[Reviewed international journal paper]

• Chao Lin, Yunyi Wang, Ryozo Ooka, Cédric Flageul, Youngseob Kim, Hideki Kikumoto, Zhizhao Wang, and Karine Sartelet
• Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD
• https://doi.org/10.5194/acp-23-1421-2023
• Atmospheric Chemistry and Physics, 23, pp.1421–1436

• Wenchao Wang, Ryozo Ooka, Hideki Kikumoto, Wonseok Oh, Chao Lin, Mengtao Han
• A numerical study on the relationship between particle dispersion, accumulation, and indoor airflow in different ventilated rooms
• https://doi.org/10.1002/2475-8876.12332
• Japan Architectural Review, 6, e12332

• Hongyuan Jia & Hideki Kikumoto
• Backward-Eulerian Footprint Modelling Based on the Adjoint Equation for Atmospheric and Urban-Terrain Dispersion
• https://link.springer.com/article/10.1007/s10546-023-00807-z
• Boundary-Layer Meteorology , 188, 159–183 (2023)

• Bingchao Zhang, Ryozo Ooka, Hideki Kikumoto, Tim K. T. Tse
• Investigation on favorable pressure gradient in an atmospheric boundary layer wind tunnel using large-eddy simulation and its impact on wind field and surface pressure measurement
• https://doi.org/10.1002/2475-8876.12361
• Japan Architectural Review, 6, e12361

• Dun Zhu, Ryozo Ooka
• WRF-based scenario experiment research on urban heat island: A review
• https://doi.org/10.1016/j.uclim.2023.101512
• Urban Climate, 49, 101512

• Chao Lin, Ryozo Ooka, Hideki Kikumoto, Cédric Flageul, Youngseob Kim, Yunyi Wang, Alice Maison, Yang Zhang, Karine Sartelet
• Large-eddy simulations on pollutant reduction effects of road-center hedge and solid barriers in an idealized street canyon
• https://doi.org/10.1016/j.buildenv.2023.110464
• Building and Environment, 241, 110464

• Chenghao Wei , Ryozo Ooka
• Indoor airflow field reconstruction using physics-informed neural network
• https://doi.org/10.1016/j.buildenv.2023.110563
• Building and Environment, 242, 110563

• Rongmao Li, Hideki Kikumoto, Hongyuan Jia, Tsubasa Okaze
• Transfer learning strategy for data-driven correction of wind measurement around buildings using cup anemometers
• https://doi.org/10.1016/j.buildenv.2023.110499
• Building and Environment, 241, 110499

• Xiang Wang, Hideki Kikumoto, Hongyuan Jia, Chao Lin, Keisuke Nakao
• Investigation of spatial variability in power law index of wind profiles above the urban area around Tokyo Bay based on local objective analysis data
• ttps://doi.org/10.1016/j.jweia.2023.105471
• Journal of Wind Engineering & Industrial Aerodynamics , 240, 105471

• Chaoyi Hu , Hongyuan Jia , Hideki Kikumoto
• Estimation of instantaneous airflow distribution in cubic building group model using multi-time-delay LSE-POD
• https://doi.org/10.1016/j.buildenv.2023.110642
• Building and Environment, 243, 110642

• Hongyuan Jia , Chaoyi Hu , Hideki Kikumoto
• Effects of sensor configuration optimization on airflow estimation in urban environment: A case study with a building group model
• https://doi.org/10.1016/j.scs.2023.104840
• Sustainable Cities and Society, 98, 104840

• Jia Tian, Ryozo Ooka, Doyun Lee
• Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review
• https://doi.org/10.1016/j.jclepro.2023.139040
• Journal of Cleaner Production, 426, 139040

• Wenchao Wang, Ryozo Ooka, Hideki Kikumoto, Wonseok Oh, Mengtao Han
• Comparison of pollen indoor/outdoor ratios during off-peak and peak periods in Japan
• https://doi.org/10.1016/j.buildenv.2023.111018
• Building and Environment, , 111018

• Xiang Wang, Hideki Kikumoto, Chaoyi Hu, Hongyuan Jia, Chao Lin, Keisuke Nakao
• Estimation of near-ground wind velocity and temperature in the urban area around Tokyo Bay using local objective analysis data and the POD–LSE method
• 16th ICWE International Conference on Wind Engineering, 2023.8

• Rongmao Li, Hideki Kikumoto and Hongyuan Jia
• Data-driven calibration for cup anemometer at different measurement locations around buildings using transfer learning based on domain adaptation
• https://doi.org/10.1051/e3sconf/202339605016
• IAQVEC2023, 2023.6