Heat Health Risk and Adaptability Assessments at the Subdistrict Scale in Metropolitan Beijing

Xiaokang Su , Fang Wang , Demin Zhou , Hongwen Zhang

International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (6) : 987 -1003.

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International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (6) : 987 -1003. DOI: 10.1007/s13753-022-00449-8
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Heat Health Risk and Adaptability Assessments at the Subdistrict Scale in Metropolitan Beijing

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Abstract

Against the background of global climate change, the increasing heat health risk from the combined effect of changes in high temperature, exposure, vulnerability, and other factors has become a growing concern. Yet the low number of temperature observation stations is insufficient to represent the complex changes in urban heatwaves, and subdistrict-scale (town, township, neighborhood committee, and equivalent) heat health risk and adaptability assessments are still limited. In this study, we built daytime and nighttime high-temperature interpolation models supported by data from 225 meteorological stations in Beijing. The models performed well at interpolating the cumulative hours of high temperature and the interpolation quality at night was better than that during the day. We further established a methodological framework for heat health risk and adaptability assessments based on heat hazard, population exposure, social vulnerability, and adaptability at the subdistrict scale in Beijing. Our results show that the heat health risk hotspots were mainly located in the central urban area, with 81 hotspots during the day and 76 at night. The average value of the heat health risk index of urban areas was 5.60 times higher than that of suburban areas in the daytime, and 6.70 times higher than that of suburban areas in the night. Greater population density and higher intensity of heat hazards were the main reasons for the high risk in most heat health risk hotspots. Combined with a heat-adaptive-capacity evaluation for hotspot areas, this study suggests that 11 high-risk and low-adaptation subdistricts are priority areas for government action to reduce heat health risk in policy formulation and urban development.

Keywords

Beijing / Heat health risk / Heat adaptability / High-temperature interpolation models / Subdistrict scale

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Xiaokang Su, Fang Wang, Demin Zhou, Hongwen Zhang. Heat Health Risk and Adaptability Assessments at the Subdistrict Scale in Metropolitan Beijing. International Journal of Disaster Risk Science, 2022, 13(6): 987-1003 DOI:10.1007/s13753-022-00449-8

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References

[1]

Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, García-Herrera R. The hot summer of 2010: Redrawing the temperature record map of Europe. Science, 2011, 332(6026): 220-224

[2]

Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 2009, 113(5): 893-903

[3]

Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA. Temperature and mortality in 11 cities of the eastern United States. American Journal of Epidemiology, 2002, 155(1): 80-87

[4]

Cutter SL, Finch C. Temporal and spatial changes in social vulnerability to natural hazards. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(7): 2301-2306

[5]

Cutter SL, Boruff BJ, Shirley WL. Social vulnerability to environmental hazards. Social Science Quarterly, 2003, 84(2): 242-261

[6]

Du W, Quan W, Xuan C, Fang X, Guo W. The study of high temperature disaster risk zoning in Beijing-Tianjin-Hebei urban agglomeration. Journal of Nanjing University (Natural Sciences), 2014, 50(6): 829-837.

[7]

El-Zein A, Tonmoy FN. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney. Ecological Indicators, 2015, 48: 207-217

[8]

Hu K, Yang X, Zhong J, Fei F, Qi J. Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data. Environmental Science & Technology, 2017, 51(3): 1498-1507

[9]

Hua, J., X. Zhang, C. Ren, Y. Shi, and T.-C. Lee. 2021. Spatiotemporal assessment of extreme heat risk for high-density cities: A case study of Hong Kong from 2006 to 2016. Sustainable Cities and Society 64: Article 102507.

[10]

IPCC (Intergovernmental Panel on Climate Change). 2007. Climate change 2007: The physical science basis. Working group I contribution to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

[11]

IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Working group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press.

[12]

Johnson DP, Stanforth A, Lulla V, Luber G. Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Applied Geography, 2012, 35(1–2): 23-31

[13]

Kestens, Y., A. Brand, M. Fournier, S. Goudreau, T. Kosatsky, M. Maloley, and A. Smargiassi. 2011. Modelling the variation of land surface temperature as determinant of risk of heat-related health events. International Journal of Health Geographics 10(1): Article 7.

