Is Sensible Heat Flux Useful for the Assessment of Thermal Vulnerability in Seoul (Korea)?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Study
2.2. Method for Assessing Thermal Vulnerability
2.3. Thermal Vulnerability and Thermal Vulnerability Index (TVI)
2.4. Sensitivity
2.5. Adaptive Capacity
2.6. Exposure
2.7. Sensible Heat Flux Estimation
2.8. Thermal Disease-Related Mortality
3. Results and Discussion
3.1. Maps of Three Indices
3.2. Validation Based on the Root Mean Square Error (RMSE)
- (a)
- The mean values of the changes in the temperature vulnerability index and the sensible heat vulnerability index were 0.25 and 0.21, respectively.
- (b)
- The differences between the maximum and minimum values of the temperature vulnerability index and sensible heat vulnerability index were 0.43 and 0.50, respectively.
- (c)
- The general trajectory drawn by the vulnerability index based on temperature is a kind of multi-nuclei circle. A trend line of sensible heat vulnerability index is similar to concentric circles but the trajectory of the sensible heat vulnerability index differs from the temperature vulnerability index.
- (d)
- The trajectory of mortality rate of the community is similar to concentric circles, like the sensible heat vulnerability index.
- (e)
- As a result, the transition trends of the community-by-Dong mortality rate and the sensible heat vulnerability index are similar.
3.3. Findings from Community’s Comprehensive Thermal Vulnerability Index
- (a)
- For grade 5, the average sensible heat flux level was approximately 324 w/m2. As a result of the good thermal environment, the first grade showed an average sensible heat flux of about 170 w/m2.
- (b)
- In order to mitigate the thermal environment from grade 5 to grade 1, green space expansion requires a sustainable energy policy by mitigating the coverage rate during land cover.
- (c)
- The following factors affect thermal vulnerability at the community level: Climate Exposure> Sensitivity> Adaptive capacity.
- (d)
- Adaptive capacity affects negatively thermal vulnerability. Two indices, sensitivity and climate exposure, are in a positive relationship. The street views of the three highest and lowest ranked communities for sensitivity and climate exposure are shown in the Appendix D.
- (e)
- The mortality trend reported for August 2015 appeared to reflect the sensible heat flux.
3.4. Spatial Attributes and Patterns Related to Sensible Heat Vulnerability
- (a)
- Seoul’s 438 “dongs” have individual placemarks based on the culture and traditions of each community. Each community expresses the thermal environment in a distinctive space, creating sensible heat mainly in the building and open space among five typical urban land cover factors. These communities (Figure 6, picture ①~⑥) had a land cover attribute that reduced sensible heat flux. According to the previous study, when the area of green surface increased by 1%, the sensible heat flux decreased by 4.9 w/m2 [24]. However, an increase in impervious surface area contributed to increased sensible heat flux (Figure 6, picture ⑦~⑫).
- (b)
- In this study, we obtained a “street view” that had a symbolic place among the communities with relative uniqueness of thermal vulnerability. By reviewing twelve pictures as shown in Figure 6, the properties of two thermal environmental types, favourable and unfavourable areas, were reflective of the land cover types [24].
