Abstract
Based on the building height and density data on a 100-m resolution, hourly 2-m temperature and humidity data at 83 automatic weather stations, and gridded local climate zone (LCZ) data on a 120-m resolution in urban Beijing in 2020, this study first employs the semivariogram combined with building parameters to calculate spatial correlations and has identified an LCZ grid resolution of 500 m suitable for best usage of the available observation data. Then, how the spatially heterogeneous LCZs affect and contribute to the canopy urban heat island intensity (UHII) and urban dry island intensity (UDII) are quantitatively investigated. It is found that UHII is high in winter and low in summer with a unimodal diurnal variation while UDI is low in winter but high in summer with a bimodal diurnal variation. The LCZ with compact mid-rise (open high-rise) buildings exhibits the highest UHII (UDII), followed by the compact high-rise (compact low-rise), while the LCZ of scattered trees presents both the lowest UHII and the lowest UDII. The most significant difference in the UHII (UDII) among the nine LCZ types in the urban area of Beijing is 2.62°C (1.1 g kg−1). Area-weighted averaging analysis reveals that the open mid-rise LCZ is the most significant contributor to the UHII (UDII), immediately followed by compact mid-rise (open low-rise), with the least contribution from bare rock or paved (scattered trees). The results also indicate that beyond the intrinsic physical properties of the LCZs of a city, their area proportions cannot be overlooked in evaluating their impact on the UHI and UDI. These quantitatively findings could help urban planners to create a livable urban climate and environment by adjusting the relevant land use.
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References
Anjos, M., A. C. Targino, P. Krecl, et al., 2020: Analysis of the urban heat island under different synoptic patterns using local climate zones. Build. Environ., 185, 107268, doi: https://doi.org/10.1016/j.buildenv.2020.107268.
Bechtel, B., M. Demuzere, G. Mills, et al., 2019: SUHI analysis using Local Climate Zones—A comparison of 50 cities. Urban Climate, 28, 100451, doi: https://doi.org/10.1016/j.uclim.2019.01.005.
Cai, M., C. Ren, and Y. Xu, 2017: Investigating the relationship between Local Climate Zone and land surface temperature. Proceedings of 2017 Joint Urban Remote Sensing Event, IEEE, Dubai, United Arab Emirates, 1–4, doi: https://doi.org/10.1109/JURSE.2017.7924622.
Chen, S. H., Y. J. Yang, F. Deng, et al., 2022: A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations. Atmos. Meas. Tech., 15, 735–756, doi: https://doi.org/10.5194/amt-15-735-2022.
Chen, X. L., Y. Xu, J. X. Yang, et al., 2020: Remote sensing of urban thermal environments within local climate zones: A case study of two high-density subtropical Chinese cities. Urban Climate, 31, 100568, doi: https://doi.org/10.1016/j.uclim.2019.100.568568.
Chen, Y. C., T. P. Lin, and W. Y. Shih, 2017: Modeling the urban thermal environment distributions in Taipei Basin using Local Climate Zone (LCZ). Proceedings of 2017 Joint Urban Remote Sensing Event, IEEE, Dubai, United Arab Emirates, 1–1, doi: https://doi.org/10.1109/JURSE.2017.7924531.
Couclelis, H., 1992: People manipulate objects (but cultivate fields): Beyond the raster-vector debate in GIS. Proceedings of the International Conference GIS-From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio–temporal Reasoning in Geographic Space, Pisa, Italy, Springer, 65–77, doi: https://doi.org/10.1007/3-540-55966-3_3.
Crutzen, P. J., 2004: New directions: The growing urban heat and pollution “island” effect-impact on chemistry and climate. Atmos. Environ., 38, 3539–3540, doi: https://doi.org/10.1016/j.atmosenv.2004.03.032.
Dang, B., Y. H. Liu, H. L. Lyu, et al., 2022: Assessment of urban climate environment and configuration of ventilation corridor: A refined study in Xi’an. J. Meteor. Res., 36, 914–930, doi: https://doi.org/10.1007/s13351-022-2035-0.
Grimm, N. B., S. H. Faeth, N. E. Golubiewski, et al., 2008: Global change and the ecology of cities. Science, 319, 756–760, doi: https://doi.org/10.1126/science.1150195.
