CoChat
Sign Up
Cited by 37Urban Heat Island MitigationClimate Change and Health ImpactsClimate Change and Environmental Impact

Urban heat vulnerability: A dynamic assessment using multi-source data in coastal metropolis of Southeast China

Open access
Wu, Chaowei, Shui, Wei, Huang, Zhigang, Wang, Chunhui, Wu, Yuehui, Wu, Yinpan, Xue, Chengzhi, Huang, Yunhui +2 more·2022·Frontiers in Public Health
DOI 10.3389/fpubh.2022.989963PMC PMC9632749
Open this paper in CoChat — free accountOpen PDF

Abstract

Extreme heat caused by global climate change has become a serious threat to the sustainable development of urban areas. Scientific assessment of the impacts of extreme heat on urban areas and in-depth knowledge of the cross-scale mechanisms of heat vulnerability forming in urban systems are expected to support policymakers and stakeholders in developing effective policies to mitigate the economic, social, and health risks. Based on the perspective of the human-environment system, this study constructed a conceptual framework and index system of “exposure-susceptibility-adaptive capacity” for urban heat vulnerability (UHV) and proposed its assessment methods. Taking Xiamen City, a coastal metropolis, as an example, spatial analysis and Geodetector were used to explore the spatial and temporal changes, spatial characteristics, and patterns of UHV under multiple external disturbances from natural to anthropological factors, and to reveal the main factors influencing UHV forming and spatial differentiation. Results showed that the exposure, susceptibility, adaptive capacity, and UHV in Xiamen City had a spatial structure of “coastal-offshore-inland”. On the hot day, both the exposure and UHV showed a temporal pattern of “rising and then falling, peaking at 14:00” and a spatial pattern of “monsoonal-like” movement between coast and inland. Coastal zoning with favorable socioeconomic conditions had less magnitude of changes in UHV, where the stability of the urban system was more likely to be maintained. During the hot months, the high UHV areas were mainly distributed in the inland, while coastal areas showed low UHV levels. Further, coastal UHV was mainly dominated by “heat exposure”, offshore by “comprehensive factors”, and inland in the northern mountainous areas by “lack of adaptive capacity”. Multi-scale urban adaptive capacity was confirmed to alter spatial distribution of exposure and reshape the spatial pattern of UHV. This study promotes the application of multi-scale vulnerability framework to disaster impact assessment, enriches the scientific knowledge of the urban system vulnerability, and provides scientific references for local targeted cooling policy development and extreme heat resilience building programs.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fpubh.2022.989963

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In climate- and urban heat-risk assessment, the most critical areas are those in which a strong thermal hazard co-occurs with high exposure and increased vulnerability.

    “This follows the general idea of climate- and urban heat-risk assessment, where the most critical areas are those in which a strong thermal hazard co-occurs with high exposure and increased vulnerability [32, 33].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [33]

  • Normalizing predicted fields to a common interval such as [0,1] is a standard step when combining heterogeneous indicators with different units and scales.

    “For integration with the remaining components of the framework, the predicted field can be normalized to a common interval such as [0 , 1], which is a standard step when combining heterogeneous indicators with different units and scales [32, 33].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [33]

  • The simplified multiplicative risk formulation is sufficient to show how next-day thermal forecasting can be extended towards interpretable urban heat risk assessment products.

    “Although this formulation is simplified, it is sufficient to show how next-day thermal forecasting can be extended towards interpretable urban heat risk assessment products [30, 33].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [33]

  • In urban heat research, vulnerability is commonly associated with environmental, demographic, social, and urban-form characteristics that increase sensitivity or reduce coping capacity under hot conditions.

    “In urban heat research, vulnerability is commonly associated with environmental, demographic, social, and urban-form characteristics that increase sensitivity or reduce coping capacity under hot conditions [33-35].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [33]

  • Multiplicative and similar composite formulations based on normalized indicators are often used in heat-vulnerability and heat-risk studies because they emphasize the joint effects of multiple factors, rather than relying solely on temperature.

    “Multiplicative and similar composite formulations based on normalized indicators are often used in heatvulnerability and heat-risk studies because they emphasize the joint effects of multiple factors, rather than relying solely on temperature [32, 33].”