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Sensible Heat Flux Estimation
Land Cover Coefficient | a1 (ratio) | a2 (h) | a3 (W/m2) |
---|---|---|---|
Green | 0.34 | 0.31 | −31 |
Building | 0.07 | 0.06 | −5 |
Impervious | 0.83 | 0.4 | −54.2 |
Water | 0.5 | 0.21 | −39.1 |
Road | 0.61 | 0.41 | −27.7 |
Appendix B. Data
Classification | Input Data | Source |
---|---|---|
Meteorological data for heat flux distribution | -Air temperature, relative humidity, cloud cover, saturated water vapour pressure | -Korea Meteorological Administration (38 stations) -SKTech X (249 stations) |
Spatial attributes | -Subdivided land cover map (green spaces, wetlands, impervious surfaces) -shp file of Seoul administrative district – building .shp file -shp file depicting the widths of roads -shp file depicting the width of roads | -Ministry of Environment, -Statistical Geographic Information Service (SGIS), -Seoul Information Communication Plaza |
A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | 1.00 | ||||||||||||||||||||||
B | 0.15 | 1.00 | |||||||||||||||||||||
C | 0.23 | 0.09 | 1.00 | ||||||||||||||||||||
D | 0.19 | 0.57 | 0.07 | 1.00 | |||||||||||||||||||
E | 0.20 | 0.57 | 0.07 | 0.99 | 1.00 | ||||||||||||||||||
F | 0.07 | 0.18 | 0.71 | 0.06 | 0.06 | 1.00 | |||||||||||||||||
G | 0.16 | 0.78 | 0.01 | 0.69 | 0.69 | 0.14 | 1.00 | ||||||||||||||||
H | 0.01 | 0.19 | 0.03 | 0.20 | 0.21 | 0.02 | 0.27 | 1.00 | |||||||||||||||
I | 0.13 | 0.35 | 0.01 | 0.55 | 0.55 | 0.14 | 0.70 | 0.11 | 1.00 | ||||||||||||||
J | 0.08 | 0.02 | 0.03 | 0.07 | 0.07 | 0.00 | 0.04 | 0.06 | 0.11 | 1.00 | |||||||||||||
K | 0.07 | 0.07 | 0.01 | 0.14 | 0.13 | 0.02 | 0.27 | 0.05 | 0.42 | 0.01 | 1.00 | ||||||||||||
L | 0.19 | 0.11 | 0.12 | 0.11 | 0.10 | 0.08 | 0.04 | −0.02 | 0.17 | 0.07 | 0.10 | 1.00 | |||||||||||
M | 0.04 | 0.04 | 0.03 | 0.19 | 0.19 | 0.07 | 0.00 | 0.04 | 0.07 | 0.12 | 0.24 | 0.04 | 1.00 | ||||||||||
N | 0.38 | 0.05 | 0.03 | 0.03 | 0.03 | 0.03 | 0.04 | 0.04 | 0.20 | 0.12 | 0.13 | 0.42 | 0.00 | 1.00 | |||||||||
O | 0.43 | 0.11 | 0.11 | 0.09 | 0.08 | 0.10 | 0.02 | 0.02 | 0.28 | 0.15 | 0.19 | 0.73 | −0.04 | 0.66 | 1.00 | ||||||||
P | 0.10 | 0.35 | 0.06 | 0.35 | 0.36 | 0.29 | 0.42 | 0.12 | 0.29 | 0.00 | 0.05 | 0.06 | 0.33 | −0.02 | 0.05 | 1.00 | |||||||
Q | 0.11 | 0.19 | 0.04 | 0.31 | 0.32 | 0.01 | 0.16 | −0.04 | 0.02 | 0.05 | 0.50 | 0.01 | 0.63 | −0.02 | 0.03 | 0.15 | 1.00 | ||||||
R | 0.58 | 0.04 | 0.03 | 0.09 | 0.09 | 0.05 | 0.08 | 0.06 | 0.14 | 0.16 | 0.05 | 0.24 | −0.03 | −0.48 | 0.70 | 0.02 | 0.04 | 1.00 | |||||
S | 0.50 | 0.12 | 0.05 | −0.03 | 0.03 | 0.05 | 0.00 | 0.04 | 0.25 | 0.15 | 0.17 | 0.73 | −0.03 | 0.64 | 0.95 | 0.06 | 0.04 | 0.72 | 1.00 | ||||
T | 0.30 | 0.12 | 0.19 | 0.04 | 0.04 | 0.06 | 0.06 | −0.02 | 0.09 | 0.08 | 0.10 | 0.64 | 0.03 | −0.04 | 0.19 | 0.06 | 0.05 | 0.39 | 0.15 | 1.00 | |||
U | 0.26 | 0.15 | 0.04 | −0.10 | 0.10 | 0.08 | 0.08 | −0.10 | 0.07 | 0.15 | 0.02 | 0.23 | 0.03 | −0.40 | 0.35 | 0.01 | 0.00 | 0.02 | 0.38 | 0.06 | 1.00 | ||
V | 0.33 | 0.02 | 0.08 | −0.15 | 0.15 | 0.09 | 0.07 | −0.08 | 0.13 | 0.14 | 0.01 | 0.49 | 0.08 | 0.46 | 0.69 | 0.08 | 0.13 | 0.73 | 0.65 | 0.16 | 0.01 | 1.00 | |
