Published March 27, 2021 | Version 1 (NetCDF)

ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)

  • 1. ROR icon University of Maryland, College Park

Description

ChinaHighNO2 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO2 dataset for China from 2008 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m-3, and a mean absolute error (MAE) of 5.34 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):

Note that the ChinaHighNO2 dataset was improved to a 1 km resolution after 2019:

        all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.4571660

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

Notes

Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).

Files

Wei_et_al-ACP-2023.pdf

Files (826.3 MB)

Name Size
md5:1693e244a2b50f5fa47af0729aa974da
70.7 MB Preview Download
md5:f21f0c13f3b4ade79f0110d7f78ff746
70.5 MB Preview Download
md5:c4b9e300ef2b5e1fdde33d0a5d88dd1b
70.7 MB Preview Download
md5:7d85c3b68c1264b973f999dcd12302f2
70.7 MB Preview Download
md5:785b2e83e8d052ca81c4165c3c1c51be
70.9 MB Preview Download
md5:69f2d896e0bc929089a2adf2808d072a
70.8 MB Preview Download
md5:3ff94c9868d53ddf0f770c403c8d8a67
71.0 MB Preview Download
md5:517fc2cd908eeb4ff422ec0629f7c322
70.7 MB Preview Download
md5:f9d0833a29d563aa5018760c609645ee
71.5 MB Preview Download
md5:65c888ac8579e35c462a4c7b8acaa496
71.2 MB Preview Download
md5:14bd8fe8dc4c6dc0594f90f5972fe342
70.0 MB Preview Download
md5:b944bbcf975ed5bf2b37ac5969f08444
2.3 MB Preview Download
md5:2a546643fcfc026516febd31fce2007b
2.3 MB Preview Download
md5:1a4df00fa18e11bdec4ba8efb997fc41
2.3 MB Preview Download
md5:7b8ac76180133bb081252bcb7dd91404
2.3 MB Preview Download
md5:38c8811bdb77d39e7b38d1c44367b278
2.3 MB Preview Download
md5:386ab82da2bec0522273a71bd5e99d34
2.3 MB Preview Download
md5:4cc9a080ae6f15dcac076bb90567e112
2.3 MB Preview Download
md5:3f71ce2d89e69090ca8a737e45d0a914
2.3 MB Preview Download
md5:8bc39abd5f9a9a2f57108af904b0f4ac
2.3 MB Preview Download
md5:316f688ce4d40d762d76443ea04911f0
2.3 MB Preview Download
md5:6e494770202a6f0c94603d7268df4e58
2.3 MB Preview Download
md5:955778e2885fc23b4cf12b447777bf15
584.6 kB Download
md5:f4f0fe5bb6e3b73eaa7c684914cec4a5
584.6 kB Download
md5:eca912d08d18554495060410f0e63983
584.6 kB Download
md5:e873fabc5c5eb33e0b773ec9253dded3
586.2 kB Download
md5:45e6bb15799a1a460ec9f82d530f1b67
586.2 kB Download
md5:24d38da91592f8bd38dcdb163279bbd1
586.3 kB Download
md5:c70eec8079c14ad519e3fec70c1c2386
586.1 kB Download
md5:f39931afd8c5a54fb96d9de48473877c
584.6 kB Download
md5:a8b87414bdc2b5ac56c7b5d05ab42fc7
586.5 kB Download
md5:8331fd21a08ef5b3a592739f8cb945b0
587.0 kB Download
md5:f2e52ed5b567b3abf5fb56bbe1aa313e
584.1 kB Download
md5:5cf23a83044dfb7b76d596d0ea8fe0e6
3.5 kB Preview Download
md5:39e31e794393fcfe42477f80f8c312a6
15.8 MB Preview Download

Additional details

Related works

Is referenced by
Journal article: 10.5194/acp-23-1511-2023 (DOI)

Dates

Available
2021-03-27

References

  • 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
  • Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834