    From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [33]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 77

  1. [1]Seneviratne SI Zhang X Adnan M Badi W Dereczynski C. “Chapter 11: Weather and Climate Extreme Events in a Changing Climate”. In: Masson-Delmotte V Zhai P Pirani A Connors S L Péan C Berger S., editors. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press (2021). p. 1533– 766.
  2. [2]Sun Y Zhang X Zwiers FW Song L Wan H Hu T. Rapid increase in the risk of extreme summer heat in Eastern China. Nat Clim Change. (2014) 4: 1082– 5. 10.1038/nclimate2410 · DOI
  3. [3]Zhao L Oppenheimer M Zhu Q Baldwin JW Ebi KL Bou-Zeid E. Interactions between urban heat islands and heat waves. Environ Res Lett. (2018) 13: 34003. 10.1088/1748-9326/aa9f7327894802 · DOI
  4. [4]Oleson KW Anderson GB Jones B McGinnis SA Sanderson B. Avoided climate impacts of urban and rural heat and cold waves over the US using large climate model ensembles for RCP85 and RCP45. Clim Change. (2018) 146: 377– 92. 10.1007/s10584-015-1504-1 29520121 PMC5839517 · DOI
  5. [5]Chinowsky P Arndt C. climate change and roads: a dynamic stressor–response model. Rev Dev Econ. (2012) 16: 448– 62. 10.1111/j.1467-9361.2012.00673.x · DOI
  6. [6]Chinowsky P Helman J Gulati S Neumann J Martinich J. Impacts of climate change on operation of the US rail network. Transport Policy. (2019) 75: 183– 91. 10.1016/j.tranpol.2017.05.007 · DOI
  7. [7]Zhou T Ren L Liu H Lu J. Impact of 15 °C and 20 °C global warming on aircraft takeoff performance in China. Sci Bull. (2018) 63: 700– 7. 10.1016/j.scib.2018.03.018 36658819 · DOI
  8. [8]Gao X Schlosser CA Morgan ER. Potential impacts of climate warming and increased summer heat stress on the electric grid: a case study for a large power transformer (LPT) in the Northeast United States. Clim Change. (2018) 147: 107– 18. 10.1007/s10584-017-2114-x · DOI
  9. [9]Craig MT Cohen S Macknick J Draxl C Guerra OJ Sengupta M. A review of the potential impacts of climate change on bulk power system planning and operations in the United States. Renew Sust Energ Rev. (2018) 98: 255– 67. 10.1016/j.rser.2018.09.022 · DOI
  10. [10]Heaney AK Carrión D Burkart K Lesk C Jack D. Climate change and physical activity: estimated impacts of ambient temperatures on bikeshare usage in New York city. Environ Health Persp. (2019) 127: 37002. 10.1289/EHP4039 30835141 PMC6768325 · DOI
  11. [11]Smith MT Reid M Kovalchik S Woods TO Duffield R. Heat stress incident prevalence and tennis matchplay performance at the Australian Open. J Sci Med Sport. (2018) 21: 467– 72. 10.1016/j.jsams.2017.08.019 28919493 · DOI
  12. [12]Orlov A Sillmann J Aaheim A Aunan K de Bruin K. Economic losses of heat-induced reductions in outdoor worker productivity: a case study of Europe. Econ Disaster Clim Chang. (2019) 3: 191– 211. 10.1007/s41885-019-00044-0 · DOI
  13. [13]Yin Q Wang J. The association between consecutive days' heat wave and cardiovascular disease mortality in Beijing, China. BMC Public Health. (2017) 17: 223. 10.1186/s12889-017-4129-7 28228117 PMC5322604 · DOI
  14. [14]Goldie J Alexander L Lewis SC Sherwood S. Comparative evaluation of human heat stress indices on selected hospital admissions in Sydney, Australia. Aust Nz J Publ Heal. (2017) 41: 381– 7. 10.1111/1753-6405.12692 28712114 · DOI
  15. [16]Han J Liu S Zhang J Zhou L Fang Q Zhang J. The impact of temperature extremes on mortality: a time-series study in Jinan, China. BMJ Open. (2017) 7: e14741. 10.1136/bmjopen-2016-014741 28465307 PMC5566622 · DOI