W | 0.62 | 0.45 | 0.25 | 0.35 | 0.35 | 0.36 | 0.41 | 0.06 | 0.19 | 0.33 | 0.06 | 0.35 | −0.25 | 0.46 | 0.67 | 0.17 | 0.28 | 0.68 | 0.69 | 0.17 | 0.32 | 0.54 | 1 |
Appendix C. Hierarchical Clustering Analysis
Appendix D. Street Views
References
- Friedrich, M.J. Depression is the leading cause of disability around the world. J. Am. Med. Assoc. 2017, 317, 1517. [Google Scholar] [CrossRef] [PubMed]
- Joel, E. Cohen Human Population: The Next Half Century. Science (80-.) 2003, 302, 1172–1175. [Google Scholar]
- Chen, H.; Ooka, R.; Harayama, K.; Kato, S.; Li, X. Study on outdoor thermal environment of apartment block in Shenzhen, China with coupled simulation of convection, radiation and conduction. Energy Build. 2004, 36, 1247–1258. [Google Scholar] [CrossRef]
- Niyogi, D.; Chen, F.; Yang, L.; Ni, G.; Tewari, M.; Tian, F.; Aliaga, D. Contrasting impacts of urban forms on the future thermal environment: Example of Beijing metropolitan area. Environ. Res. Lett. 2016, 11, 034018. [Google Scholar]
- Baker, L.A.; Brazel, A.J.; Selover, N.; Martin, C.; McIntyre, N.; Steiner, F.R.; Nelson, A.; Musacchio, L. Urbanization and warming of Phoenix (Arizona, USA): Impacts, feedbacks and mitigation. Urban Ecosyst. 2002, 6, 183–203. [Google Scholar] [CrossRef]
- Tam, B.Y.; Gough, W.A.; Mohsin, T. The impact of urbanization and the urban heat island effect on day to day temperature variation. Urban Clim. 2015, 12, 1–10. [Google Scholar] [CrossRef]
- Kleerekoper, L.; Van Esch, M.; Salcedo, T.B. How to make a city climate-proof, addressing the urban heat island effect. Resour. Conserv. Recycl. 2012, 64, 30–38. [Google Scholar] [CrossRef]
- Coutts, A.M.; Beringer, J.; Tapper, N.J. Impact of increasing urban density on local climate: Spatial and temporal variations in the surface energy balance in Melbourne, Australia. J. Appl. Meteorol. Climatol. 2007, 46, 477–493. [Google Scholar] [CrossRef]
- Gasparrini, A.; Armstrong, B. The impact of heat waves on mortality. Epidemiology 2011, 22, 68–73. [Google Scholar] [CrossRef] [Green Version]
- Johnson, D.P.; Wilson, J.S. The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia. Appl. Geogr. 2009, 29, 419–434. [Google Scholar] [CrossRef]
- Ebi, K.L.; Ogden, N.H.; Semenza, J.C.; Woodward, A. Detecting and Attributing health burdens to climate change. Environ. Health Perspect. 2017, 085004, 1–8. [Google Scholar] [CrossRef] [PubMed]
- Chen, R.; Lu, R. Dry Tropical Nights and Wet Extreme Heat in Beijing: Atypical Configurations between High Temperature and Humidity. Mon. Weather Rev. 2014, 142, 1792–1802. [Google Scholar] [CrossRef]
- Park, W.-S.; Suh, M.-S. Characteristics and trends of tropical night occurrence in South Korea for recent 50 years (1958-2007). Atmosphere (Basel) 2011, 21, 361–371. [Google Scholar]
- Kusaka, H.; Kimura, F. Thermal Effects of Urban Canyon Structure on the Nocturnal Heat Island: Numerical Experiment Using a Mesoscale Model Coupled with an Urban Canopy Model. J. Appl. Meteorol. 2004, 43, 1899–1910. [Google Scholar] [CrossRef]
- Kondo, H.; Kikegawa, Y. Temperature Variation in the Urban Canopy with Anthropogenic Energy Use. In Air Quality; Rao, G.V., Raman, S., Singh, M.P., Eds.; Springer: Basel, Switzerland, 2003; pp. 317–324. ISBN 9783764370053. [Google Scholar]
