【Data Sharing】National daily SO2 raster data from 2013 to 2020 (free access)

Air quality data is data that is often used in our daily research! Previously, we shared PM2.5, PM10 and 10km resolution SO2 raster data from the Zendo platform (you can check the previous article for details):

  • Daily PM2.5 raster data at 1km resolution across the country from 2000 to 2021
  • Monthly PM2.5 grid data at 1km resolution across the country from 2000 to 2021
  • Year-by-year grid data of PM2.5 at 1km resolution across the country from 2000 to 2021
  • Daily PM10 raster data at 1km resolution across the country from 2000 to 2021
  • Monthly PM10 raster data at 1km resolution across the country from 2000 to 2021
  • Year-by-year PM10 raster data at 1km resolution across the country from 2000 to 2021
  • Monthly SO2 raster data at 10km resolution across the country from 2013 to 2020
  • Year-by-year SO2 raster data at 10km resolution across the country from 2013 to 2020

We found that there is also daily nationwide SO2 raster data from 2013 to 2020 shared on the Zendo platform. The data format is GeoTIFF, the spatial resolution is 10km, the unit is µg/m3, and the coordinate system is GCS_WGS_1984 .

The data comes from the China High Air Pollutants (CHAP) data set researched and produced by the team of Dr. Wei Jing and Professor Li Zhanqing from the University of Maryland. SO2 data is one of the main indicators of the data set. The data set uses artificial intelligence technology, taking into account the spatiotemporal heterogeneity of air pollution, and is produced from big data (such as ground-based observations, satellite remote sensing products, atmospheric reanalysis and model simulation data, etc.) Surface SO2 data, in addition, the data is continuously updated, if necessary, please continue to pay attention!

The following is a detailed introduction of the data:

01Data  preview

Since it is daily PM10 raster data, there are 365 raster files in one year. For each raster file, name the raster file according to the "CHAP_SO2_D10K_date_V1.tif" format, for example: CHAP_SO2_D10K_20200101_V1.tif , which means It is the SO2 raster data of 10km resolution on January 1, 2020; CHAP_SO2_D10K_20201231_V1 .tif is the SO2 raster data of 10km resolution on December 31, 2020.

Let's take the nationwide SO2 data on January 1, 2020, June 1, 2020 and December 1, 2020 as an example to preview:

National SO2 on January 1, 2020

National SO2 on June 1, 2020

National SO2 on December 1, 2020

02 Data Details

Original data download site:

https://zenodo.org/record/7574171

Data processing instructions:

The spatial range of the original raster data downloaded from the official website is a rectangle that frames the whole country, and -999 is used to represent the vacancy value. We remove the vacancy value and only keep the data within the national boundary to obtain the nationwide SO2 raster data.

time frame :

2013-2020 (daily)

Space range:

Nationwide

Data Format:

GeoTIFF

Spatial resolution:

10km

Data unit:

and/m3

Data coordinates:

GCS_WGS_1984

Data reference:

Jing Wei, & Zhanqing Li. (2021). ChinaHighSO2: Big Data Seamless 10 km Ground-level SO2 Dataset for China [Data set]. In Atmospheric Chemistry and Physics (Version 1, Vol. 23, Number 2, pp. 1511–1532). Zenodo. https://doi.org/10.5281/zenodo.7574171

Related paper citations:

Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023

If you have data usage needs, please refer to it according to the requirements of the official platform. For more data details, you can check the official website to learn!

At the bottom of the article is our official account business card. We will regularly introduce various urban data and data visualization and analysis technologies. Regarding the daily SO2 raster data nationwide from 2013 to 2020, you are welcome to pay more attention to us to learn more!

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Origin blog.csdn.net/weixin_63042008/article/details/132114287