Health risk appraisal of urban thermal environment and characteristic analysis on vulnerable populations
Abstract
Continuous global warming and frequent extreme high temperatures keep the urban climate health risk increasing, seriously threatening residents’ emotional health. Therefore, analysis on spatial distribution of the health risk that the urban heat island (UHI) effect imposes on emotional health as well as basic research on the characteristics of vulnerable populations need to be conducted. This study, with Tianjin city as the case, analyzed data from Landsat remote-sensing images, meteorological stations, and digital maps, explored the influence of summer UHI effect on distress (a typical negative emotion factor) and its spatiotemporal evolution, and conducted difference analysis on the age groups, genders, family state, and distress levels of vulnerable populations. The results show: (1) During the period of 1992–2020, the level and area of UHI influence on residents’ distress drastically increased–influence level elevated from level 2–4 to level 4–7, and highlevel influence areas were concentrated in six districts of central Tianjin. (2) Influence of the UHI effect on distress varied in different age groups–generally dropping with fluctuations as residents got older, especially residents aged 50–59. (3) Men experienced a W-shaped pattern in distress and were more irritable and unsteady emotionally; while women were more sensitive to distress in the beginning, but they became more placid as temperature got higher. (4) Studies on family status show that couples living together showed sound heat resistance in the face of heat stress, while middle-aged and elderly people living alone or with children were relatively weak in adjusting to high ambient temperature.
Keyword : distress, environmental management, Tianjin, urban heat island, urban thermal environment, vulnerable population
How to Cite
Huang, H., Zhao, Y., Deng, X., Yang, H., & Ren, L. (2023). Health risk appraisal of urban thermal environment and characteristic analysis on vulnerable populations. Journal of Environmental Engineering and Landscape Management, 31(1), 34–43. https://doi.org/10.3846/jeelm.2023.17635
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Huang, X. J., Wang, B., Liu, M. M., Guo, Y. H., & Li, Y. Y. (2020b). Characteristics of urban extreme heat and assessment of social vulnerability in China. Geographical Research, 39(7), 1534–1547. https://doi.org/10.11821/dlyj020190608
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Itani, M., Ghaddar, N., Ghali, K., & Laouadi, A. (2020). Bioheat modeling of elderly and young for prediction of physiological and thermal responses in heat-stressful conditions. Journal of Thermal Biology, 88, 102533. https://doi.org/10.1016/j.jtherbio.2020.102533
Kanteraki, A. E., Kyriakopoulos, G. L., Zamparas, M., Kapsalis, V. C., Makridis, S. S., & Mihalakakou, G. (2020). Investigating thermal performance of residential buildings in Marmari region, South Evia, Greece. Challenges, 11(1), 5. https://doi.org/10.3390/challe11010005
Kos, E., Pashynskyi, V., Klymenko, Y., & Pashynskyi, M. (2020). Analysis of methods for determining climate loads at a specified territory point by meteorological data. Tehnički glasnik, 14(2), 206-211. https://doi.org/10.31803/tg-20191125075805
Kovats, R. S. (2000). El Nino and human health. Bulletin of the World Health Organization, 78(9), 1127–1135.
Kyriakopoulos, G. L. (2021). Should low carbon energy technologies be envisaged in the context of sustainable energy systems? In Low carbon energy technologies in sustainable energy systems (pp. 357–389). Academic Press. https://doi.org/10.1016/B978-0-12-822897-5.00015-8
Kyriakopoulos, G., Ntanos, S., Anagnostopoulos, T., Tsotsolas, N., Salmon, I., & Ntalianis, K. (2020). Internet of Things (IoT)-enabled elderly fall verification, exploiting temporal inference models in smart homes. International Journal of Environmental Research and Public Health, 17(2), 408. https://doi.org/10.3390/ijerph17020408
Lee, J., & Parpart, J. L. (2018). Constructing gender identity through masculinity in CSR reports: The South Korean case. Business Ethics-A European Review, 27(4), 309–323. https://doi.org/10.1111/beer.12191
Li, R., Guo, Y. X., Guo, C. C., Zhang, B., Zhang, W. J., Wang, Y. D., & Wang, Z. L. (2020). Evolution of annual and spring precipitation and air temperature in Tianjin coastal zone in recent 60 years. Journal of Tianjin Normal University (Natural Science Edition), 40(5), 44–53. https://doi.org/10.19638/j.issn1671-1114.20200507
Lin, Y. L., & Hu, Z. F. (2000). Environmental psychology. China Architecture & Building Press.