- Bao, J.; Li, X.; Yu, C. The construction and validation of the heat vulnerability index, a review. Int. J. Environ. Res. Public Health 2015, 12, 7220–7234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- El-Zein, A.; Tonmoy, F.N. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney. Ecol. Indic. 2015, 48, 207–217. [Google Scholar] [CrossRef] [Green Version]
- Chen, K.; Bi, J.; Chen, J.; Chen, X.; Huang, L.; Zhou, L. Influence of heat wave definitions to the added effect of heat waves on daily mortality in Nanjing, China. Sci. Total Environ. 2015, 506–507, 18–25. [Google Scholar] [CrossRef]
- Nayak, S.G.; Shrestha, S.; Kinney, P.L.; Ross, Z.; Sheridan, S.C.; Pantea, C.I.; Hsu, W.H.; Muscatiello, N.; Hwang, S.A. Development of a heat vulnerability index for New York State. Public Health 2018, 161, 127–137. [Google Scholar] [CrossRef]
- Ebi, K.L.; Semenza, J.C. Community-Based Adaptation to the Health Impacts of Climate Change. Am. J. Prev. Med. 2008, 35, 501–507. [Google Scholar] [CrossRef]
- Wolf, T.; McGregor, G. The development of a heat wave vulnerability index for London, United Kingdom. Weather Clim. Extrem. 2013, 1, 59–68. [Google Scholar] [CrossRef] [Green Version]
- Birkmann, J.; Böhm, H.R.; Buchholz, F.; Büscher, D.; Daschkeit, A.; Ebert, S.; Fleischhauer, M.; Frommer, B.; Köhler, S.; Kufeld, W.; et al. Glossars Klimawandel und Raumentwicklung 2., überarbeitete Fassung, 2nd ed.; Müller, P., Ed.; Akademie für Raumforschung und Landesplanung: Hannover, Germany, 2013; ISBN 9783888387289. [Google Scholar]
- Sheridan, S.C.; Allen, M.J. Temporal trends in human vulnerability to excessive heat. Environ. Res. Lett. 2018, 13, 043001. [Google Scholar] [CrossRef]
- Kwon, Y.J.; Lee, D.K.; Lee, K. Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City. Energies 2019, 12, 2320. [Google Scholar] [CrossRef] [Green Version]
- Zhuang, Q.; Wu, B.; Yan, N.; Zhu, W.; Xing, Q. A method for sensible heat flux model parameterization based on radiometric surface temperature and environmental factors without involving the parameter KB −1. Int. J. Appl. Earth Obs. Geoinf. 2016, 47, 50–59. [Google Scholar] [CrossRef]
- Voogt, J.A.; Grimmond, C.S.B. Modeling Surface Sensible Heat Flux Using Surface Radiative Temperatures in a Simple Urban Area. J. Appl. Meteorol. 2010, 39, 1679–1699. [Google Scholar] [CrossRef]
- Heaviside, C.; Macintyre, H.; Vardoulakis, S. The Urban Heat Island: Implications for Health in a Changing Environment. Curr. Environ. Heal. reports 2017, 4, 296–305. [Google Scholar] [CrossRef]
- Coccolo, S.; Kämpf, J.; Scartezzini, J.L.; Pearlmutter, D. Outdoor human comfort and thermal stress: A comprehensive review on models and standards. Urban Clim. 2016, 18, 33–57. [Google Scholar] [CrossRef]
- Hwang, M.K.; Bang, J.H.; Kim, S.; Kim, Y.K.; Oh, I. Estimation of thermal comfort felt by human exposed to extreme heat wave in a complex urban area using a WRF-MENEX model. Int. J. Biometeorol. 2019, 927–938. [Google Scholar] [CrossRef]
- Bernhard, M.C.; Kent, S.T.; Sloan, M.E.; Evans, M.B.; McClure, L.A.; Gohlke, J.M. Measuring personal heat exposure in an urban and rural environment. Environ. Res. 2015, 137, 410–418. [Google Scholar] [CrossRef] [Green Version]
