Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Institute of Eco-Chongming, 20 Cuiniao Rd., Chongming, Shanghai
202162, China
Ke Li
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Mingliang Ma
School of Surveying and Geo-Informatics, Shandong Jianzhu University,
Jinan 250101, China
Kaitao Li
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Zhengqiang Li
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering,
University of Central Florida, Orlando, FL 32816, USA
Zhuo Tan
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Di Han
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Viewed
Total article views: 5,767 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Nov 2021)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
4,013 | 1,641 | 113 | 5,767 | 340 | 107 | 130 |
- HTML: 4,013
- PDF: 1,641
- XML: 113
- Total: 5,767
- Supplement: 340
- BibTeX: 107
- EndNote: 130
Total article views: 4,284 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Feb 2022)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
3,336 | 859 | 89 | 4,284 | 226 | 97 | 112 |
- HTML: 3,336
- PDF: 859
- XML: 89
- Total: 4,284
- Supplement: 226
- BibTeX: 97
- EndNote: 112
Total article views: 1,483 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Nov 2021)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
677 | 782 | 24 | 1,483 | 114 | 10 | 18 |
- HTML: 677
- PDF: 782
- XML: 24
- Total: 1,483
- Supplement: 114
- BibTeX: 10
- EndNote: 18
Viewed (geographical distribution)
Total article views: 5,767 (including HTML, PDF, and XML)
Thereof 5,508 with geography defined
and 259 with unknown origin.
Total article views: 4,284 (including HTML, PDF, and XML)
Thereof 4,092 with geography defined
and 192 with unknown origin.
Total article views: 1,483 (including HTML, PDF, and XML)
Thereof 1,416 with geography defined
and 67 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
69 citations as recorded by crossref.
- Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method C. Huang et al. 10.1016/j.solener.2023.112038
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- 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. 10.1016/j.eiar.2024.107673
- Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale B. Guo et al. 10.1016/j.atmosenv.2023.120159
- A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
- Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
- Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
- Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. 10.5194/essd-14-3197-2022
- Data Integration for ML-CNPM₂.₅: A Public Sample Dataset Based on Machine Learning Models and Remote Sensing Technology Applied for Estimating Ground-Level PM₂.₅ in China Y. Fan et al. 10.1109/TGRS.2024.3436006
- Global spatiotemporal completion of daily high-resolution TCCO from TROPOMI over land using a swath-based local ensemble learning method Y. Wang et al. 10.1016/j.isprsjprs.2022.10.012
- Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. 10.1016/j.isprsjprs.2022.09.001
- A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique X. Yu et al. 10.1016/j.atmosenv.2023.120216
- Deriving PM2.5 from satellite observations with spatiotemporally weighted tree-based algorithms: enhancing modeling accuracy and interpretability T. Li et al. 10.1038/s41612-024-00692-4
- Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
- 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. 10.1080/17538947.2024.2310734
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. 10.1021/acs.est.4c02701
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
- PM2.5 estimated directly from satellite data and from fused data produced by an interpretable multi-model stacking ensemble method X. Ma et al. 10.1016/j.apr.2024.102259
- Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban Z. Liu et al. 10.1016/j.envpol.2024.124899
- A Spatiotemporal Interpolation Graph Convolutional Network for Estimating PM₂.₅ Concentrations Based on Urban Functional Zones X. Chen et al. 10.1109/TGRS.2022.3231968
- Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework Q. Xiao et al. 10.5194/acp-22-13229-2022
- Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
- 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
- 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. 10.1016/j.envpol.2023.121509
- Spatiotemporal patterns and quantitative analysis of influencing factors of PM2.5 and O3 pollution in the North China Plain M. Ma et al. 10.1016/j.apr.2023.101950
- 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
- Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies W. Liu et al. 10.1021/acs.est.4c03088
- 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. 10.1016/j.scitotenv.2023.165588
- 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. 10.1016/j.scitotenv.2022.160808
- Estimating 1-km PM2.5 concentrations based on a novel spatiotemporal parallel network STMSPNet in the Beijing-Tianjin-Hebei region Q. Zeng et al. 10.1016/j.atmosenv.2024.120796
- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. 10.1117/1.JRS.18.012004
- The evolution of open biomass burning during summer crop harvest in the North China Plain S. Li et al. 10.1177/03091333231187817
- Impacts of urban expansion on meteorology and air quality in North China Plain during wintertime: A case study Q. Jiang et al. 10.1016/j.uclim.2023.101696
