The China 2020 Natural Crustal Dust Emission Inventory

Overview >

The China 2020 Natural Crustal Dust Emission Inventory is developed by the team of Prof. Yinchang Feng and Prof. Xiaohui Bi from Nankai University. The inventory is based on the wind erosion equation model, coupled with multi-source information such as satellite remote sensing data, meteorological reanalysis data, and soil geology data. The methodology system has been successfully applied to urban scales such as Tianjin, Shijiazhuang and Chengdu, and regional scales such as Beijing-Tianjin-Hebei, which can capture the high spatial resolution emission characteristics of natural crustal dust in different regions, and the credibility and accuracy of the methodology have been verified under various perspectives such as model simulation and ground observation. The spatial region of this set of inventories is the whole area of China, with a temporal resolution of months and a spatial resolution of 1km, and contains emission inventories of both PM10 and PM2.5 pollutants.

If you have any questions, please contact Prof. Xiaohui Bi (bixh@nankai.edu.cn).

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  • The China 2020 Natural Crustal Dust Emission Inventory is restricted to non-commercial purposes. Any uses of this emission inventory by business organizations are regarded as commercial purpose which needs prior authorization.
  • Users are not permitted to distribute the emission data to any third-party.
  • User must ensure the data integrity and independence when using this emission inventory. Without explicit and prior authorization, no organization or individual is allowed to integrate any forms of the emission data into other emission products or models.
  • Papers, reports or products using this inventory should cite the related publications.

Citation >

Tingkun Li , Xiaohui Bi , Qili Dai , Baoshuang Liu, Yan Han , Haoyan You, Lu Wang, Jiaying Zhang, Yuan Cheng, Yufen Zhang, Jianhui Wu, Yingze Tian, Yinchang Feng, 2018. Improving spatial resolution of soil fugitive dust emission inventory using RS-GIS technology: An application case in Tianjin, China. Atmospheric Environment 191, 46-54.

Lilai Song , Zhen Li , Jinqiu Zhang , Hu Li , Chenchu Wang , Xiaohui Bi , Qili Dai , Yinchang Feng , An hourly and localized optimization method for soil fugitive dust emission inventory based on machine learning, Journal of Environmental Sciences (2024), doi: https://doi.org/10.1016/j.jes.2024.12.016.