- Zaitchik, B.F.; Vanos, J.K.; Calkins, M.M.; Jagger, M.A.; Uejio, C.K.; Gohlke, J.M.; Middel, A.; Hess, J.J.; Spector, J.T.; Kintziger, K.W.; et al. Opportunities and Challenges for Personal Heat Exposure Research. Environ. Health Perspect. 2017, 125, 085001. [Google Scholar]
- Kim, D.W.; Deo, R.C.; Lee, J.S.; Yeom, J.M. Mapping heatwave vulnerability in Korea. Nat. Hazards 2017, 89, 35–55. [Google Scholar] [CrossRef]
- Mushore, T.D.; Mutanga, O.; Odindi, J.; Dube, T. Determining extreme heat vulnerability of Harare Metropolitan City using multispectral remote sensing and socio-economic data. J. Spat. Sci. 2018, 63, 173–191. [Google Scholar] [CrossRef]
- Van Hoof, J.; Schellen, L.; Soebarto, V.; Wong, J.K.W.; Kazak, J.K. Ten questions concerning thermal comfort and ageing. Build. Environ. 2017, 120, 123–133. [Google Scholar] [CrossRef]
- Xu, Z.; Hu, W.; Su, H.; Turner, L.R.; Ye, X.; Wang, J.; Tong, S. Extreme temperatures and paediatric emergency department admissions. J. Epidemiol. Community Health 2014, 68, 304–311. [Google Scholar] [CrossRef] [Green Version]
- Berry, P.; Clarke, K.L.; Pajot, M.; Hutton, D. Risk Perception, Health Communication, and Adaptation to the Health Impacts of Climate Change in Canada. In Climate Change Adaptation in Developmed Nations; Ford, J.D., Berrang-Ford, L., Eds.; Springer: Dordrecht, The Netherlands, 2011; Volume 42, pp. 205–219. ISBN 978-94-007-0566-1. [Google Scholar]
- Sun, S.; Xu, X.; Lao, Z.; Liu, W.; Li, Z.; Higueras García, E.; He, L.; Zhu, J. Evaluating the impact of urban green space and landscape design parameters on thermal comfort in hot summer by numerical simulation. Build. Environ. 2017, 123, 277–288. [Google Scholar] [CrossRef]
- Tan, Z.; Lau, K.K.L.; Ng, E. Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment. Energy Build. 2016, 114, 265–274. [Google Scholar] [CrossRef]
- Salata, F.; Golasi, I.; Petitti, D.; de Lieto Vollaro, E.; Coppi, M.; de Lieto Vollaro, A. Relating microclimate, human thermal comfort and health during heat waves: An analysis of heat island mitigation strategies through a case study in an urban outdoor environment. Sustain. Cities Soc. 2017, 30, 79–96. [Google Scholar] [CrossRef]
- Coutts, A.M.; White, E.C.; Tapper, N.J.; Beringer, J.; Livesley, S.J. Temperature and human thermal comfort effects of street trees across three contrasting street canyon environments. Theor. Appl. Climatol. 2015, 2016, 55–68. [Google Scholar]
- Guo, Y.; Gasparrini, A.; Armstrong, B.G.; Tawatsupa, B.; Tobias, A.; Lavigne, E.; Coelho, M.D.S.Z.S.; Pan, X.; Kim, H.; Hashizume, M.; et al. Heat Wave and Mortality: A Multicountry, Multicommunity Study. Environ. Health Perspect. 2017, 125, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Gasparrini, A.; Guo, Y.; Hashizume, M. Mortality risk attributable to high and low ambient temperature: A multicountry observational study. Environ. Risques et Sante 2015, 386, 369–375. [Google Scholar] [CrossRef]
- Honda, Y.; Kondo, M.; McGregor, G.; Kim, H.; Guo, Y.L.; Hijioka, Y.; Yoshikawa, M.; Oka, K.; Takano, S.; Hales, S.; et al. Heat-related mortality risk model for climate change impact projection. Environ. Health Prev. Med. 2014, 19, 56–63. [Google Scholar] [CrossRef]
