101 citations as recorded by crossref.

1. Reconstruction of Human-Induced Forest Loss in China during 1900–2000 Y. Zhang et al. 10.3390/rs15153831
2. Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data K. Peng et al. 10.1016/j.atmosres.2023.106925
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4. A Hybrid Approach to Estimate Spatially and Temporally Resolved Pm2.5 Distributions from Multi-Sourced Aod Data Q. Pu & Y. Eun-Hye 10.2139/ssrn.4094009
5. Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine X. Yu et al. 10.3390/rs15164104
6. Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna K. Mo et al. 10.3390/foods13132028
7. Two-stage estimation of hourly diffuse solar radiation across China using end-to-end gradient boosting with sequentially boosted features L. Chen et al. 10.1016/j.rse.2024.114445
8. High‐Resolution Mapping of Regional NMVOCs Using the Fast Space‐Time Light Gradient Boosting Machine (LightGBM) B. Lu et al. 10.1029/2023JD039591
9. First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning L. Gao et al. 10.1088/1748-9326/ad2022
10. A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data Q. Pu & E. Yoo 10.1016/j.envpol.2022.120419
11. Vertical Distributions of Aerosol and Cloud Microphysical Properties and the Aerosol Impact on a Continental Cumulus Cloud Based on Aircraft Measurements From the Loess Plateau of China Z. Cai et al. 10.3389/fenvs.2021.808861
12. Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models S. Park et al. 10.3390/su132413782
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16. Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data Z. Long et al. 10.3390/rs15112769
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18. SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting W. Chu et al. 10.1016/j.jag.2024.103972
19. Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019 R. Liu et al. 10.1021/acs.est.3c05424
20. Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach C. Chen et al. 10.1016/j.envpol.2021.118159
21. Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data J. Chen et al. 10.1016/j.jag.2022.102955
22. Human health risk identification of petrochemical sites based on extreme gradient boosting M. Wang et al. 10.1016/j.ecoenv.2022.113332
23. 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
24. PM 2.5 estimation and its relationship with NO 2 and SO 2 in China from 2016 to 2020 H. Tan et al. 10.1080/17538947.2024.2398055
25. Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China L. Lin et al. 10.3390/rs14205239
26. Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations Z. Niu et al. 10.3390/rs13183730
27. Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak C. Tang et al. 10.1016/j.scitotenv.2023.164456
28. Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
29. River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China H. Chen et al. 10.1016/j.scitotenv.2023.163571
30. Pilot Selection in the Era of Virtual Reality: Algorithms for Accurate and Interpretable Machine Learning Models L. Ke et al. 10.3390/aerospace10050394
31. Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India S. Wang et al. 10.1016/j.atmosenv.2024.120834
32. Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest L. Zhang et al. 10.3390/atmos15030384
33. Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection Y. Ding et al. 10.1016/j.rse.2023.113901
34. Performance Evaluation of Modified IRI2016 and Its Application to the 24 hr Ahead Forecast foF2 Mapping Over China Q. Song et al. 10.1029/2022JA030873
35. Joint features random forest (JFRF) model for mapping hourly surface PM2.5 over China L. Dong et al. 10.1016/j.atmosenv.2022.118969
36. Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
37. Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree W. He et al. 10.1016/j.chemosphere.2022.134003
38. Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms B. Chen et al. 10.1016/j.scitotenv.2021.150338
39. Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. 10.5194/essd-14-3197-2022
40. High-resolution mapping of regional VOCs using the enhanced space-time extreme gradient boosting machine (XGBoost) in Shanghai B. Lu et al. 10.1016/j.scitotenv.2023.167054
41. Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach N. Aman et al. 10.1007/s11356-024-34548-4
42. The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite L. Tian et al. 10.3390/rs15051459
43. Aerosol-induced direct radiative forcing effects on terrestrial ecosystem carbon fluxes over China W. Xue et al. 10.1016/j.envres.2021.111464
44. High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China Z. Song et al. 10.1016/j.atmosres.2022.106199
45. Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
46. Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
47. Underappreciated roles of soil nitrogen oxide emissions on global acute health burden S. Liu et al. 10.1016/j.envint.2024.109087
48. Environmental regulation and synergistic effects of PM2.5 control in China X. Li et al. 10.1016/j.jclepro.2022.130438
49. MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
50. Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning C. Fan et al. 10.3389/fenvs.2024.1409072
