Open Access
Issue
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
Article Number 03005
Number of page(s) 8
Section New Progress in New Energy and Resource Utilization Technology
DOI https://doi.org/10.1051/e3sconf/202452003005
Published online 03 May 2024
  1. Kingdon C. (2023) Air pollution is the largest nvironmental risk to public health and children are specially vulnerable. J. BMJ, 2023, 381. ttps://doi.org/10.1136/bmj.p1037 . [Google Scholar]
  2. Sanjay, R., Jagat, N. (2022) The Perils of nthropogenic Air Pollution: When All Roads Led to ome[J]. Journal of the American College of ardiology, 80(19):1829–1832. ttps://doi.org/10.1016/J.JACC.2022.09.015. [CrossRef] [Google Scholar]
  3. Zhang, X., Cheng, L., Wang, Q., et al. (2019) Simulation f Typical Wind Fields in the Central Part of uanzhong Basin Based on WRF/CALMET. J. haanxi Meteorology, 2019(4), 8–12. oi:10.3969/j.issn.1006-4354.2019.04.003. [Google Scholar]
  4. Ghiaus, C., Allard, F., Santamouris, M., et al. (2006) rban environment influence on natural ventilation otential. J. Building and environment, 2006, 41(4): 395–406. ttps://doi.org/10.1016/j.buildenv.2005.02.003. [Google Scholar]
  5. Bauer, P., Thorpe, A., Brunet, G.. The quiet revolution of umerical weather prediction. J. Nature, 2015, 525(7567): 47–55. https://doi.org/10.1038/nature14956. [CrossRef] [PubMed] [Google Scholar]
  6. Zhang, C., Shu, J. (2011) Numerical Simulation of the nfluence of Land Use Type Changes on the haracteristics of Urban Atmospheric Boundary ayer. J. Journal of East China Normal University Natural Science Edition), 2011(04):83–93. doi: 10.3969/j.issn.1000-5641.2011.04.010. [Google Scholar]
  7. Zhang, Y., Miao, S., Dai, Y., et al. (2013) Numerical imulation of Boundary Layer Characteristics during lear-Sky Summer Days in Beijing and the Influence f Coastal Winds on Urban Underlying Surface. J. hinese Journal of Geophysics,2013, 56(08): 2558–2573. doi: 10.6038/cjg20130806 [Google Scholar]
  8. Chen, G., Zhao, L., Chitanda,, T.. (2016) Simulation tudy on the Impact of Urban Expansion on Urban hermal Environment. J. Journal of rchitecture,2016, 32(10):65–72. doi: 10.13614/j.cnki.11-1962/tu.2016.10.12. [Google Scholar]
  9. Franco, D.M.P., de Fatima, Andrade M., Ynoue, R.Y., et al. (2018) Effect of Local Climate Zone (LCZ) lassification on ozone chemical transport model imulations in Sao Paulo, Brazil. J. Urban Climate, 2019, 27: 293–313. https://doi.org/10.1016/j.uclim.2018.12.007. [CrossRef] [Google Scholar]
  10. Morris, K.I., Chan, A., Morris, K.J.K., et al. (2017) Rbanisation and urban climate of a tropical onurbation, Klang Valley, Malaysia. J. Urban limate, 2017, 19: 54–71. https://doi.org/10.1016/j.uclim.2016.12.002. [CrossRef] [Google Scholar]
  11. Sun, Q., Jiao, R., Xia, J., et al. (2019) Study on Wind peed Correction of Numerical Weather Prediction ased on Machine Learning. J. Meteorological cience,2019, 45(03):426–436. doi: 10.7519/j.issn.1000-0526.2019.03.012. [Google Scholar]
  12. Zhang, C., Liao,, T., Sun,, Y., et al. (2022) Fine-scale low Field Simulation Based on Machine Learning lgorithms. J. Journal of Environmental Sciences, 42(02), 318–331. doi: 10.13671/j.hjkxxb.2021.0256. [Google Scholar]
  13. Han, N., Yang, L., Chen, M., et al. (2022) Machine earning Correction Method for Wind, Temperature, nd Humidity Elements at Jing-Jin-Ji Stations. J. ournal of Applied Meteorological cience ,2022, 33(04):489–500. doi: 10.11898/1001-7313.20220409. [Google Scholar]
  14. Qiu, G., Yu, B., Tao, Y., et al. (2023) Maximum Wind peed Forecast in Yanqing Competition Area of inter Olympics Based on Ensemble Learning lgorithms. J. Meteorological cience, 2023, 49(06):721–732. doi: 10.7519/j.issn.1000-0526.2022.092601. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.