- Guo, Y.; Gasparrini, A.; Armstrong, B.; Li, S.; Tawatsupa, B.; Tobias, B.; Eric Lavigne, E. Micheline de Sousa Zanotti Stagliorio Coelho8, Michela Leone9, Xiaochuan Pan10, Shilu Tong11, Linwei Tian12, Ho Kim13, Masahiro Hashizume14, Yasushi Honda15, Yue-Liang Leon Guo16, Chang-Fu Wu17, Kornwipa Punnasiri4, Seung-Muk Yi13, Paola Michelozzi9, Pa, and G.W. Global variation in the effects of ambient temperature on mortality: A systematic evaluation. Epidemiology 2014, 25, 781–789. [Google Scholar] [PubMed] [Green Version]
- Bi, P.; Parton, K.A. Effect of climate change on Australian rural and remote regions: What do we know and what do we need to know? Aust. J. Rural Health 2008, 16, 2–4. [Google Scholar] [CrossRef] [PubMed]
- Jenerette, G.D.; Harlan, S.L.; Buyantuev, A.; Stefanov, W.L.; Declet-Barreto, J.; Ruddell, B.L.; Myint, S.W.; Kaplan, S.; Li, X. Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landsc. Ecol. 2016, 31, 745–760. [Google Scholar] [CrossRef]
- Kuang, W.; Liu, Y.; Dou, Y.; Chi, W.; Chen, G.; Gao, C.; Yang, T.; Liu, J.; Zhang, R. What are hot and what are not in an urban landscape: Quantifying and explaining the land surface temperature pattern in Beijing, China. Landsc. Ecol. 2014, 30, 357–373. [Google Scholar] [CrossRef]
- Benas, N.; Chrysoulakis, N.; Cartalis, C. Trends of urban surface temperature and heat island characteristics in the Mediterranean. Theor. Appl. Climatol. 2017, 130, 807–816. [Google Scholar] [CrossRef]
- Feigenwinter, C.; Parlow, E.; Vogt, R.; Schmutz, M.; Chrysoulakis, N.; Lindberg, F.; Marconcini, M.; Del Frate, F. Spatial distribution of sensible and latent heat flux in the URBANFLUXES case study city Basel (Switzerland). 2017 Jt. Urban Remote Sens. Event, JURSE 2017 2017, 1–4. [Google Scholar] [CrossRef]
- Jiang, J.; Jin, Y.; Bao, T.; Ou, X. Sensible heat discharging from pavements with varying thermophysical properties. Sustain. Cities Soc. 2019, 45, 431–438. [Google Scholar] [CrossRef]
- Ströhle, S.; Haselbacher, A.; Jovanovic, Z.R.; Steinfeld, A. Upgrading sensible-heat storage with a thermochemical storage section operated at variable pressure: An effective way toward active control of the heat-transfer fluid outflow temperature. Appl. Energy 2017, 196, 51–61. [Google Scholar] [CrossRef]
- Rocklöv, J.; Ebi, K.; Forsberg, B. Mortality related to temperature and persistent extreme temperatures: A study of cause-specific and age-stratified mortality. Occup. Environ. Med. 2011, 68, 531–536. [Google Scholar] [CrossRef] [Green Version]
- Tomlinson, C.J.; Chapman, L.; Thornes, J.E.; Baker, C.J. Including the urban heat island in spatial heat health risk assessment strategies: A case study for Birmingham, UK. Int. J. Health Geogr. 2011, 10, 42. [Google Scholar] [CrossRef] [Green Version]
- Stone, B.; Hess, J.J.; Frumkin, H. Urban form and extreme heat events: Are sprawling cities more vulnerable to climate change than compact cities? Environ. Health Perspect. 2010, 118, 1425–1428. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huang, C.; Barnett, A.G.; Xu, Z.; Chu, C.; Wang, X.; Turner, L.R.; Tong, S. Managing the Health Effects of Temperature in Response to Climate Change. EHP 2013, 121, 415–419. [Google Scholar] [CrossRef] [Green Version]