  16. [17]Meehl GA Tebaldi C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science. (2004) 305: 994– 7. 10.1126/science.1098704 15310900 · DOI · Paper page
  17. [18]Li D Zhou T Zou L Zhang W Zhang L. Extreme high-temperature events over east Asia in 15°C and 2°C warmer futures: analysis of NCAR CESM low-warming experiments geophys. Res Lett. (2018) 45: 1541– 50. 10.1002/2017GL076753 · DOI
  18. [19]Huang X Li Y Guo Y Zheng D Qi M. Assessing urban risk to extreme heat in China. Sustainability-Basel. (2020) 12: 275. 10.3390/su12072750 · DOI
  19. [20]Fujian, Meteorological Bureau. 2020 Fujian Climate Bulletin. Available online at: http://www.weather.com.cn/fujian/zxfw/qhgb/05/3537555.shtml (accessed August 12, 2022).
  20. [21]Wang F Wang Y. Potential role of local contributions to record-breaking high-temperature event in Xiamen, China. Weather Clim Extrem. (2021) 33: 100338. 10.1016/j.wace.2021.100338 · DOI
  21. [22]Ma W Zeng W Zhou M Wang L Rutherford S Lin H. The short-term effect of heat waves on mortality and its modifiers in China: an analysis from 66 communities. Environ Int. (2015) 75: 103– 9. 10.1016/j.envint.2014.11.004 25461419 · DOI
  22. [23]Basu R Ostro BD A. Multicounty analysis identifying the populations vulnerable to mortality associated with high ambient temperature in California. Am J Epidemiol. (2008) 168: 632– 7. 10.1093/aje/kwn170 18663214 · DOI
  23. [24]Hu W Kerrie M Anthony M Shilu T. Temperature, air pollution and total mortality during summers in Sydney, 1994–2004. Int J Biometeorol. (2008) 52: 689– 96. 10.1007/s00484-008-0161-8 18506490 · DOI
  24. [25]Liu T Xu YJ Zhang YH Yan QH Song XL Xie HY. Associations between risk perception, spontaneous adaptation behavior to heat waves and heatstroke in Guangdong province, China. BMC Public Health. (2013) 13: 913. 10.1186/1471-2458-13-913 24088302 PMC3853971 · DOI
  25. [26]National Research Council. Understanding the Changing Planet: Strategic Directions for the Geographical Sciences. Washington, DC: The National Academies Press (2010). p. 41– 8.
  26. [27]Eakin H Luers AL. Assessing the vulnerability of social-environmental systems. Annu Rev Env Resour. (2006) 31: 365– 94. 10.1146/annurev.energy.30.050504.144352 · DOI
  27. [28]Shui W Chen Z Deng J Li Y Wang Q Wang W. Evaluation of urban high temperature vulnerability of coupling adaptability in Fuzhou, China (In Chinese). Acta Geographica Sinica. (2017) 72: 830– 49. 10.11821/dlxb201705006 · DOI
  28. [29]Rathi SK Chakraborty S Mishra SK Dutta A Nanda L. A heat vulnerability index: spatial patterns of exposure, sensitivity and adaptive capacity for urbanites of four cities of India. Int J Env Res Pub He. (2022) 19. 10.3390/ijerph19010283 35010542 PMC8750942 · DOI
  29. [30]Reid CE O'Neill MS Gronlund CJ Brines SJ Brown DG Diez-Roux AV. Mapping community determinants of heat vulnerability. Environ Health Persp. (2009) 117: 1730– 6. 10.1289/ehp.0900683 20049125 PMC2801183 · DOI · Paper page
  30. [31]Rinner C Patychuk D Bassil K Nasr S Gower S Campbell M. The role of maps in neighborhood-level heat vulnerability assessment for the city of Toronto. Cartogr Geogr Inf Sc. (2010) 37: 31– 44. 10.1559/152304010790588089 · DOI
  31. [32]Harlan SL Brazel AJ Prashad L Stefanov WL Larsen L. Neighborhood microclimates and vulnerability to heat stress. Soc Sci Med. (2006) 63: 2847– 63. 10.1016/j.socscimed.2006.07.030 16996668 · DOI · Paper page
