High-resolution estimation of near-surface ozone concentration and population exposure risk in China
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10661-024-12416-5.pdf
Reference51 articles.
1. Che, W. W., Zhang, Y. M., Lin, C. Q., & Fung, Y. H. (2023). Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data. Journal of Environmental Science, 125, 513–523.
2. Chen, J. J., Shen, H. F., & Li, X. H. (2023a). Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data. International Journal of Applied Earth Observation and Geoinformation, 112, 102955.
3. Chen, S. C., Kitayama, K., Itahashi, S., Irie, H., & Shimadera, H. (2023b). Effectiveness of emission controls implemented since 2000 on ambient ozone concentrations in multiple timescales in Japan: An emission inventory development and simulation study. Science of the Total Environment, 894, 165058.
4. Chen, X. Y., Wang, Z. G., Yu, J., Hu, B. F., Shen, Q. H., Xue, J., et al. (2023c). Estimating monthly surface ozone using multi-source satellite products in China based on deep forest model. Atmospheric Environment, 307, 119819.
5. Fei, Z., Liu, C., Cai, Z. N., Liu, X., Bak, J., Kim, J., et al. (2021). Ozone profile retrievals from TROPOMI: Implication for the variation of tropospheric ozone during the outbreak of COVID-19 in China. Science of the Total Environment, 764, 142886.
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