Huang, X., and Y. Wang, 2019: Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China. ISPRS J. Photogramm. Remote Sens., 152, 119–131, doi: https://doi.org/10.1016/j.is-prsjprs.2019.04.010.
Hutengs, C., and M. Vohland, 2016: Downscaling land surface temperatures at regional scales with random forest regression. Remote Sens. Environ., 178, 127–141, doi: https://doi.org/10.1016/j.rse.2016.03.006.
Keramitsoglou, I., C. T. Kiranoudis, and Q. H. Weng, 2013: Down scaling geostationary land surface temperature imagery for urban analysis. IEEE Geosci. Remote Sens. Lett., 10, 1253–1257, doi: https://doi.org/10.1109/LGRS.2013.2257668.
Kotharkar, R., and A. Bagade, 2018: Evaluating urban heat island in the critical local climate zones of an Indian city. Landscape Urban Plann., 169, 92–104, doi: https://doi.org/10.1016/j.landurbplan.2017.08.009.
Leconte, F., J. Bouyer, R. Claverie, et al., 2015: Using Local Climate Zone scheme for UHI assessment: Evaluation of the method using mobile measurements. Build. Environ., 83, 39–49, doi: https://doi.org/10.1016/j.buildenv.2014.05.005.
Li, N. N., J. Yang, Z. Qiao, et al., 2021: Urban thermal characteristics of local climate zones and their mitigation measures across cities in different climate zones of China. Remote Sens., 13, 1468, doi: https://doi.org/10.3390/rs13081468.
Li, Y. F., S. Schubert, J. P. Kropp, et al., 2020: On the influence of density and morphology on the Urban Heat Island intensity. Nat. Commun., 11, 2647, doi: https://doi.org/10.1038/s41467-020-16461-9.
Liu, J., L. Shen, Y. Huang, et al., 2020: Research on the spatial differentiation characteristics of nocturnal urban heat island intensity in Beijing based on local climate zones. Geogr. GeoInf. Sci., 36, 39–45, 64, doi: https://doi.org/10.3969/j.issn.1672-0504.2020.05.006. (in Chinese)
Liu, Y. H., Y. M. Xu, Y. P. Zhang, et al., 2022: Impacts of the urban spatial landscape in Beijing on surface and canopy urban heat islands. J. Meteor. Res., 36, 822–999, doi: https://doi.org/10.1007/s13351-022-2045-y.
Luo, F., Y. J. Yang, L. Zong, et al., 2023: The interactions between urban heat island and heat waves amplify urban warming in Guangzhou, China: Roles of urban ventilation and local climate zones. Front. Environ. Sci., 11, 1084473, doi: https://doi.org/10.3389/fenvs.2023.1084473.
Luo, M., and N.-C. Lau, 2018: Increasing heat stress in urban areas of eastern China: Acceleration by urbanization. Geophys. Res. Lett., 45, 13,060–13,069, doi: https://doi.org/10.1029/2018GL080306.
Luo, M., and N.-C. Lau, 2019: Urbanexpansion and drying climate in anurban agglomeration of East China. Geophys. Res. Lett., 46, 6868–6877, doi: https://doi.org/10.1029/2019GL082736.
Merbitz, H., M. Buttstädt, S. Michael, et al., 2012: GIS-based identification of spatial variables enhancing heat and poor air quality in urban areas. Appl. Geogr., 33, 94–106, doi: https://doi.org/10.1016/j.apgeog.2011.06.008.
Miller, R. B., and C. Small, 2003: Cities from space: Potential applications of remote sensing in urban environmental research and policy. Environ. Sci. Policy, 6, 129–137, doi: https://doi.org/10.1016/S1462-9011(03)00002-9.
Mu, Q. C., S. G. Miao, Y. W. Wang, et al., 2020: Evaluation of employing local climate zone classification for mesoscale modelling over Beijing metropolitan area. Meteor. Atmos. Phys., 132, 315–326, doi: https://doi.org/10.1007/s00703-019-00692-7.
Patz, J. A., D. Campbell-Lendrum, T. Holloway, et al., 2005: Impact of regional climate change on human health. Nature, 438, 310–317, doi: https://doi.org/10.1038/nature04188.
