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2 results for “ChinaHighCO”
ChinaHighCO: Daily Seamless 1 km Ground-Level CO Dataset for China (2019–Present)
<p>ChinaHighCO 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.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level CO dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO<sub> </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641530">https://doi.org/10.5281/zenodo.4641530</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighCO: Daily Seamless 10 km Ground-Level CO Dataset for China (2013–2018)
<p>ChinaHighCO 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.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset for China <strong>from 2013 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10477022">https://doi.org/10.5281/zenodo.10477022</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
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