Liu, J., Shu, S. C., Lin, X. G., & Shi, Z. W. (2017a). A systematic review of research on anti-disaster capability in urban disaster. Journal of Engineering Science and Technology Review, 10(5), 181–189. https://doi.org/10.25103/jestr.105.22
Liu, Y. X., Peng, J., & Wang, Y. L. (2017b). Relationship between urban heat island and landscape patterns: From city size and landscape composition to spatial configuration. Chinese Journal of Ecology, 37(23), 7769–7780. https://doi.org/10.5846/stxb201610202142
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Noelke, C., Mcgovern, M., Corsi, D. J., Jimenez, M. P., Stern, A., Wing, I. S., & Berkman, L. (2016). Increasing ambient temperature reduces emotional well-being. Environmental Research, 151, 124–129. https://doi.org/10.1016/j.envres.2016.06.045
Qi, X., Hu, W. B., Page, A., & Tong, S. L. (2015). Associations between climate variability, unemployment and suicide in Australia: A multicity study. BMC Psychiatry, 15, 114. https://doi.org/10.1186/s12888-015-0496-8
Qin, Z. H., Li, W. J., Zhang, M. H., Arnon, K., & Pedro, B. (2003). Estimating of the essential atmospheric parameters of Mono-window algorithm for land surface temperature retrieval from Landast TM6. Remote Sensing for Natural Resources, 15(2), 37–43.
Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Stankuniene, G., Streimikiene, D., & Kyriakopoulos, G. L. (2020). Systematic literature review on behavioral barriers of climate change mitigation in households. Sustainability, 12(18), 7369. https://doi.org/10.3390/su12187369
Streimikiene, D., Lekavičius, V., Baležentis, T., Kyriakopoulos, G. L., & Abrhám, J. (2020). Climate change mitigation policies targeting households and addressing energy poverty in European Union. Energies, 13(13), 3389. https://doi.org/10.3390/en13133389
Tawatsupa, B., Lim, L. Y., Kjellstrom, T., Seubsman, S. A., Sleigh, A., & Team, T. C. S. (2010). The association between overall health, psychological distress, and occupational heat stress among a large national cohort of 40,913 Thai workers. Global Health Action, 3(1), 5034. https://doi.org/10.3402/gha.v3i0.5034
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Valerie, S., Palmer, K. I., Birchfield, P. S., & Spencer, P. S. (2018). Health of vulnerable populations. Academic Medicine: Journal of the Association of American Medical Colleges, 93(9), 1263–1264. https://doi.org/10.1097/ACM.0000000000002324
Wang, P. Y., Tang, J. P., Sun, X. G., Liu, J. Y., & Juan, F. (2019). Spatiotemporal characteristics of heat waves over China in regional climate simulations within the CORDEX-EA project. Climate Dynamics, 52(1–2), 799–818. https://doi.org/10.1007/s00382-018-4167-6
Witt, C., Schubert, J. A., Jehn, M., Holzgreve, A., Liebers, U., & Endlicher, W. (2015). The effects of climate change on patients with chronic lung disease. Deutsches Arzteblatt International, 112, 51–52. https://doi.org/10.3238/arztebl.2015.0878
Wolf, T., McGregor, G., & Analitis, A. (2014). Performance assessment of a heat wave vulnerability index for Greater London, United Kingdom. Weather and Climate Extremes, 6(1), 32–46. https://doi.org/10.1175/WCAS-D-13-00014.1
Xu, Y. Z., Zheng, Y. F., Yin, J. F., & Wu, R. J. (2011). Characteristics of high temperature and heat wave in Nanjing City and their impacts on human health. Chinese Journal of Ecology, 30(12), 2815–2820. https://doi.org/10.13292/j.1000-4890.2011.0413
Yin, P., Chen, R. J., Wang, L. J., Liu C., Niu, Y., & Wang, W. D. (2018). The added effects of heatwaves on cause-specific mortality: A nationwide analysis in 272 Chinese cities. Environment International, 121, 898–905. https://doi.org/10.1016/j.envint.2018.10.016
Yu, G. L., Chen, T. T., & Zhao, F. Q. (2020). The influence of air temperature and temperature variability on mental health. Advances in Psychological Science, 28(8), 1282–1292. https://doi.org/10.3724/SP.J.1042.2020.01282
Yue, H., & Liu, Y. (2018). Comparison and analysis of land surface temperature retrieval algorithms based on Landsat 8 TIRS. Science Technology and Engineering, 18(20), 200–205. https://doi.org/10.3969/j.issn.1671-1815.2018.20.028
