Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations

Wei, Jing; Li, Zhanqing; Wang, Jun; Li, Can; Gupta, Pawan; Cribb, Maureen

doi:https://doi.org/10.5194/acp-23-1511-2023

Articles | Volume 23, issue 2

https://doi.org/10.5194/acp-23-1511-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-1511-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 2

Research article

|

26 Jan 2023

Research article |

| 26 Jan 2023

Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations

Jing Wei, Zhanqing Li, Jun Wang, Can Li, Pawan Gupta, and Maureen Cribb

Supplement

https://doi.org/10.5194/acp-23-1511-2023-supplement

Data sets

ChinaHighNO2: Big Data Seamless 10 km Ground-level NO2 Dataset for China J. Wei and Z. Li https://doi.org/10.5281/zenodo.4641542

ChinaHighSO2: Big Data Seamless 10 km Ground-level SO2 Dataset for China J. Wei and Z. Li https://doi.org/10.5281/zenodo.4641538

ChinaHighCO: Big Data Seamless 10 km Ground-level CO dataset for China J. Wei and Z. Li https://doi.org/10.5281/zenodo.4641530

Data For "Spatially and Temporally Coherent Reconstruction of Tropospheric NO2 over China combining OMI and GOME-2B measurements" Q. He, K. Qin, J. B. Cohen, D. Loyola, D. Li, J. Shi, and Y. Xue https://doi.org/10.6084/m9.figshare.13126847.v1

EARTHDATA NASA https://search.earthdata.nasa.gov/

Jet Propulsion Laboratory NASA https://www2.jpl.nasa.gov/srtm/

NCCS NASA https://portal.nccs.nasa.gov/datashare/gmao/geos-cf/

LandScan ORNL https://landscan.ornl.gov/

ChinaHighAirPollutants (CHAP) J. Wei https://weijing-rs.github.io/product.html

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Short summary

This study estimated the daily seamless 10 km ambient gaseous pollutants (NO₂, SO₂, and CO) across China using machine learning with extensive input variables measured on monitors, satellites, and models. Our dataset yields a high data quality via cross-validation at varying spatiotemporal scales and outperforms most previous related studies, making it most helpful to future (especially short-term) air pollution and environmental health-related studies.

Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations

Supplement

Data sets

Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations