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3 results for “MDA8”
Gridded MDA8 surface ozone observations for the EU and US during 1993-2014
<p>Maximum daily eight-hour average (MDA8) surface ozone derived from gridded hourly observational datasets for the European and US domain [Schnell et al., 2014; 2015; Schnell and Prather, 2017], according to the MDA8 nomenclature of the European Union [EUR-LEX, 2008]. Note, we use a 358-day calendar, i.e. all months have 30 days except for February (28 days).</p> <p>EUR-LEX. (2008). Directive 2008/50/EC of the European parliament and of the council of 21 May 2008 on ambient air quality and cleaner air for Europe (2008/50/EC). Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32008L0050</p> <p>Schnell, J. L., Holmes, C. D., Jangam, A., Prather, M. J. (2014). Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model. Atmos Chem Phys, 14(15), 7721-7739. doi:10.5194/acp-14-7721-2014</p> <p>Schnell, J. L., Prather, M. J., Josse, B., Naik, V., Horowitz, L. W., Cameron-Smith, P., Bergmann, D., Zeng, G., Plummer, D. A., Sudo, K., Nagashima, T., Shindell, D. T., Faluvegi, G., Strode, S. A. (2015). Use of North American and European air quality networks to evaluate global chemistry–climate modeling of surface ozone. Atmos. Chem. Phys., 15(18), 10581-10596. doi:10.5194/acp-15-10581-2015</p> <p>Schnell, J. L., Prather, M. J. (2017). Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America. Proceedings of the National Academy of Sciences, 114(11), 2854-2859. doi:10.1073/pnas.1614453114</p>
Reconstructed daily ground-level MDA8 O3 over 2005-2022 in China
<p>This daily ground-level MDA8 O3 dataset covers a long time period (2005-2022) with 0.1°×0.1° gap-free grids. Here we upload the monthly and annual mean datasets. </p>
ChinaHighO3: Big Data Seamless 10 km Ground-level MDA8 O3 Dataset for China
<p>ChinaHighO<sub>3</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level maximum 8-hour average (MDA8) O<sub>3</sub> dataset in China from 1979 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.87, a root-mean-square error (RMSE) of 17.10 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.29 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022; He et al., 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2022.pdf">Full-coverage mapping and spatiotemporal variations of ground-level ozone (O<sub>3</sub>) pollution from 2013 to 2020 across China</a>. <em>Remote Sensing of Environment</em>, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775</p> </li> <li> <p>He, L., Wei, J., Wang, Y., Shang, Q., Liu, J., Yin, Y., Frankerberg, C., Jiang, J., Li, Z., and Yung, Y. <a href="https://weijing-rs.github.io/publications/He_et_al-EF-2022.pdf">Marked impacts of pollution mitigation on crop yields in China</a>. <em>Earth's Future</em>, 2022, 10, e2022EF002936. https://doi.org/10.1029/2022EF002936</p> </li> </ul> <p><strong>Note that access to this dataset is now restricted, as a longer-term (2000 to present), high-resolution (1 km), and higher quality ChinaHighO<sub>3</sub> dataset is now available: <a href="http://doi.org/10.5281/zenodo.10477125">http://doi.org/10.5281/zenodo.10477125</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
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