Ren, C., K. L. Lau, K. P. Yiu, et al., 2013: The application of urban climatic mapping to the urban planning of high-density cities: The case of Kaohsiung, Taiwan. Ctties, 31, 1–16, doi: https://doi.org/10.1016/j.cities.2012.12.005.
Ren, C., K. Wang, Y. Shi, et al., 2021: Investigating the urban heat and cool island effects during extreme heat events in high-density cities: A case study of Hong Kong from 2000 to 2018. Int. J. Climatol., 41, 6736–6754, doi: https://doi.org/10.1002/joc.7222.
Sismanidis, P., I. Keramitsoglou, C. Kiranoudis, et al., 2016: Assessing the capability of a downscaled urban land surface temperature time series to reproduce the spatiotemporal features of the original data. Remote Sens., 8, 274, doi: https://doi.org/10.3390/rs8040274.
Stewart, I. D., 2011: A systematic review and scientific critique of methodology in modern urban heat island literature. Int. J. Climatol., 31, 200–217, doi: https://doi.org/10.1002/joc.2141.
Stewart, I. D., and T. R. Oke, 2012: Local climate zones for urban temperature studies. Bull. Amer. Meteor. Soc., 93, 1879–1900, doi: https://doi.org/10.1175/BAMS-D-11-00019.1.
Stewart, I. D., T. R. Oke, and E. S. Krayenhoff, 2014: Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations. Int. J. Climatol., 34, 1062–1080, doi: https://doi.org/10.1002/joc.3746.
Tian, W. S., Y. J. Yang, L. L. Wang, et al., 2023: Role of local climate zones and urban ventilation in canopy urban heat island-heatwave interaction in Nanjing megacity, China. Urban Climate, 49, 101474, doi: https://doi.org/10.1016/j.uclim.2023.101474.
Wang, C. Y., A. Middel, S. W. Myint, et al., 2018: Assessing local climate zones in arid cities: The case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J. Photogramm. Remote Sens., 141, 59–71, doi: https://doi.org/10.1016/j.isprsjprs.2018.04.009.
Wang, R., M. Cai, C. Ren, et al., 2019: Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone. Urban Climate, 28, 100455, doi: https://doi.org/10.1016/j.uclim.2019.100455.
Weng, Q. H., 2009: Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J. Photogramm. Remote Sens., 64, 335–344, doi: https://doi.org/10.1016/j.isprsjprs.2009.03.007.
Xu, Y., C. Ren, M. Cai, et al., 2017: Classification of local climate zones using ASTER and landsat data for high-density cities. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 3397–3405, doi: https://doi.org/10.1109/JSTARS.2017.2683484.
Xue, J. S., L. Zong, Y. J. Yang, et al., 2023: Diurnal and interannual variations of canopy urban heat island (CUHI) effects over a mountain-valley city with a semi-arid climate. Urban Climate, 48, 101425, doi: https://doi.org/10.1016/j.uclim.2023.101425.
Yang, D. H., Y. W. Wang, and C. J. Yue, 2022: Effect of using land use data with building characteristics on urban weather simulations: A high temperature event in Shanghai. J. Meteor. Res., 36, 900–913, doi: https://doi.org/10.1007/s13351-022-2104-4.
Yang, J., Y. X. Zhan, X. M. Xiao, et al., 2020: Investigating the diversity of land surface temperature characteristics in different scale cities based on local climate zones. Urban Climate, 34, 100700, doi: https://doi.org/10.1016/j.uclim.2020.100700.
Yang, J. C., and E. Bou-Zeid, 2019: Scale dependence of the benefits and efficiency of green and cool roofs. Landscape Urban Plann., 185, 127–140, doi: https://doi.org/10.1016/j.landurbplan.2019.02.004.
Yang, J. C., Z. H. Wang, K. E. Kaloush, et al., 2016: Effect of pavement thermal properties on mitigating urban heat islands: A multi-scale modeling case study in Phoenix. Build. Environ., 108, 110–121, doi: https://doi.org/10.1016/j.buildenv.2016.08.021.