Zhao, A. Z., Pei, T., Cao, S., Zhang, A. B., Fan, Q. Q., & Wang, J. J. (2020). Impacts of urbanization on vegetation growth and surface urban heat island intensity in the Beijing-Tianjin-Hebei. China Environmental Science, 40(4), 1825–1833. https://doi.org/10.19674/j.cnki.issn1000-6923.2020.0206
Zheng, S., Wang, M. Z., Shang, K. Z., He, S. L., Yin, L., Li, T. S., & Wang, S. G. (2016). A case–crossover analysis of heat wave and hospital emergency department visits for cardiovascular diseases in 3 hospitals in Beijing. Journal of Hygiene Research, 45(2), 246–251. https://doi.org/10.19813/j.cnki.weishengyanjiu.2016.02.012
Applegate, W. B., Runyan, J. W., Brasfield, L., Williams, M. L., Konigsberg, C., & Fouche, C. (1981). Analysis of the 1980 heat wave in Memphis. Journal of the American Geriatrics Society, 29(8), 337–342. https://doi.org/10.1111/j.1532-5415.1981.tb01238.x
Astrom, D. O., Astrom, C., Forsberg, B., Vicedo-Cabrera, A. M., Gasparrini, A., Oudin, A., & Sundquist, K. (2020). Heat wave–related mortality in Sweden: A case-crossover study investigating effect modification by neighbourhood deprivation. Scandinavian Journal of Public Health, 48(4), 428–435. https://doi.org/10.1177/1403494818801615
Basu, R., Gavin, L., Pearson, D., Ebisu, K., & Malig, B. (2018). Examining the association between apparent temperature and mental health-related emergency room visits in California. American Journal of Epidemiology, 187(4), 726–735. https://doi.org/10.1093/aje/kwx295
Berry, H. L., Bowen, K., & Kjellstrom, T. (2010). Climate change and mental health: A causal pathways framework. International Journal of Public Health, 55(2), 123–132. https://doi.org/10.1007/s00038-009-0112-0
Blas, I. D., Gonzalez, L., & Carranza, C. (2021). Integrated assessment models (IAMS) applied to climate change and energy transition. DYNA, 96(3), 316–321. https://doi.org/10.6036/9922
Cai, W., Zhang, C., Suen, H. P., Ai, S., Bai, Y., Bao, J., Chen, B., Cheng, L., Cui, X., Dai, H., Di, Q., Dong, W., Dou, D., Fan, W., Fan, X., Gao, T., Geng, Y., Guan, D., Guo, Y., … Gong, P. (2021). The 2020 China report of the Lancet Countdown on health and climate change. The Lancet Public Health, 6(1), 64–81. https://doi.org/10.1016/S2468-2667(20)30256-5
Chen, Q., Ding, M. J., Yang, X. C., & Hu, K. J. (2017). Spatially explicit assessment of heat health risks using multi-source data: A case study of the Yangtze River delta region. Journal of Geo-information Science, 19(11), 1475–1484.
Denissen, J. J. A., Butalid, L., Penke, L., & Van Aken, M. A. G. (2008). The effects of weather on daily mood: A multilevel approach. Emotion, 8(5), 662–667. https://doi.org/10.1037/a0013497
Ding, N., Berry, H. L., Bennett, C. M., & Scott, J. G. (2016). The importance of humidity in the relationship between heat and population mental health: Evidence from Australia. PLOS ONE, 11(10), 1–15. https://doi.org/10.1371/journal.pone.0164190
Escobar, F. B., Velasco, C., Motoki, K., Byrne, D. V., & Wang, Q. J. (2021). The temperature of emotions. PLOS ONE, 16(6), e0252408. https://doi.org/10.1371/journal.pone.0252408
Feller, S. C., Castillo, E. G., Greenberg, J. M., Abascal, P., Van Horn, R., & Wells, K. B. (2018). Emotional well-being and public health: Proposal for a model national initiative. Public Health Reports, 133(2), 136–141. https://doi.org/10.1177/0033354918754540
Guo, J., Li, M. C., & Liu, D. Y. (2009). Effects of urbanization on air temperature of Tianjin in recent 40 years. Ecology and Environmental Sciences, 18(1), 29–34. https://doi.org/10.16258/j.cnki.1674-5906.2009.01.028
Hames, E., Stoler, J., Emrich, C. T., Tewary, S., & Pandya, N. (2016). A GIS approach to identifying socially and medically vulnerable older adult populations in South Florida. Gerontologist, 57(6), 1133–1141. https://doi.org/10.1093/geront/gnw106
Hoyt, M. A., Wang, W. T., Boggero, I. A., Eisenlohr-Moul, T. A., & Segerstrom, S. C. (2020). Emotional approach coping in older adults as predictor of physical and mental health. Psychology and Aging, 35(4), 591–603. https://doi.org/10.1037/pag0000463
Huang, H. C., Deng, X., Yang, H. L., Li, S. M., & Li, M. Y. (2020a). Spatial evolution of the effects of urban heat island on residents’ health. Tehnički vjesnik, 27(5), 1427–1435. https://doi.org/10.17559/TV-20200503211912
Huang, X. J., Wang, B., Liu, M. M., Guo, Y. H., & Li, Y. Y. (2020b). Characteristics of urban extreme heat and assessment of social vulnerability in China. Geographical Research, 39(7), 1534–1547. https://doi.org/10.11821/dlyj020190608
Intergovernmental Panel on Climate Change. (2013). Working group I contribution to the IPCC fifth Assessment Report (AR5). Climate change 2013: The physical science basis. http://www.climatechange2013.org/images/uploads/WGIAR5_WGI-12Doc2b_FinalDraft_All.pdf
Itani, M., Ghaddar, N., Ghali, K., & Laouadi, A. (2020). Bioheat modeling of elderly and young for prediction of physiological and thermal responses in heat-stressful conditions. Journal of Thermal Biology, 88, 102533. https://doi.org/10.1016/j.jtherbio.2020.102533
Kanteraki, A. E., Kyriakopoulos, G. L., Zamparas, M., Kapsalis, V. C., Makridis, S. S., & Mihalakakou, G. (2020). Investigating thermal performance of residential buildings in Marmari region, South Evia, Greece. Challenges, 11(1), 5. https://doi.org/10.3390/challe11010005
Kos, E., Pashynskyi, V., Klymenko, Y., & Pashynskyi, M. (2020). Analysis of methods for determining climate loads at a specified territory point by meteorological data. Tehnički glasnik, 14(2), 206-211. https://doi.org/10.31803/tg-20191125075805
Kovats, R. S. (2000). El Nino and human health. Bulletin of the World Health Organization, 78(9), 1127–1135.
Kyriakopoulos, G. L. (2021). Should low carbon energy technologies be envisaged in the context of sustainable energy systems? In Low carbon energy technologies in sustainable energy systems (pp. 357–389). Academic Press. https://doi.org/10.1016/B978-0-12-822897-5.00015-8
Kyriakopoulos, G., Ntanos, S., Anagnostopoulos, T., Tsotsolas, N., Salmon, I., & Ntalianis, K. (2020). Internet of Things (IoT)-enabled elderly fall verification, exploiting temporal inference models in smart homes. International Journal of Environmental Research and Public Health, 17(2), 408. https://doi.org/10.3390/ijerph17020408
Lee, J., & Parpart, J. L. (2018). Constructing gender identity through masculinity in CSR reports: The South Korean case. Business Ethics-A European Review, 27(4), 309–323. https://doi.org/10.1111/beer.12191
Li, R., Guo, Y. X., Guo, C. C., Zhang, B., Zhang, W. J., Wang, Y. D., & Wang, Z. L. (2020). Evolution of annual and spring precipitation and air temperature in Tianjin coastal zone in recent 60 years. Journal of Tianjin Normal University (Natural Science Edition), 40(5), 44–53. https://doi.org/10.19638/j.issn1671-1114.20200507
Lin, Y. L., & Hu, Z. F. (2000). Environmental psychology. China Architecture & Building Press.
Liu, J., Shu, S. C., Lin, X. G., & Shi, Z. W. (2017a). A systematic review of research on anti-disaster capability in urban disaster. Journal of Engineering Science and Technology Review, 10(5), 181–189. https://doi.org/10.25103/jestr.105.22
Liu, Y. X., Peng, J., & Wang, Y. L. (2017b). Relationship between urban heat island and landscape patterns: From city size and landscape composition to spatial configuration. Chinese Journal of Ecology, 37(23), 7769–7780. https://doi.org/10.5846/stxb201610202142
Mirzaei, P. A., & Haghighat, F. (2010). Approaches to study Urban Heat Island-Abilities and limitations. Building and Environment, 45(10), 2192–2201. https://doi.org/10.1016/j.buildenv.2010.04.001
Noelke, C., Mcgovern, M., Corsi, D. J., Jimenez, M. P., Stern, A., Wing, I. S., & Berkman, L. (2016). Increasing ambient temperature reduces emotional well-being. Environmental Research, 151, 124–129. https://doi.org/10.1016/j.envres.2016.06.045
Qi, X., Hu, W. B., Page, A., & Tong, S. L. (2015). Associations between climate variability, unemployment and suicide in Australia: A multicity study. BMC Psychiatry, 15, 114. https://doi.org/10.1186/s12888-015-0496-8
Qin, Z. H., Li, W. J., Zhang, M. H., Arnon, K., & Pedro, B. (2003). Estimating of the essential atmospheric parameters of Mono-window algorithm for land surface temperature retrieval from Landast TM6. Remote Sensing for Natural Resources, 15(2), 37–43.
Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Stankuniene, G., Streimikiene, D., & Kyriakopoulos, G. L. (2020). Systematic literature review on behavioral barriers of climate change mitigation in households. Sustainability, 12(18), 7369. https://doi.org/10.3390/su12187369
Streimikiene, D., Lekavičius, V., Baležentis, T., Kyriakopoulos, G. L., & Abrhám, J. (2020). Climate change mitigation policies targeting households and addressing energy poverty in European Union. Energies, 13(13), 3389. https://doi.org/10.3390/en13133389
Tawatsupa, B., Lim, L. Y., Kjellstrom, T., Seubsman, S. A., Sleigh, A., & Team, T. C. S. (2010). The association between overall health, psychological distress, and occupational heat stress among a large national cohort of 40,913 Thai workers. Global Health Action, 3(1), 5034. https://doi.org/10.3402/gha.v3i0.5034
Tianjin, B. S. (2020). Tianjin statistical yearbook (in Chinese). China Statistical Publishing House.
Valerie, S., Palmer, K. I., Birchfield, P. S., & Spencer, P. S. (2018). Health of vulnerable populations. Academic Medicine: Journal of the Association of American Medical Colleges, 93(9), 1263–1264. https://doi.org/10.1097/ACM.0000000000002324
Wang, P. Y., Tang, J. P., Sun, X. G., Liu, J. Y., & Juan, F. (2019). Spatiotemporal characteristics of heat waves over China in regional climate simulations within the CORDEX-EA project. Climate Dynamics, 52(1–2), 799–818. https://doi.org/10.1007/s00382-018-4167-6
Witt, C., Schubert, J. A., Jehn, M., Holzgreve, A., Liebers, U., & Endlicher, W. (2015). The effects of climate change on patients with chronic lung disease. Deutsches Arzteblatt International, 112, 51–52. https://doi.org/10.3238/arztebl.2015.0878
Wolf, T., McGregor, G., & Analitis, A. (2014). Performance assessment of a heat wave vulnerability index for Greater London, United Kingdom. Weather and Climate Extremes, 6(1), 32–46. https://doi.org/10.1175/WCAS-D-13-00014.1
Xu, Y. Z., Zheng, Y. F., Yin, J. F., & Wu, R. J. (2011). Characteristics of high temperature and heat wave in Nanjing City and their impacts on human health. Chinese Journal of Ecology, 30(12), 2815–2820. https://doi.org/10.13292/j.1000-4890.2011.0413
Yin, P., Chen, R. J., Wang, L. J., Liu C., Niu, Y., & Wang, W. D. (2018). The added effects of heatwaves on cause-specific mortality: A nationwide analysis in 272 Chinese cities. Environment International, 121, 898–905. https://doi.org/10.1016/j.envint.2018.10.016
Yu, G. L., Chen, T. T., & Zhao, F. Q. (2020). The influence of air temperature and temperature variability on mental health. Advances in Psychological Science, 28(8), 1282–1292. https://doi.org/10.3724/SP.J.1042.2020.01282
Yue, H., & Liu, Y. (2018). Comparison and analysis of land surface temperature retrieval algorithms based on Landsat 8 TIRS. Science Technology and Engineering, 18(20), 200–205. https://doi.org/10.3969/j.issn.1671-1815.2018.20.028
Zhao, A. Z., Pei, T., Cao, S., Zhang, A. B., Fan, Q. Q., & Wang, J. J. (2020). Impacts of urbanization on vegetation growth and surface urban heat island intensity in the Beijing-Tianjin-Hebei. China Environmental Science, 40(4), 1825–1833. https://doi.org/10.19674/j.cnki.issn1000-6923.2020.0206
Zheng, S., Wang, M. Z., Shang, K. Z., He, S. L., Yin, L., Li, T. S., & Wang, S. G. (2016). A case–crossover analysis of heat wave and hospital emergency department visits for cardiovascular diseases in 3 hospitals in Beijing. Journal of Hygiene Research, 45(2), 246–251. https://doi.org/10.19813/j.cnki.weishengyanjiu.2016.02.012