[14]

Klinenberg E. Heat wave: A social autopsy of disaster in Chicago, 2003, Chicago and London: University of Chicago Press

[15]

Li, H., J. Zhao, Y. Gao, and Z. Gu. 2022. Prediction and evaluation of spatial distributions of ozone and urban heat island using a machine learning modified land use regression method. Sustainable Cities and Society 78: Article 103643.

[16]

Luber G, McGeehin M. Climate change and extreme heat events. American Journal of Preventive Medicine, 2008, 35(5): 429-435

[17]

Luo X, Du Y, Zheng J. Risk regionalization of human health caused by high temperature & heat wave in Guangdong Province. Climate Change Research, 2016, 12(2): 139-146.

[18]

Morabito, M., A. Crisci, B. Gioli, G. Gualtieri, P. Toscano, V. Di Stefano, S. Orlandini, and G.F. Gensini. 2015. Urban-hazard risk analysis: Mapping of heat-related risks in the elderly in major Italian cities. PLOS ONE 10(5): Article e0127277.

[19]

Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature, 2005, 438: 310-317

[20]

Reid CE, O'Neill MS, Gronlund CJ, Brines SJ, Brown DG, Diez-Roux AV, Schwartz J. Mapping community determinants of heat vulnerability. Environmental Health Perspectives, 2009, 117(11): 1730-1736

[21]

Semenza JC, Rubin CH, Falter KH, Selanikio JD, Flanders WD, Howe HL, Wilhelm JL. Heat-related deaths during the July 1995 heat wave in Chicago. New England Journal of Medicine, 1996, 335(2): 84-90

[22]

Shi Y, Ren C, Cai M, Lau K-L, Lee T-C, Wong W-K. Assessing spatial variability of extreme hot weather conditions in Hong Kong: A land use regression approach. Environmental Research, 2019, 171: 403-415

[23]

Stott PA, Stone DA, Allen MR. Human contribution to the European heatwave of 2003. Nature, 2004, 432(7017): 610-614

[24]

Tomlinson, C.J., L. Chapman, J.E. Thornes, and C.J. Baker. 2011. Including the urban heat island in spatial heat health risk assessment strategies: A case study for Birmingham, UK. International Journal of Health Geographics 10: Article 42.

[25]

Uejio CK, Wilhelmi OV, Golden JS, Mills DM, Gulino SP, Samenow JP. Intra-urban societal vulnerability to extreme heat: The role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health & Place, 2011, 17(2): 498-507

[26]

Weng Q, Lu D, Schubring J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 2004, 89(4): 467-483

[27]

Wolf T, McGregor G. The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes, 2013, 1: 59-68

[28]

Wolf T, Chuang W-C, McGregor G. On the science-policy bridge: Do spatial heat vulnerability assessment studies influence policy?. International Journal of Environmental Research and Public Health, 2015, 12(10): 13321-13349

[29]

Xie P, Wang Y, Liu Y, Peng J. Incorporating social vulnerability to assess population health risk due to heat stress in China. Acta Geographica Sinica, 2015, 70(7): 1041-1051.

[30]

Xie P, Wang Y, Liu Y, Peng J. Health related urban heat wave vulnerability assessment: Research progress and framework. Progress in Geography, 2015, 34(2): 165-174.

[31]

Yin Z, Yin J, Zhang X. Multi-scenario-based hazard analysis of high temperature extremes experienced in China during 1951–2010. Journal of Geographical Sciences, 2013, 23(3): 436-446

[32]

Zhang M, Li H. Framework and application of health risk assessment of heat wave in Beijing. Journal of Environmental Health, 2020, 37(1): 58-65 in Chinese

[33]

Zhu, Q., T. Liu, H. Lin, J. Xiao, Y. Luo, W. Zeng, S. Zeng, Y. Wei, et al. 2014. The spatial distribution of health vulnerability to heat waves in Guangdong Province, China. Global Health Action 7: Article 25051.

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