Articles | Volume 21, issue 10
https://doi.org/10.5194/acp-21-7863-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-7863-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2021
Research article |
| 25 May 2021
| 25 May 2021
Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Rachel T. Pinker
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
Lin Sun
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
Wenhao Xue
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Maureen Cribb
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Viewed
Total article views: 5,533 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Jan 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,240 | 1,216 | 77 | 5,533 | 57 | 106 |
- HTML: 4,240
- PDF: 1,216
- XML: 77
- Total: 5,533
- BibTeX: 57
- EndNote: 106
Total article views: 4,658 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 May 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,686 | 907 | 65 | 4,658 | 55 | 101 |
- HTML: 3,686
- PDF: 907
- XML: 65
- Total: 4,658
- BibTeX: 55
- EndNote: 101
Total article views: 875 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Jan 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 554 | 309 | 12 | 875 | 116 | 2 | 5 |
- HTML: 554
- PDF: 309
- XML: 12
- Total: 875
- Supplement: 116
- BibTeX: 2
- EndNote: 5
Viewed (geographical distribution)
Total article views: 5,533 (including HTML, PDF, and XML)
Thereof 5,584 with geography defined
and -51 with unknown origin.
Total article views: 4,658 (including HTML, PDF, and XML)
Thereof 4,608 with geography defined
and 50 with unknown origin.
Total article views: 875 (including HTML, PDF, and XML)
Thereof 976 with geography defined
and -101 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
102 citations as recorded by crossref.
- Reconstruction of Human-Induced Forest Loss in China during 1900–2000 Y. Zhang et al. 10.3390/rs15153831
- 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
- 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
- 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
- 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
- Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna K. Mo et al. 10.3390/foods13132028
- 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
- High‐Resolution Mapping of Regional NMVOCs Using the Fast Space‐Time Light Gradient Boosting Machine (LightGBM) B. Lu et al. 10.1029/2023JD039591
- 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
- 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
- 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
- Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models S. Park et al. 10.3390/su132413782
- MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning J. Ma et al. 10.1016/j.scitotenv.2022.154363
- Temporal heterogeneity in the performance of machine learning models for PM2.5 concentration estimation P. Li et al. 10.1016/j.psep.2024.06.115
- Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
- Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data Z. Long et al. 10.3390/rs15112769
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting W. Chu et al. 10.1016/j.jag.2024.103972
- 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
- 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
- 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
- Human health risk identification of petrochemical sites based on extreme gradient boosting M. Wang et al. 10.1016/j.ecoenv.2022.113332
- 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
- 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
- Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China L. Lin et al. 10.3390/rs14205239
- Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations Z. Niu et al. 10.3390/rs13183730
- 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
- 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
- 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
- Pilot Selection in the Era of Virtual Reality: Algorithms for Accurate and Interpretable Machine Learning Models L. Ke et al. 10.3390/aerospace10050394
- 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
- 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
- Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection Y. Ding et al. 10.1016/j.rse.2023.113901
- 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
- Joint features random forest (JFRF) model for mapping hourly surface PM2.5 over China L. Dong et al. 10.1016/j.atmosenv.2022.118969
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- 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
- Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms B. Chen et al. 10.1016/j.scitotenv.2021.150338
- Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. 10.5194/essd-14-3197-2022
- 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
- Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China Y. Hang et al. 10.1016/j.scitotenv.2024.177883
- 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
- The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite L. Tian et al. 10.3390/rs15051459
- Aerosol-induced direct radiative forcing effects on terrestrial ecosystem carbon fluxes over China W. Xue et al. 10.1016/j.envres.2021.111464
- 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
- 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
- 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
- Underappreciated roles of soil nitrogen oxide emissions on global acute health burden S. Liu et al. 10.1016/j.envint.2024.109087
- Environmental regulation and synergistic effects of PM2.5 control in China X. Li et al. 10.1016/j.jclepro.2022.130438
- MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
- Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning C. Fan et al. 10.3389/fenvs.2024.1409072
- 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
- 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
- How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning? Z. Tian et al. 10.3390/rs15153780
- 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
- 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
- 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
- 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
- Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations Z. Jiao & X. Fan 10.3390/rs16132406
- 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
- 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
- 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
- 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
- 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
- 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
- Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China Z. Sun et al. 10.3390/rs13183752
- Understanding urban bus travel time: Statistical analysis and a deep learning prediction Y. Liu et al. 10.1142/S0217979223500340
- Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite R. Zhang et al. 10.3390/rs16213944
- 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
- 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
- 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
- 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
- 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
- Full Coverage Hourly PM2.5 Concentrations’ Estimation Using Himawari-8 and MERRA-2 AODs in China Z. Liu et al. 10.3390/ijerph20021490
- 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
- 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
- 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
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China Z. Song et al. 10.1016/j.envpol.2022.118826
- 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
- Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations W. Chu et al. 10.3390/rs15204973
- Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations Q. Chen et al. 10.1016/j.jenvman.2024.122107
- Combining Himawari-8 AOD and Deep Forest Model to Obtain City-Level Distribution of PM <sub>2.5</sub> in China Z. Song et al. 10.2139/ssrn.3937107
- 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
- Tracking hourly PM2.5 using geostationary satellite sensor images and multiscale spatiotemporal deep learning Z. Wang et al. 10.1016/j.jag.2024.104145
- 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
- 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
- Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China A. Hasnain et al. 10.1007/s10661-024-12644-9
100 citations as recorded by crossref.