51. Predicting spatiotemporal variations of PM2.5 concentrations during spring festival for county-level cities in China using VIIRS-DNB data N. Erkin et al. 10.1016/j.atmosenv.2022.119484
52. The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN) Z. Wang et al. 10.1016/j.isprsjprs.2022.05.011
53. How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning? Z. Tian et al. 10.3390/rs15153780
54. Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method C. Wu et al. 10.1016/j.envres.2023.116866
55. Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk S. Araki et al. 10.1016/j.envpol.2024.124650
56. Air pollutant concentration prediction based on a new hybrid model, feature selection, and secondary decomposition W. Wang et al. 10.1007/s11869-023-01388-z
57. Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR T. Xu et al. 10.3389/feart.2022.1037719
58. Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations Z. Jiao & X. Fan 10.3390/rs16132406
59. Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China W. Chu et al. 10.3390/rs14184432
60. Mapping PM2.5 concentration from the top-of-atmosphere reflectance of Himawari-8 via an ensemble stacking model X. Chen et al. 10.1016/j.atmosenv.2024.120560
61. Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method S. Wang et al. 10.5194/gmd-17-3617-2024
62. Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province T. Li et al. 10.1016/j.envpol.2023.122612
63. Full-coverage estimation of PM2.5 in the Beijing-Tianjin-Hebei region by using a two-stage model Q. Zeng et al. 10.1016/j.atmosenv.2023.119956
64. The joint impact of PM2.5 constituents on the risk of cerebrovascular diseases hospitalization: A large community-based cohort study S. Chen et al. 10.1016/j.envres.2024.119644
65. Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China Z. Sun et al. 10.3390/rs13183752
66. Understanding urban bus travel time: Statistical analysis and a deep learning prediction Y. Liu et al. 10.1142/S0217979223500340
67. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
68. Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
69. An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data B. Chen et al. 10.1016/j.atmosenv.2021.118827
70. Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate S. Chen et al. 10.1016/j.rse.2022.113224
71. Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model Z. Song et al. 10.5194/amt-14-5333-2021
72. The influence of socioeconomic and environmental determinants on acute myocardial infarction (AMI) mortality from the spatial epidemiological perspective Y. Wang et al. 10.1007/s11356-022-19825-4
73. A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
74. Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM Y. Chen et al. 10.3390/app14020866
75. Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A B. Chen et al. 10.1029/2021JD036393
76. Impacts of aerosol direct radiative forcing on terrestrial ecosystem respiration in China from 2001 to 2014 T. Lu et al. 10.1016/j.atmosres.2021.105713
77. Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models T. Zhu et al. 10.1016/j.scitotenv.2022.159348
78. Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing F. Bao et al. 10.1016/j.rse.2024.114275
79. 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
80. Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. 10.1109/TGRS.2021.3129853
81. Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite R. Zhang et al. 10.3390/rs16213944
82. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact J. Wei et al. 10.1038/s41467-023-43862-3
83. 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. 10.1016/j.atmosenv.2024.120347
84. Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region N. Ding et al. 10.1016/j.atmosenv.2024.120483
85. Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data Q. Guo et al. 10.1016/j.chemosphere.2023.139886
86. Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. 10.5194/acp-23-3311-2023
87. Full Coverage Hourly PM2.5 Concentrations’ Estimation Using Himawari-8 and MERRA-2 AODs in China Z. Liu et al. 10.3390/ijerph20021490
88. Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM2.5 seasonal prediction J. Ma et al. 10.1016/j.scitotenv.2023.166989
89. Assessing the temporal-spatial dynamic reduction in ecosystem services caused by air pollution: A near-real-time data perspective Q. Yang et al. 10.1016/j.resconrec.2022.106205
90. Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model C. Hsu et al. 10.1038/s41370-023-00630-1
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93. Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model Y. Feng et al. 10.3390/rs14112714
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97. Modeling the Effects of Drivers on PM2.5 in the Yangtze River Delta with Geographically Weighted Random Forest Z. Su et al. 10.3390/rs15153826
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100. Forzamiento radiativo directo debido a los aerosoles atmosféricos en Perú utilizando técnicas de sensoramiento remoto R. Angeles Vasquez et al. 10.47797/llamkasun.v3i1.86
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