  32. [34]Janssen MA Schoon ML Ke W Börner K. Scholarly networks on resilience, vulnerability and adaptation within the human dimensions of global environmental change. Glob Environ Change. (2006) 16: 240– 52. 10.1016/j.gloenvcha.2006.04.001 · DOI
  33. [35]Adger WN. Vulnerability. Glob Environ Change. (2006) 16: 268– 81. 10.1016/j.gloenvcha.2006.02.006 · DOI · Paper page
  34. [36]Timmerman P. Vulnerability, resilience and the collapse of society: a review of models and possible climatic applications. Environmental Monograph. Toronto: Institute for Environmental Studies, University of Toronto. (1981).
  35. [37]Li H Zhang P Cheng Y. Concepts and assessment methods of vulnerability (in Chinese). Progress in Geography. (2008) 27: 18– 25. 10.11820/dlkxjz.2008.02.003 · DOI
  36. [38]Turner BL Matson PA McCarthy JJ Corell RW Christensen L Eckley N. Illustrating the coupled human–environment system for vulnerability analysis: Three case studies. Proc Nat Acad Sci. (2003) 100: 8080– 5. 10.1073/pnas.1231334100 12815106 PMC166185 · DOI
  37. [39]Dow K. Exploring differences in our common future(s): the meaning of vulnerability to global environmental change. Geoforum. (1992) 23: 417– 36. 10.1016/0016-7185(92)90052-6 · DOI
  38. [41]Young OR Berkhout F Gallopin GC Janssen MA Ostrom E van der Leeuw S. The globalization of socio-ecological systems: AN agenda for scientific research. Glob Environ Change. (2006) 16: 304– 16. 10.1016/j.gloenvcha.2006.03.004 · DOI · Paper page
  39. [42]Kang S Yeom J Jung J. Urban form and natural hazards: exploring the dual aspect concept of urban forms on flood damage. Sustainability-Basel. (2021) 13: 9007. 10.3390/su13169007 · DOI
  40. [43]Huang J Su F Zhang P. Measuring social vulnerability to natural hazards in Beijing-Tianjin-Hebei Region, China. Chinese Geogr Sci. (2015) 25: 472– 85. 10.1007/s11769-015-0769-7 · DOI
  41. [44]Villa F Mcleod H. Environmental vulnerability indicators for environmental planning and decision-making: guidelines and applications. Environ Manage. (2002) 29: 335– 48. 10.1007/s00267-001-0030-2 11830764 · DOI
  42. [45]IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press. (2022).
  43. [46]Bradford K Abrahams L Hegglin M Klima K A. Heat vulnerability index and adaptation solutions for Pittsburgh, Pennsylvania. Environ Sci Technol. (2015) 49: 11303– 11. 10.1021/acs.est.5b03127 26333158 · DOI
  44. [48]Cutter SL Finch C. Temporal and spatial changes in social vulnerability to natural hazards. Proc Nat Acad Sci. (2008) 105: 2301– 6. 10.1073/pnas.0710375105 18268336 PMC2268131 · DOI
  45. [49]Zhu Q Liu T Lin H Xiao J Luo Y Zeng W. The spatial distribution of health vulnerability to heat waves in Guangdong Province, China. Glob Health Action. (2014) 7: 25051. 10.3402/gha.v7.25051 25361724 PMC4212080 · DOI
  46. [50]El-Zein A Tonmoy FN. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney. Ecol Indic. (2015) 48: 207– 17. 10.1016/j.ecolind.2014.08.012 · DOI
  47. [51]IPCC. Impacts, Adaptation & Vulnerability, IPCC Working Group-II, IPCC 4th Assessment Report. Cambridge, UK and New York, USA: Cambridge University Press (2007).
  48. [52]Wolf T McGregor G. The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes. (2013) 1: 59– 68. 10.1016/j.wace.2013.07.004 · DOI
  49. [53]Weber S Sadoff N Zell E de Sherbinin A. Policy-relevant indicators for mapping the vulnerability of urban populations to extreme heat events: a case study of Philadelphia. Appl Geogr. (2015) 63: 231– 43. 10.1016/j.apgeog.2015.07.006 · DOI