Yang, P., G. Y. Ren, and W. D. Liu, 2013: Spatial and temporal characteristics of Beijing urban heat island intensity. J. Appl. Meteor. Climatol., 52, 1803–1816, doi: https://doi.org/10.1175/JAMC-D-12-0125.1.
Yang, Y. J., M. Y. Zhang, Q. X. Li, et al., 2020a: Modulations of surface thermal environment and agricultural activity on intraseasonal variations of summer diurnal temperature range in the Yangtze River Delta of China. Sci. Total Environ., 736, 139445, doi: https://doi.org/10.1016/j.scitotenv.2020.139445.
Yang, Y. J., Z. F. Zheng, S. Y. L. Yim, et al., 2020b: PM2.5 pollution modulates wintertime urban heat island intensity in the Beijing–Tianjin–Hebei megalopolis, China. Geophys. Res. Lett., 47, e2019GL084288, doi: https://doi.org/10.1029/2019gl084288.
Yang, Y. J., M. Guo, G. Y. Ren, et al., 2022: Modulation of wintertime canopy Urban Heat Island (CUHI) intensity in Beijing by synoptic weather pattern in planetary boundary layer. J. Geophys. Res. Atmos., 127, e2021JD035988, doi: https://doi.org/10.1029/2021JD035988.
Yang, Y. J., M. Guo, L. L. Wang, et al., 2023: Unevenly spatiotemporal distribution of urban excess warming in coastal Shanghai megacity, China: Roles of geophysical environment, ventilation and sea breezes. Build. Environ., 235, 110180, doi: https://doi.org/10.1016/j.buildenv.2023.110180.
Zhang, H., M. Luo, T. Pei, et al., 2023: Unequal urban heat burdens impede climate justice and equity goals. Innovation, 4, 100488, doi: https://doi.org/10.1016/j.xinn.2023.100488.
Zhang, L., Luo, F., Pan, G., Zhang, W., Ren, G., Zheng, Z., & Yang, Y. 2024: Elucidating the multi-timescale variability of a canopy urban heat island by using the short-time Fourier transform. Geophysical Research Letters, 51, 22023GL106221, doi: https://doi.org/10.1029/2023GL106221.
Zheng, Y. S., C. Ren, Y. Xu, et al., 2018: GIS-based mapping of Local Climate Zone in the high-density city of Hong Kong. Urban Climate, 24, 419–448, doi. https://doi.org/10.1016/j.uclim.2017.05.008.
Zheng, Z. F., G. Y. Ren, H. Wang, et al., 2018: Relationship between fine particle pollution and the urban heat island in Beijing, China: Observational evidence. Bound.-Layer Meteor., 169, 93–113, doi: https://doi.org/10.1007/s10546-018-0362-6.
Zheng, Z. F., G. R. Xu, and H. Gao, 2021: Characteristics of summer hourly extreme precipitation events and its local environmental influencing factors in Beijing under urbanization background. Atmosphere, 12, 632, doi: https://doi.org/10.3390/atmos12050632.
Zheng, Z. F., G. Y. Ren, H. Gao, et al., 2022: Urban ventilation planning and its associated benefits based on numerical experiments: A case study in Beijing, China. Build. Environ., 222, 109383, doi: https://doi.org/10.1016/j.buildenv.2022.109383.
Zong, L., S. H. Liu, Y. J. Yang, et al., 2021: Synergistic influence of local climate zones and wind speeds on the urban heat island and heat waves in the megacity of Beijing, China. Front. Earth Sci., 9, 673786, doi: https://doi.org/10.3389/feart.2021.673786.
Zong, L., Y. J. Yang, H. Y. Xia, et al., 2022: Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: Roles of synoptic weather pattern and urbanization. Atmos. Chem. Phys., 22, 6523–6538, doi: https://doi.org/10.5194/acp-22-6523-2022.
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Supported by the National Natural Science Foundation of China (42171337 and 42222503).
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Zheng, Z., Luo, F., Li, N. et al. Impact of Local Climate Zones on the Urban Heat and Dry Islands in Beijing: Spatial Heterogeneity and Relative Contributions. J Meteorol Res 38, 126–137 (2024). https://doi.org/10.1007/s13351-024-3081-6
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DOI: https://doi.org/10.1007/s13351-024-3081-6