Urban Climate, Journal Year: 2024, Volume and Issue: 57, P. 102130 - 102130
Published: Sept. 1, 2024
Language: Английский
Urban Climate, Journal Year: 2024, Volume and Issue: 57, P. 102130 - 102130
Published: Sept. 1, 2024
Language: Английский
Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112536 - 112536
Published: Jan. 1, 2025
Language: Английский
Citations
0Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102320 - 102320
Published: Jan. 30, 2025
Language: Английский
Citations
0Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122724 - 122724
Published: Feb. 1, 2025
Language: Английский
Citations
0Urban Climate, Journal Year: 2025, Volume and Issue: 61, P. 102393 - 102393
Published: March 25, 2025
Language: Английский
Citations
0Building and Environment, Journal Year: 2025, Volume and Issue: 276, P. 112890 - 112890
Published: March 29, 2025
Language: Английский
Citations
0GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)
Published: Feb. 25, 2024
Machine learning has become an important approach for land use change modeling. However, conventional machine algorithms are limited in their ability to capture causal relationships change, which knowledge planners and decision makers. In this study, we showcase the usefulness of understand heterogeneous effect changing on building height through a case study Shenzhen, China. Also, by leveraging power learning, identify key conditions under greater would occur after interventions. The results suggest that increase 3.68 floors 1.61 average if industrial is converted residential commercial, respectively, 2.35 commercial changed land. heterogeneity also captured different scenarios. factor analysis based tree algorithm reveals use. Overall, can contribute literature providing effective counterfactual modeling with enhanced explainability.
Language: Английский
Citations
3Atmosphere, Journal Year: 2025, Volume and Issue: 16(2), P. 123 - 123
Published: Jan. 23, 2025
The development of new energy vehicles and road dust removal technologies presents opportunities for constructing urban ventilation systems based on patterns. However, the impact system layouts pedestrian-level wind environments remains insufficiently understood. This study utilizes general-purpose CFD software Phoenics to analyze effects orientation, width, density, intersection configurations block ventilation. standard k-ε model three-dimensional steady-state RANS equations are employed calculate mean air age as an indicator efficiency. Grid convergence analysis validation against previous tunnel measurements were conducted. Results show that influence overall efficiency by affecting airflow volume, direction, velocity. Optimal occurs when orientation aligns with prevailing at 0° or exceeds 70°. Recommended widths trunk, secondary, local roads 46 m, 30 18 respectively. Lower densities enhance ventilation, while higher trunk secondary beneficial. Intersection distribution, windward segments aiding lateral side roads. Finally, design strategies proposed, offering potential leveraging networks construct efficient systems.
Language: Английский
Citations
0Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112979 - 112979
Published: April 1, 2025
Language: Английский
Citations
0International Journal of Sustainable Development & World Ecology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20
Published: May 7, 2025
Language: Английский
Citations
0Urban Climate, Journal Year: 2022, Volume and Issue: 43, P. 101181 - 101181
Published: May 1, 2022
Based on a summer observational experiment and the meteorological data from 2009 to 2018, this paper investigated local climate effects of representative 1 Level One 2 Two wind corridors (LOC LTCs, respectively) in central urban area (CUA) Beijing by introducing speed ratio (WsR) heat island (UHI) intensity indices. In addition, impacts ventilation 8 spatial landscape parameters were analyzed. The results indicated that LOC LTCs have significant benefit certain UHI mitigation varying with season, day night weather,which able accelerate winds when speeds exceed 0.57 m/s 0.75 m/s, respectively. most important influencing capability frontal index roughness length. multiyear average WsRs 57% 31% higher than CUA, reduction did not occur along whole corridor or throughout day. maximum seasonal mean during daytime nighttime 0.89 °C °C, mainly reduced 0.30 °C.
Language: Английский
Citations
16