Air quality data is data that is often used in our daily research! Before we shared the 1km resolution PM2.5 and PM10 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
We found that the nationwide monthly SO2 raster data from 2013 to 2020 is also 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. We downloaded the data. In addition, the nationwide data is very large and inconvenient to use. We divided the national data into the data of 34 provinces!
The data comes from the China High Air Pollutants (CHAP) dataset produced by the team of Dr. Wei Jing and Professor Li Zhanqing from the University of Maryland in the United States. SO2 data is one of the main indicators of the dataset. 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,
The following is a detailed introduction of the data:
01Data preview
The data provides data at two spatial scales, national and provincial.
Let's take a look at the nationwide data first. For the monthly SO2 raster data from 2013 to 2020, name the monthly raster file according to the "CHAP_SO2_M10K_Year Month_V1.tif" format, for example : CHAP_SO2_M10K_202001_V1.tif , Expressed as SO2 raster data at 10km resolution in January 2020; CHAP_SO2_M10K_202012_V1 .tif , expressed as SO2 raster data at 10km resolution in December 2020.
Let's take the nationwide SO2 data in January 2013 as an example to preview:
Next, let's look at the data of each province. The naming method of the raster data of the 34 provinces is also named after the year.
Let's take the SO2 data of Sichuan Province and Hubei Province in January 2013 as an example to preview:
02Data 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. Based on this data, the SO2 raster data of 34 provinces were clipped according to the national provincial administrative boundaries.
time frame :
2013-2020 (monthly)
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 monthly SO2 raster data nationwide from 2013 to 2020, you are welcome to pay more attention to us to learn more!