Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-907-2022
https://doi.org/10.5194/essd-14-907-2022
Data description paper
 | 
24 Feb 2022
Data description paper |  | 24 Feb 2022

LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion

Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han

Viewed

Total article views: 3,769 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,611 1,095 63 3,769 265 52 75
  • HTML: 2,611
  • PDF: 1,095
  • XML: 63
  • Total: 3,769
  • Supplement: 265
  • BibTeX: 52
  • EndNote: 75
Views and downloads (calculated since 18 Nov 2021)
Cumulative views and downloads (calculated since 18 Nov 2021)

Viewed (geographical distribution)

Total article views: 3,769 (including HTML, PDF, and XML) Thereof 3,588 with geography defined and 181 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 09 Dec 2023
Download
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.
Altmetrics
Final-revised paper
Preprint