  50. [54]Nandy S Singh C Das KK Kingma NC Kushwaha SPS. Environmental vulnerability assessment of eco-development zone of Great Himalayan National Park, Himachal Pradesh, India. Ecol Indic. (2015) 57: 182– 95. 10.1016/j.ecolind.2015.04.024 · DOI
  51. [56]Cutter SL. Social Vulnerability to environmental hazards. Prog Hum Geog. (1996) 20: 529– 39. 10.1177/030913259602000407 · DOI
  52. [57]Araya-Muñoz D Metzger MJ Stuart N Wilson AMW Alvarez L. Assessing urban adaptive capacity to climate change. J Environ Manage. (2016) 183: 314– 24. 10.1016/j.jenvman.2016.08.060 27604755 · DOI
  53. [59]Johnson DP Stanforth A Lulla V Luber G. Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Appl Geogr. (2012) 35: 23– 31. 10.1016/j.apgeog.2012.04.006 · DOI
  54. [60]Kershaw SE Millward AA A. spatio-temporal index for heat vulnerability assessment. Environ Monit Assess. (2012) 184: 7329– 42. 10.1007/s10661-011-2502-z 22270590 · DOI
  55. [61]Azhar G Saha S Ganguly P Mavalankar D Madrigano J. Heat wave vulnerability mapping for India. Int J Env Res Pub He. (2017) 14: 357. 10.3390/ijerph14040357 28358338 PMC5409558 · DOI
  56. [62]Sylla MB Faye A Giorgi F Diedhiou A Kunstmann H. Projected heat stress under 15 °c and 2 °c global warming scenarios creates unprecedented discomfort for humans in West Africa. Earth's Future. (2018) 6: 1029– 44. 10.1029/2018EF000873 · DOI
  57. [63]Gao XJ Wu J Shi Y Wu J Han ZY Zhang DF. Future changes in thermal comfort conditions over China based on multi-RegCM4 simulations. Atmos Ocean Sci Lett. (2018) 11: 291– 9. 10.1080/16742834.2018.1471578 · DOI
  58. [64]Millyard A Layden JD Pyne DB Edwards AM Bloxham SR. Impairments to thermoregulation in the elderly during heat exposure events. Gerontol Geriatr Med. (2020) 6: 598956720. 10.1177/2333721420932432 32596421 PMC7297481 · DOI
  59. [65]Ge W Deng L Wang F Han J. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci Total Environ. (2021) 773: 145648. 10.1016/j.scitotenv.2021.145648 33582337 · DOI
  60. [66]Chen M Xu CG Wang RS. Key natural impacting factors of China's human population distribution. Popul Environ. (2007) 28: 187– 200. 10.1007/s11111-007-0041-x35256676 · DOI
  61. [67]Capari L Wilfing H Exner A Höflehner T Haluza D. Cooling the city? A scientometric study on urban green and blue infrastructure and climate change-induced public health effects. Sustainability-Basel. (2022) 14: 4929. 10.3390/su14094929 · DOI
  62. [68]Zhou Y Guan H Huang C Fan L Gharib S Batelaan O. Sea breeze cooling capacity and its influencing factors in a coastal city. Build Environ. (2019) 166: 106408. 10.1016/j.buildenv.2019.106408 · DOI
  63. [69]Park C Jeong S. Population exposure projections to intensified summer heat. Earth's Future. (2022) 10:e2021E−602E. 10.1029/2021EF002602 · DOI
  64. [70]Kim Y Joh S A. vulnerability study of the low-income elderly in the context of high temperature and mortality in Seoul, Korea. Sci Total Environ. (2006) 371: 82– 8. 10.1016/j.scitotenv.2006.08.014 17007909 · DOI
  65. [71]Wu C Shui W Yang H Ma M Zhu S Liu Y. Heat adaptive capacity: what causes the differences between residents of xiamen island and other areas? Frontiers in Public Health. (2022) 10: 799365. 10.3389/fpubh.2022.799365 35265572 PMC8899036 · DOI
  66. [73]Zhu L Meng J Zhu L. Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin. Ecol Indic. (2020) 117: 106545. 10.1016/j.ecolind.2020.106545 · DOI
