120 citations as recorded by crossref.

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4. The impact of urban landscape patterns on land surface temperature at the street block level: Evidence from 38 big Chinese cities A. Zhang et al. https://doi.org/10.1016/j.eiar.2024.107673
5. Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale B. Guo et al. https://doi.org/10.1016/j.atmosenv.2023.120159
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7. Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. https://doi.org/10.1016/j.chemosphere.2023.138742
8. PM2.5 Sink-Source Dichotomy in Urban Clusters: Land Cover Efficiency Gradients and Socio-Meteorological Interactions J. Yao & Y. Zhao https://doi.org/10.3390/atmos16101122
9. Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. https://doi.org/10.5194/essd-14-3197-2022
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11. Health Risk Assessment of Diurnal Fine Particulate Matter Exposure in Selected Urban Microenvironments of Benin City, Nigeria A. Eghomwanre & A. Ovenseri https://doi.org/10.1007/s41810-026-00423-7
12. Global spatiotemporal completion of daily high-resolution TCCO from TROPOMI over land using a swath-based local ensemble learning method Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2022.10.012
13. Suitability Assessment of Remotely Sensed Urban Air Quality Data Z. Zhang et al. https://doi.org/10.3390/rs17111848
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17. Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. https://doi.org/10.1021/acs.est.4c02701
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20. Source-explicit estimation of brown carbon in the polluted atmosphere over the North China Plain: implications for distribution, absorption, and the direct radiative effect J. Zhou et al. https://doi.org/10.5194/acp-25-7563-2025
21. Improving vegetation photosynthesis model (VPM) by incorporating CO2 and atmospheric aerosols: A case study using urban and rural flux data from Shenzhen, China G. Dong et al. https://doi.org/10.1016/j.pce.2026.104430
22. Hourly 2-km Himawari-8 aerosol fine-mode fraction retrieval improves PM2.5 mapping in the Beijing–Tianjin–Hebei region J. Jin et al. https://doi.org/10.1016/j.apr.2026.103180
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24. Estimation of regional PM2.5 concentration in China based on fine-mode aerosol optical thickness (AODf) and study of influencing factors C. Wan et al. https://doi.org/10.1016/j.atmosenv.2025.121026
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26. Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism X. Yan et al. https://doi.org/10.1016/j.envpol.2023.121509
27. Spatiotemporal patterns and quantitative analysis of influencing factors of PM2.5 and O3 pollution in the North China Plain M. Ma et al. https://doi.org/10.1016/j.apr.2023.101950
28. Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods H. Wang et al. https://doi.org/10.3390/atmos16060655
29. Integrating LGBM machine learning methods with SHAP model to explain the impact of different environmental factors on precipitation in China Y. Liu et al. https://doi.org/10.1016/j.jastp.2025.106715
30. Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies W. Liu et al. https://doi.org/10.1021/acs.est.4c03088
31. The complex role of air pollution on the association between greenness and respiratory mortality: Insight from a large cohort, 2009–2020 W. Wu et al. https://doi.org/10.1016/j.scitotenv.2023.165588
32. Mixture effects of multiple environmental factors on chronic obstructive pulmonary disease risk Trajectories: Evidence from Pearl River cohort in China X. Wu et al. https://doi.org/10.1016/j.envint.2026.110287
33. Long-term exposure to particulate matter and COPD mortality: Insights from causal inference methods based on a large population cohort in southern China Y. Wang et al. https://doi.org/10.1016/j.scitotenv.2022.160808
34. Dominant Role of Meteorology and Aerosols in Regulating the Seasonal Variation of Urban Thermal Environment in Beijing S. Zhang et al. https://doi.org/10.3390/rs17233921
35. Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. https://doi.org/10.1016/j.apr.2022.101579
36. Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. https://doi.org/10.1109/TGRS.2022.3213797
37. Spatiotemporal variation of LAI in different vegetation types and its response to climate change in China from 2001 to 2020 Y. Ma et al. https://doi.org/10.1016/j.ecolind.2023.111101
38. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. https://doi.org/10.1038/s41597-023-02696-w
39. Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning M. Liu et al. https://doi.org/10.3390/su16062475
40. LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics K. Bai et al. https://doi.org/10.5194/essd-16-2425-2024
41. SCAGAT: A scene-aware ensemble graph attention network for global PM2.5 pollution mapping via land–atmosphere interactions K. Bai et al. https://doi.org/10.1016/j.isprsjprs.2025.04.019
42. Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. https://doi.org/10.1016/j.atmosenv.2023.119642
43. Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China C. Zuo et al. https://doi.org/10.1016/j.atmosenv.2023.119795
44. Spatiotemporal Evolution in the Thermal Environment and Impact Analysis of Drivers in the Beijing–Tianjin–Hebei Urban Agglomeration of China from 2000 to 2020 H. Liu et al. https://doi.org/10.3390/rs16142601
45. A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions X. Zhang et al. https://doi.org/10.1016/j.envpol.2026.128233
46. PM 2.5 estimation and its relationship with NO 2 and SO 2 in China from 2016 to 2020 H. Tan et al. https://doi.org/10.1080/17538947.2024.2398055
47. 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. https://doi.org/10.1016/j.jenvman.2023.118145
48. Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China H. Xu et al. https://doi.org/10.1016/j.atmosenv.2024.120721
49. Generating station-like downward shortwave radiation data by using sky condition-guided model based on ERA5-Land data X. Li et al. https://doi.org/10.1016/j.energy.2024.132417
50. Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network T. Li et al. https://doi.org/10.1016/j.gsf.2022.101499
51. Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. https://doi.org/10.1016/j.atmosenv.2024.120347
52. Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. https://doi.org/10.5194/essd-16-3565-2024
53. Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020) W. Wu et al. https://doi.org/10.1016/j.scs.2023.105079
54. SSAT: Sensor-Satellite Auto-Correlation Transformer for Enhanced Aerosol Optical Depth Prediction M. Zhang et al. https://doi.org/10.1109/ACCESS.2025.3591479
55. Reconstruction of satellite remote sensing AOD considering spatial gap filling and downscaling X. Chen et al. https://doi.org/10.1016/j.atmosenv.2026.122106
56. 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. https://doi.org/10.1016/j.atmosres.2022.106481
57. Daily seamless reconstruction of aerosol optical depth product from a single satellite data source using the novel stream method X. Zhang et al. https://doi.org/10.1080/10095020.2025.2603729
58. Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.161552
59. Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China T. Guo et al. https://doi.org/10.1016/j.ecoenv.2024.116212
60. Potential causal links between long-term ambient particulate matter exposure and cardiovascular mortality: New evidence from a large community-based cohort in South China Y. Zhang et al. https://doi.org/10.1016/j.ecoenv.2023.114730
61. Mapping the seamless hourly surface visibility in China: a real-time retrieval framework using a machine-learning-based stacked ensemble model X. Zhang et al. https://doi.org/10.1038/s41612-024-00617-1
62. The dynamic impact of trade on environment H. Wang et al. https://doi.org/10.1111/joes.12624
63. Urbanization Amplified Asymmetrical Changes of Rainfall and Exacerbated Drought: Analysis Over Five Urban Agglomerations in the Yangtze River Basin, China S. Huang et al. https://doi.org/10.1029/2022EF003117
64. The establishment of blue-sky index: how to quantify the aerosol optical impacts on sky color S. Shen et al. https://doi.org/10.1088/1748-9326/ad9340
65. Downscaling aerosol optical depth by fusing satellite retrieval and model simulation using artificial intelligence technology L. Sun et al. https://doi.org/10.1016/j.atmosres.2025.108411
66. Drivers and Urban–Rural Disparities of PM2.5-Related Health Burden: Emerging Inequality Challenges under China’s Air-Quality Management Y. Li et al. https://doi.org/10.1021/acs.est.5c15628
67. Machine learning-based generation of high-resolution 3D full-coverage aerosol distribution data over China using multisource data W. Li et al. https://doi.org/10.1016/j.rse.2025.114772
68. Intelligent vehicle environment perception algorithm based on bubble sort data processing and data fusion Y. Long https://doi.org/10.1016/j.array.2025.100574
69. Predicting distribution characteristics of PM2.5 concentrations across China combining land use regression model and spatiotemporally weighted stacking machine learning algorithms W. Wang et al. https://doi.org/10.1016/j.jece.2026.121780
70. Research on high-resolution PM2.5 concentration estimation methods based on transfer learning M. Xing et al. https://doi.org/10.1016/j.asr.2026.01.052
71. Quantitative estimation of the spatiotemporal responses of PM2.5 and O3 synergistic pollution to precursors in the Chengdu-Chongqing City Group of China M. Ma et al. https://doi.org/10.1007/s11869-025-01863-9
72. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. https://doi.org/10.1080/15481603.2022.2060596
73. High-resolution PM2.5 retrieval using Gaofen-1 WFV camera data C. Hou et al. https://doi.org/10.1016/j.atmosenv.2025.121315
74. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.001
75. A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique X. Yu et al. https://doi.org/10.1016/j.atmosenv.2023.120216
76. Deriving PM2.5 from satellite observations with spatiotemporally weighted tree-based algorithms: enhancing modeling accuracy and interpretability T. Li et al. https://doi.org/10.1038/s41612-024-00692-4
77. Evaluating urban and nonurban PM 2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset B. Liu et al. https://doi.org/10.1080/17538947.2024.2310734
78. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. https://doi.org/10.1016/j.earscirev.2023.104461
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81. A Spatiotemporal Interpolation Graph Convolutional Network for Estimating PM₂.₅ Concentrations Based on Urban Functional Zones X. Chen et al. https://doi.org/10.1109/TGRS.2022.3231968
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84. MUSTI: Multi-scale spatiotemporal tensor imputation for global leaf area index time series reconstruction X. Yu et al. https://doi.org/10.1016/j.rse.2026.115559
85. Responses of surface and canopy heat islands to extreme temperatures: A synergistic analysis in the Beijing-Tianjin-Hebei urban agglomeration Y. Haiyi et al. https://doi.org/10.1016/j.uclim.2025.102712
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95. Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data T. Li et al. https://doi.org/10.1016/j.scitotenv.2023.164921
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102. Evaluating the causal links between long-term exposure to artificial light at night and lung cancer mortality based on the Pearl River Cohort Study with 0.5 million participants J. Jiang et al. https://doi.org/10.1016/j.jum.2025.12.002
103. Residential greenness and chronic obstructive pulmonary disease in a large cohort in southern China: Potential causal links, risk trajectories, and mediation pathways W. Wu et al. https://doi.org/10.1016/j.jare.2024.05.025
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105. Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning L. Ming et al. https://doi.org/10.1016/j.ecoinf.2024.102887
106. Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning B. Liu et al. https://doi.org/10.1016/j.apr.2024.102354
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108. Impact of aerosols on the physical parameters of cloud and precipitation processes in the eastern slope region of the Qinghai-Xizang Plateau S. Wang et al. https://doi.org/10.1016/j.rcar.2025.09.005
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118. Multimodal Sentiment Analysis of Online Product Marketing Information Based on Artificial Intelligence Neural Networks and Text Mining A. Huang et al. https://doi.org/10.1109/TEM.2025.3589199
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