shi et al. 10.3389/fevo.2022.1013859
67. Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks L. Zhang et al. 10.3390/rs13193834
68. 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
69. A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data N. Liu et al. 10.1016/j.atmosenv.2024.120440
70. 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