  67. [74]Wang JF Li XH Christakos G Liao YL Zhang T Gu X. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. Int J Geogr Inf Sci. (2010) 24: 107– 27. 10.1080/13658810802443457 · DOI
  68. [75]Song Y Wang J Ge Y Xu C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. Gisci Remote Sens. (2020) 57: 593– 610. 10.1080/15481603.2020.1760434 · DOI
  69. [77]Zhang X Liu LY Chen XD Gao Y Xie S Mi J. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst Sci Data. (2021) 13: 2753– 76. 10.5194/essd-13-2753-2021 · DOI
  70. [78]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: 498– 507. 10.1016/j.healthplace.2010.12.005 21216652 · DOI · Paper page
  71. [79]Yin Y Grundstein A Mishra DR Ramaswamy L Hashemi Tonekaboni N Dowd J. DTEx: A dynamic urban thermal exposure index based on human mobility patterns. Environ Int. (2021) 155: 106573. 10.1016/j.envint.2021.106573 33930721 · DOI
  72. [80]Zuhra SS Tabinda AB Yasar A. Appraisal of the heat vulnerability index in Punjab: a case study of spatial pattern for exposure, sensitivity, and adaptive capacity in megacity Lahore, Pakistan. Int J Biometeorol. (2019) 63: 1669– 82. 10.1007/s00484-019-01784-0 31446482 · DOI · Paper page
  73. [81]Ren Y Deng L Zuo S Song X Liao Y Xu C. Quantifying the influences of various ecological factors on land surface temperature of urban forests. Environ Pollut. (2016) 216: 519– 29. 10.1016/j.envpol.2016.06.004 27321883 · DOI
  74. [82]Méndez-Lázaro P Muller-Karger FE Otis D McCarthy MJ Rodríguez E A. heat vulnerability index to improve urban public health management in San Juan, Puerto Rico. Int J Biometeorol. (2018) 62: 709– 22. 10.1007/s00484-017-1319-z 28210860 · DOI
  75. [83]Park C Jeong S Harrington LJ Lee M Zheng C. Population ageing determines changes in heat vulnerability to future warming. Environ Res Lett. (2020) 15: 114043. 10.1088/1748-9326/abbd60 · DOI
  76. [84]Williams AA Allen JG Catalano PJ Spengler JD. The role of individual and small-area social and environmental factors on heat vulnerability to mortality within and outside of the home in Boston, MA. Climate. (2020) 8: 29. 10.3390/cli8020029 35368800 PMC8974638 · DOI
  77. [85]Wang Q Zhang Y Ban J Zhu H Xu H Li T. The relationship between population heat vulnerability and urbanization levels: a county-level modeling study across China. Environ Int. (2021) 156: 106742. 10.1016/j.envint.2021.106742 34224997 · DOI

Cited by

Sorted by citations

Authors
Wu, C., Shui, W., Huang, Z., Wang, C., Wu, Y., Wu, Y., Xue, C., Huang, Y., Zhang, Y., & Zheng, D.
Affiliations
  • Fudan University (CN)
  • Fuzhou University (CN)
  • China Meteorological Administration (CN)
  • McGill University (CA)
Corresponding author
Shui, Wei
ORCID
Venue
Frontiers in Public Health
Publisher
Frontiers Media SA
Published
2022-10-20
Volume
10
Pages
989963-989963
ISSN
2296-2565 (ISSN-L)
DOI
10.3389/fpubh.2022.989963
PMC
PMC9632749
OpenAlex
W4306941897
Cited by
37
References
4
Citations by year
2026: 16 · 2025: 8 · 2024: 9 · 2023: 4
FWCI
1.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Key Research and Development Program of China (2016YFC0502905)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
Keywords
urban heat vulnerability, extreme heat, Xiamen, spatiotemporal variation, heat exposure, heat resilience, socioeconomic factors
MeSH terms
China, Cities, Hot Temperature, Humans, Climate Change

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review