- Reconstruction of Human-Induced Forest Loss in China during 1900–2000 Y. Zhang et al. 10.3390/rs15153831
- 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
- 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
- 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
- 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
- Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna K. Mo et al. 10.3390/foods13132028
- 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
- High‐Resolution Mapping of Regional NMVOCs Using the Fast Space‐Time Light Gradient Boosting Machine (LightGBM) B. Lu et al. 10.1029/2023JD039591
- 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
- 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
- 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
- Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models S. Park et al. 10.3390/su132413782
- MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning J. Ma et al. 10.1016/j.scitotenv.2022.154363
- Temporal heterogeneity in the performance of machine learning models for PM2.5 concentration estimation P. Li et al. 10.1016/j.psep.2024.06.115
- Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
- Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data Z. Long et al. 10.3390/rs15112769
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting W. Chu et al. 10.1016/j.jag.2024.103972
- 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
- 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
- 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
- Human health risk identification of petrochemical sites based on extreme gradient boosting M. Wang et al. 10.1016/j.ecoenv.2022.113332
- 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
- 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
- Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China L. Lin et al. 10.3390/rs14205239
- Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations Z. Niu et al. 10.3390/rs13183730
- 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
- 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
- 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
- Pilot Selection in the Era of Virtual Reality: Algorithms for Accurate and Interpretable Machine Learning Models L. Ke et al. 10.3390/aerospace10050394
- 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
- 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
- Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection Y. Ding et al. 10.1016/j.rse.2023.113901
- 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
- Joint features random forest (JFRF) model for mapping hourly surface PM2.5 over China L. Dong et al. 10.1016/j.atmosenv.2022.118969
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- 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
- Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms B. Chen et al. 10.1016/j.scitotenv.2021.150338
- Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. 10.5194/essd-14-3197-2022
- 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
- Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China Y. Hang et al. 10.1016/j.scitotenv.2024.177883
- 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
- The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite L. Tian et al. 10.3390/rs15051459
- Aerosol-induced direct radiative forcing effects on terrestrial ecosystem carbon fluxes over China W. Xue et al. 10.1016/j.envres.2021.111464
- 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
- 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
- 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
- Underappreciated roles of soil nitrogen oxide emissions on global acute health burden S. Liu et al. 10.1016/j.envint.2024.109087
- Environmental regulation and synergistic effects of PM2.5 control in China X. Li et al. 10.1016/j.jclepro.2022.130438
- MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
- Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning C. Fan et al. 10.3389/fenvs.2024.1409072
- 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
- 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
- How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning? Z. Tian et al. 10.3390/rs15153780
- 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
- 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
- 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
- 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
- Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations Z. Jiao & X. Fan 10.3390/rs16132406
- 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
- 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
- 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
- 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
- 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
- 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
- Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China Z. Sun et al. 10.3390/rs13183752
- Understanding urban bus travel time: Statistical analysis and a deep learning prediction Y. Liu et al. 10.1142/S0217979223500340
- Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite R. Zhang et al. 10.3390/rs16213944
- 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
- 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
- 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
- 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
- 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
- Full Coverage Hourly PM2.5 Concentrations’ Estimation Using Himawari-8 and MERRA-2 AODs in China Z. Liu et al. 10.3390/ijerph20021490
- 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
- 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
- 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
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China Z. Song et al. 10.1016/j.envpol.2022.118826
- 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
- Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations W. Chu et al. 10.3390/rs15204973
- Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations Q. Chen et al. 10.1016/j.jenvman.2024.122107
- Combining Himawari-8 AOD and Deep Forest Model to Obtain City-Level Distribution of PM <sub>2.5</sub> in China Z. Song et al. 10.2139/ssrn.3937107
- 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
- Tracking hourly PM2.5 using geostationary satellite sensor images and multiscale spatiotemporal deep learning Z. Wang et al. 10.1016/j.jag.2024.104145
- 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
2 citations as recorded by crossref.
- 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
- Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China A. Hasnain et al. 10.1007/s10661-024-12644-9
Latest update: 13 Dec 2024
Short summary
This study developed a space-time Light Gradient Boosting Machine (STLG) model to derive the high-temporal-resolution (1 h) and high-quality PM2.5 dataset in China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution from the Himawari-8 Advanced Himawari Imager aerosol products. Our model outperforms most previous related studies with a much lower computation burden in terms of speed and memory, making it most suitable for real-time air pollution monitoring in China.
This study developed a space-time Light Gradient Boosting Machine (STLG) model to derive the...
Altmetrics
Final-revised paper
Preprint