81 citations as recorded by crossref.

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3. Variability, predictability, and uncertainty in global aerosols inferred from gap-filled satellite observations and an econometric modeling approach X. Li et al. 10.1016/j.rse.2021.112501
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10. A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula A. Alban et al. 10.1007/s41748-024-00398-w
11. Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China M. Ali et al. 10.1016/j.atmosenv.2022.119297
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13. Responses of vegetation photosynthetic processes to aerosol-induced direct radiative effect in China W. Xue et al. 10.1016/j.atmosenv.2024.120557
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22. Are House Prices Affected by PM2.5 Pollution? Evidence from Beijing, China W. Xue et al. 10.3390/ijerph19148461
23. Simulating and Evaluating Global Aerosol Distributions With the Online Aerosol‐Coupled CAS‐FGOALS Model H. Wang et al. 10.1029/2019JD032097
24. Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018 E. Kassianov et al. 10.1038/s41597-021-00866-2
25. 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
26. How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies A. Sayer 10.1029/2020EA001290
27. Robust evidence for reversal of the trend in aerosol effective climate forcing J. Quaas et al. 10.5194/acp-22-12221-2022
28. Analyzing the Characteristics of Cloud Condensation Nuclei (CCN) in Hebei, China Using Multi-Year Observation and Reanalysis Data H. Wang et al. 10.3390/atmos13030468
29. Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China W. Xue et al. 10.1016/j.scitotenv.2019.134577
30. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region W. Xue et al. 10.1016/j.jclepro.2020.123742
31. Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations R. Fan et al. 10.1016/j.scitotenv.2022.159117
32. Influence of Saharan Dust on the Large‐Scale Meteorological Environment for Development of Tropical Cyclone Over North Atlantic Ocean Basin Y. Sun & C. Zhao 10.1029/2020JD033454
33. MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis J. Wei et al. 10.1016/j.atmosenv.2020.117768
34. Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
35. Enhanced Atmospheric Pollutants Strengthened Winter Convective Mixing and Phytoplankton Blooms in the Northern Arabian Sea B. Sridevi & V. Sarma 10.1029/2021JG006527
36. Satellite-based analysis of top of atmosphere shortwave radiative forcing trend induced by biomass burning aerosols over South-Eastern Atlantic C. Jouan & G. Myhre 10.1038/s41612-024-00631-3
37. 15-year variability of desert dust optical depth on global and regional scales S. Logothetis et al. 10.5194/acp-21-16499-2021
38. Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. 10.1029/2022JD037417
39. Two decades of fire activity over the PEEX domain: a look from space, with contribution from models and ground-based measurements L. Sogacheva et al. 10.1080/20964471.2024.2316730
40. Visibility as a proxy for air quality in East Africa A. Singh et al. 10.1088/1748-9326/ab8b12
41. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model U. Im et al. 10.5194/acp-21-10413-2021
42. A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
43. ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set A. Gkikas et al. 10.5194/amt-14-309-2021
44. An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors J. Gu et al. 10.3390/rs14071617
45. Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system S. Garrigues et al. 10.5194/acp-22-14657-2022
46. Aerosol optical depth regime over megacities of the world K. Papachristopoulou et al. 10.5194/acp-22-15703-2022
47. Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations K. Shukla et al. 10.1016/j.jastp.2024.106220
48. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean Z. Cao et al. 10.3390/toxics11100813
49. New seasonal pattern of pollution emerges from changing North American wildfires R. Buchholz et al. 10.1038/s41467-022-29623-8
50. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
51. Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability Z. Wang et al. 10.1016/j.isprsjprs.2022.03.002
52. 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
53. Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission J. Ke et al. 10.3390/atmos13091384
54. Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements Y. Tong et al. 10.3390/rs12142330
55. Solar UV radiation measurements in Marambio, Antarctica, during years 2017–2019 M. Aun et al. 10.5194/acp-20-6037-2020
56. 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
57. Evaluation of MODIS, MISR, and VIIRS daily level-3 aerosol optical depth products over land Q. Chen et al. 10.1016/j.atmosres.2021.105810
58. Can the aerosol pollution extreme events be revealed by global reanalysis products? J. Li et al. 10.1016/j.scitotenv.2024.171424
59. Spatiotemporal variations and relationships of aerosol-radiation-ecosystem productivity over China during 2001–2014 W. Xue et al. 10.1016/j.scitotenv.2020.140324
60. 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
61. Air pollution trends measured from Terra: CO and AOD over industrial, fire-prone, and background regions R. Buchholz et al. 10.1016/j.rse.2020.112275
62. Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products J. Wei et al. 10.1109/TGRS.2021.3050999
63. Denoising algorithm of LJ1-01 nightlight data with hybrid Chi-square distribution P. Yang et al. 10.1007/s12517-023-11663-z
64. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
65. Enhanced Aerosol Estimations From Suomi-NPP VIIRS Images Over Heterogeneous Surfaces J. Wei et al. 10.1109/TGRS.2019.2927432
66. Review of satellite-driven statistical modelsPM2.5concentration estimation with comprehensive information X. Xu et al. 10.1016/j.atmosenv.2021.118302
67. Anthropogenic and meteorological drivers of 1980–2016 trend in aerosol optical and radiative properties over the Yangtze River Basin L. He et al. 10.1016/j.atmosenv.2019.117188
68. Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products J. Wei et al. 10.1016/j.scitotenv.2019.07.326
69. Trends in AOD, Clouds, and Cloud Radiative Effects in Satellite Data and CMIP5 and CMIP6 Model Simulations Over Aerosol Source Regions R. Cherian & J. Quaas 10.1029/2020GL087132
70. Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach J. Li et al. 10.1029/2019JD031884
71. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China J. Wei et al. 10.1016/j.rse.2021.112775
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73. Long-term trends in Aerosol Optical Depth obtained across the globe using multi-satellite measurements G. Gupta et al. 10.1016/j.atmosenv.2022.118953
74. Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study J. Wei et al. 10.1016/S2542-5196(23)00235-8
75. Distinct aerosol effects on cloud-to-ground lightning in the plateau and basin regions of Sichuan, Southwest China P. Zhao et al. 10.5194/acp-20-13379-2020
76. Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring C. Chen et al. 10.5194/essd-12-3573-2020
77. Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China T. Zhang et al. 10.3390/rs12101684
78. Comprehensive Assessment and Analysis of the Current Global Aerosol Optical Depth Products L. Zhang et al. 10.3390/rs16081425
79. Analysis of the Winter AOD Trends over Iran from 2000 to 2020 and Associated Meteorological Effects R. Yousefi et al. 10.3390/rs15040905
80. Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants J. Gu et al. 10.1117/1.JRS.18.012006
81. 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