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- 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
- 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. 10.1016/j.scitotenv.2023.164921
- Urban residential greenness and cancer mortality: Evaluating the causal mediation role of air pollution in a large cohort Z. Li et al. 10.1016/j.envpol.2024.124704
- Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. 10.1016/j.apr.2022.101579
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations H. Bai et al. 10.1117/1.JRS.18.012003
- Spatiotemporal variation of LAI in different vegetation types and its response to climate change in China from 2001 to 2020 Y. Ma et al. 10.1016/j.ecolind.2023.111101
- Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. 10.1038/s41597-023-02696-w
- 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. 10.3390/su16062475
- 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. 10.5194/essd-16-2425-2024
- 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. 10.1016/j.jare.2024.05.025
- Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. 10.1016/j.atmosenv.2023.119642
- Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning B. Liu et al. 10.1016/j.apr.2024.102354
- Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China C. Zuo et al. 10.1016/j.atmosenv.2023.119795
- 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. 10.3390/rs16142601
- 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
- 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. 10.1016/j.jenvman.2023.118145
- Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China H. Xu et al. 10.1016/j.atmosenv.2024.120721
- Generating station-like downward shortwave radiation data by using sky condition-guided model based on ERA5-Land data X. Li et al. 10.1016/j.energy.2024.132417
- Potential causal links between long-term ambient particulate matter exposure and cerebrovascular mortality: Insights from a large cohort in southern China S. Chen et al. 10.1016/j.envpol.2023.121336
- Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network T. Li et al. 10.1016/j.gsf.2022.101499
- 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
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- 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. 10.1016/j.scs.2023.105079
- Potential causal links of long‐term air pollution with lung cancer incidence: From the perspectives of mortality and hospital admission in a large cohort study in southern China T. Guo et al. 10.1002/ijc.34699
- Advancing ecological quality assessment in China: Introducing the ARSEI and identifying key regional drivers Q. Tang et al. 10.1016/j.ecolind.2024.112109
- 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. 10.1016/j.atmosres.2022.106481
- Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China H. Bai et al. 10.1016/j.envpol.2023.121505
- Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. 10.1016/j.scitotenv.2023.161552
- Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China T. Guo et al. 10.1016/j.ecoenv.2024.116212
- 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. 10.1016/j.ecoenv.2023.114730
- 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. 10.1038/s41612-024-00617-1
- The dynamic impact of trade on environment H. Wang et al. 10.1111/joes.12624
- Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain Y. Li et al. 10.3390/app14125026
- Urbanization Amplified Asymmetrical Changes of Rainfall and Exacerbated Drought: Analysis Over Five Urban Agglomerations in the Yangtze River Basin, China S. Huang et al. 10.1029/2022EF003117
69 citations as recorded by crossref.
- Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method C. Huang et al. 10.1016/j.solener.2023.112038
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- 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. 10.1016/j.eiar.2024.107673
- Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale B. Guo et al. 10.1016/j.atmosenv.2023.120159
- A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
- Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
- Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
- Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China J. Zhong et al. 10.5194/essd-14-3197-2022
- Data Integration for ML-CNPM₂.₅: A Public Sample Dataset Based on Machine Learning Models and Remote Sensing Technology Applied for Estimating Ground-Level PM₂.₅ in China Y. Fan et al. 10.1109/TGRS.2024.3436006
- Global spatiotemporal completion of daily high-resolution TCCO from TROPOMI over land using a swath-based local ensemble learning method Y. Wang et al. 10.1016/j.isprsjprs.2022.10.012
- Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. 10.1016/j.isprsjprs.2022.09.001
- A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique X. Yu et al. 10.1016/j.atmosenv.2023.120216
- Deriving PM2.5 from satellite observations with spatiotemporally weighted tree-based algorithms: enhancing modeling accuracy and interpretability T. Li et al. 10.1038/s41612-024-00692-4
- Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
- 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. 10.1080/17538947.2024.2310734
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. 10.1021/acs.est.4c02701
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
- PM2.5 estimated directly from satellite data and from fused data produced by an interpretable multi-model stacking ensemble method X. Ma et al. 10.1016/j.apr.2024.102259
- Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban Z. Liu et al. 10.1016/j.envpol.2024.124899
- A Spatiotemporal Interpolation Graph Convolutional Network for Estimating PM₂.₅ Concentrations Based on Urban Functional Zones X. Chen et al. 10.1109/TGRS.2022.3231968
- Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework Q. Xiao et al. 10.5194/acp-22-13229-2022
- Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
- 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
- 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. 10.1016/j.envpol.2023.121509
- Spatiotemporal patterns and quantitative analysis of influencing factors of PM2.5 and O3 pollution in the North China Plain M. Ma et al. 10.1016/j.apr.2023.101950
- 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
- Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies W. Liu et al. 10.1021/acs.est.4c03088
- 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. 10.1016/j.scitotenv.2023.165588
- 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. 10.1016/j.scitotenv.2022.160808
- Estimating 1-km PM2.5 concentrations based on a novel spatiotemporal parallel network STMSPNet in the Beijing-Tianjin-Hebei region Q. Zeng et al. 10.1016/j.atmosenv.2024.120796
- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. 10.1117/1.JRS.18.012004
- The evolution of open biomass burning during summer crop harvest in the North China Plain S. Li et al. 10.1177/03091333231187817
- Impacts of urban expansion on meteorology and air quality in North China Plain during wintertime: A case study Q. Jiang et al. 10.1016/j.uclim.2023.101696
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- 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
- 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. 10.1016/j.scitotenv.2023.164921
- Urban residential greenness and cancer mortality: Evaluating the causal mediation role of air pollution in a large cohort Z. Li et al. 10.1016/j.envpol.2024.124704
- Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. 10.1016/j.apr.2022.101579
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations H. Bai et al. 10.1117/1.JRS.18.012003
- Spatiotemporal variation of LAI in different vegetation types and its response to climate change in China from 2001 to 2020 Y. Ma et al. 10.1016/j.ecolind.2023.111101
- Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. 10.1038/s41597-023-02696-w
- 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. 10.3390/su16062475
- 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. 10.5194/essd-16-2425-2024
- 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. 10.1016/j.jare.2024.05.025
- Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. 10.1016/j.atmosenv.2023.119642
- Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning B. Liu et al. 10.1016/j.apr.2024.102354
- Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China C. Zuo et al. 10.1016/j.atmosenv.2023.119795
- 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. 10.3390/rs16142601
- 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
- 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. 10.1016/j.jenvman.2023.118145
- Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China H. Xu et al. 10.1016/j.atmosenv.2024.120721
- Generating station-like downward shortwave radiation data by using sky condition-guided model based on ERA5-Land data X. Li et al. 10.1016/j.energy.2024.132417
- Potential causal links between long-term ambient particulate matter exposure and cerebrovascular mortality: Insights from a large cohort in southern China S. Chen et al. 10.1016/j.envpol.2023.121336
- Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network T. Li et al. 10.1016/j.gsf.2022.101499
- 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
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- 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. 10.1016/j.scs.2023.105079
- Potential causal links of long‐term air pollution with lung cancer incidence: From the perspectives of mortality and hospital admission in a large cohort study in southern China T. Guo et al. 10.1002/ijc.34699
- Advancing ecological quality assessment in China: Introducing the ARSEI and identifying key regional drivers Q. Tang et al. 10.1016/j.ecolind.2024.112109
- 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. 10.1016/j.atmosres.2022.106481
- Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China H. Bai et al. 10.1016/j.envpol.2023.121505
- Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. 10.1016/j.scitotenv.2023.161552
- Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China T. Guo et al. 10.1016/j.ecoenv.2024.116212
- 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. 10.1016/j.ecoenv.2023.114730
- 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. 10.1038/s41612-024-00617-1
- The dynamic impact of trade on environment H. Wang et al. 10.1111/joes.12624
- Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain Y. Li et al. 10.3390/app14125026
- Urbanization Amplified Asymmetrical Changes of Rainfall and Exacerbated Drought: Analysis Over Five Urban Agglomerations in the Yangtze River Basin, China S. Huang et al. 10.1029/2022EF003117
Latest update: 09 Nov 2024
Short summary
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free...
Altmetrics
Final-revised paper
Preprint