72 citations as recorded by crossref.

1. Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models . Doreswamy et al. 10.1016/j.procs.2020.04.221
2. Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean G. Tuna Tuygun et al. 10.1016/j.atmosenv.2021.118562
3. Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD S. Kim et al. 10.3390/app112210755
4. Generation of Hypothetical Radiances for Missing Green and Red Bands in Geostationary Environment Monitoring Spectrometer H. Ryu et al. 10.1109/JSTARS.2023.3280905
5. Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning Y. Cho et al. 10.1016/j.atmosres.2023.106787
6. Ambient PM_2.5 exposures could increase risk of tuberculosis recurrence K. Min et al. 10.1265/ehpm.23-00131
7. Prediction of hourly PM10 concentration through a hybrid deep learning-based method S. Nasabpour Molaei et al. 10.1007/s12145-023-01146-w
8. Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output S. Park et al. 10.3390/rs12213499
9. 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
10. Estimating daily PM2.5 concentrations using an extreme gradient boosting model based on VIIRS aerosol products over southeastern Europe S. Gündoğdu et al. 10.1007/s11869-022-01245-5
11. Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. 10.1002/gj.4780
12. Airborne Inlets and Instrumentation on Aircraft Platform for Air Quality Observation B. Seo et al. 10.5572/KOSAE.2019.35.6.815
13. Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals D. Stratoulias et al. 10.3390/rs16162932
14. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
15. A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation W. Choi et al. 10.3390/rs13040609
16. Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system U. Shin et al. 10.1016/j.atmosenv.2020.118141
17. Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia S. Park et al. 10.1016/j.envpol.2022.119425
18. Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China W. Guo et al. 10.1016/j.apr.2021.101154
19. 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
20. New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
21. Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting N. Yang et al. 10.1016/j.rse.2021.112828
22. Calibrating MERRA-2 PM2.5 concentrations with aerosol diagnostics: testing different machine learning approaches in the Eastern Mediterranean G. Tuna Tuygun et al. 10.1007/s11869-022-01250-8
23. Assessment of air quality in North Korea from satellite observations H. Chong et al. 10.1016/j.envint.2022.107708
24. A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
25. Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
26. Short Path Wind-Field Distance-Based Lagrangian Trajectory Model for Enhancing Atmospheric Dispersion Prediction Accuracy S. R’Bigui et al. 10.1109/ACCESS.2023.3320563
27. Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea H. Park et al. 10.1109/JSTARS.2021.3103754
28. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
29. A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning S. Zhang et al. 10.1016/j.scitotenv.2022.154278
30. Enhancing ozone nowcasting over East Asia using a data-to-data translation approach with observations from a geostationary environment monitoring spectrometer S. Oh et al. 10.1016/j.apr.2024.102054
31. Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study A. Wu et al. 10.1002/cpe.7035
32. Türkiye’de Terra ve Aqua MODIS Uydularından Elde Edilen Aerosol Optik Derinliğinin Zamansal ve Mekansal Değişimi S. KARSLIOĞLU et al. 10.21205/deufmd.2022247027
33. Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II H. Choi et al. 10.1016/j.envpol.2023.121169
34. Optical Properties of Boundary Layer Aerosols From High Spectral Resolution Lidar Measurements in a Polluted Urban Environment (Seoul, Korea) S. Park et al. 10.1029/2023JD038523
35. High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning Y. Yang et al. 10.3390/rs16132498
36. Identifying priority air pollution management areas during the burning season in Nan Province, Northern Thailand D. Kamthonkiat et al. 10.1007/s10668-020-00850-7
37. Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data Y. Sun et al. 10.3390/rs13214276
38. Ultrahigh-Resolution (250 m) Regional Surface PM2.5 Concentrations Derived First From MODIS Measurements J. Liu et al. 10.1109/TGRS.2021.3064191
39. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. 10.1109/TGRS.2021.3107542
40. Estimating ground-level particulate matter concentrations using satellite-based data: a review M. Shin et al. 10.1080/15481603.2019.1703288
41. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
42. Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea G. Nguyen et al. 10.3390/rs13204033
43. Estimation of ground-level dry PM2.5 concentrations at 3 km resolution over Beijing using Geostationary Ocean Colour Imager J. Wang & Z. Li 10.1080/2150704X.2020.1795298
44. SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality K. Stebel et al. 10.3390/rs13112219
45. Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning C. Lee et al. 10.3390/rs13112121
46. A new drought monitoring approach: Vector Projection Analysis (VPA) B. Son et al. 10.1016/j.rse.2020.112145
47. Satellite Remote Sensing for Estimating PM2.5 and Its Components Y. Li et al. 10.1007/s40726-020-00170-4
48. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. 10.5194/amt-12-4619-2019
49. Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution X. Cheng et al. 10.1016/j.atmosres.2019.04.002
50. Estimation of high-resolution PM2.5 concentrations based on gap-filling aerosol optical depth using gradient boosting model M. Han et al. 10.1007/s11869-021-01149-w
51. Ground-based retrievals of aerosol column absorption in the UV spectral region and their implications for GEMS measurements S. Go et al. 10.1016/j.rse.2020.111759
52. Calibration of CAMS PM2.5 data over Hungary: a machine learning approach A. Qor-el-aine et al. 10.1088/2515-7620/ad6239
53. Potential role of urban forest in removing PM2.5: A case study in Seoul by deep learning with satellite data A. Lee et al. 10.1016/j.uclim.2021.100795
54. Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea J. Yeom et al. 10.1088/1748-9326/ab9467
55. Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation K. Kim et al. 10.1088/2515-7620/accfe9
56. Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data S. Jeong et al. 10.3390/s21030941
57. 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
58. Comparison of Aerosol Properties in the Korean Peninsula Between AERONET Version 2 and 3 Data Set J. Lee et al. 10.1007/s13143-020-00221-2
59. Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066
60. Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China R. Li et al. 10.3390/rs12040736
61. Two-step carbon storage estimation in urban human settlements using airborne LiDAR and Sentinel-2 data based on machine learning Y. Lee et al. 10.1016/j.ufug.2024.128239
62. Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea Y. Kang et al. 10.3390/app10228213
63. Estimation of particulate matter concentrations in Türkiye using a random forest model based on satellite AOD retrievals G. Tuna Tuygun & T. Elbir 10.1007/s00477-023-02459-4
64. PM2.5 Inversion Based on XGBoost And LightGBM Integrated Models Y. Ren et al. 10.1051/e3sconf/202452002023
65. Influence of cloud, fog, and high relative humidity during pollution transport events in South Korea: Aerosol properties and PM2.5 variability T. Eck et al. 10.1016/j.atmosenv.2020.117530
66. Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. 10.1016/j.scitotenv.2023.166504
67. Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis S. Sohrab et al. 10.1186/s12302-024-00972-z
68. Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band G. Choo et al. 10.1016/j.atmosenv.2024.120861
69. Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria T. Etchie et al. 10.1016/j.scitotenv.2021.145187
70. Geostationary Satellite-Derived Ground-Level PM ₁₀ and PM _2.5 Concentrations Using Real-Time Machine Learning in Northeast Asia S. Park et al. 10.2139/ssrn.3983971
71. Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6  ×  6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. 10.5194/amt-15-1075-2022
72. Review of Satellite-driven Statistical Models PM2.5 Concentration Estimation with Comprehensive Information X. Xu et al. 10.1016/j.atmosenv.2021.118302