- Harlan, S.L.; Declet-barreto, J.H.; Stefanov, W.L.; Petitti, D.B. Neighborhood Effects on Heat Deaths: Social and Environmental Predictors of Vulnerability in Maricopa County, Arizona. Environ. Health Perspect. 2013, 121, 197–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dong, W.; Liu, Z.; Zhang, L.; Tang, Q.; Liao, H.; Li, X. Assessing heat health risk for sustainability in Beijing’s urban heat island. Sustainability 2014, 6, 7334–7357. [Google Scholar] [CrossRef] [Green Version]
- Sera, F.; Armstrong, B.; Tobias, A.; Vicedo-Cabrera, A.M.; Åström, C.; Bell, M.L.; Chen, B.-Y.; de Sousa Zanotti Stagliorio Coelho, M.; Matus Correa, P.; Cruz, J.C.; et al. How urban characteristics affect vulnerability to heat and cold: A multi-country analysis. Int. J. Epidemiol. 2019, 48, 1101–1112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Walsh, D.; McCartney, G.; Collins, C.; Taulbut, M.; Batty, G.D. History, politics and vulnerability: Explaining excess mortality in Scotland and Glasgow. Public Health 2017, 151, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Chen, K.; Zhou, L.; Chen, X.; Ma, Z.; Liu, Y.; Huang, L.; Bi, J.; Kinney, P.L. Urbanization level and vulnerability to heat-related mortality in Jiangsu Province, China. Environ. Health Perspect. 2016, 124, 1863–1869. [Google Scholar] [CrossRef]
- Fernandez Milan, B.; Creutzig, F. Reducing urban heat wave risk in the 21st century. Curr. Opin. Environ. Sustain. 2015, 14, 221–231. [Google Scholar] [CrossRef] [Green Version]
- Carter, T.R.; Fronzek, S.; Inkinen, A.; Lahtinen, I.; Lahtinen, M.; Mela, H.; O’Brien, K.L.; Rosentrater, L.D.; Ruuhela, R.; Simonsson, L.; et al. Characterising vulnerability of the elderly to climate change in the Nordic region. Reg. Environ. Chang. 2016, 16, 43–58. [Google Scholar] [CrossRef] [Green Version]
- Basagaña, X.; Sartini, C.; Barrera-gómez, J.; Dadvand, P.; Ostro, B.; Sunyer, J.; Medina-ramón, M.; Basagana, X.; Sartini, C.; Barrera-gomeza, J. Heat Waves and Cause-specific Mortality at all ages. Epidemiology 2011, 22, 765–772. [Google Scholar] [CrossRef]
- Basu, R. High ambient temperature and mortality: A review of epidemiologic studies from 2001 to 2008. Environ. Health 2009, 13, 40. [Google Scholar] [CrossRef] [Green Version]
- Leal Filho, W.; Echevarria Icaza, L.; Neht, A.; Klavins, M.; Morgan, E.A. Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. J. Clean. Prod. 2018, 171, 1140–1149. [Google Scholar] [CrossRef] [Green Version]
- Benmarhnia, T.; Kaufman, J.S. When evidence of heat-related vulnerability depends on the contrast measure. Int. J. Biometeorol. 2017, 61, 391–393. [Google Scholar] [CrossRef] [PubMed]
- Theeuwes, N.E.; Steeneveld, G.J.; Ronda, R.J.; Rotach, M.W.; Holtslag, A.A.M. Cool city mornings by urban heat. Environ. Res. Lett. 2015, 10, 114022. [Google Scholar] [CrossRef]
- Mayer, H.; Höppe, P. Thermal comfort of man in different urban environments. Theor. Appl. Climatol. 1987, 38, 43–49. [Google Scholar] [CrossRef]
- Johansson, E.; Thorsson, S.; Emmanuel, R.; Krüger, E. Instruments and methods in outdoor thermal comfort studies—The need for standardization. Urban Clim. 2014, 10, 346–366. [Google Scholar] [CrossRef] [Green Version]
- Schweiker, M.; Wagner, A. A framework for an adaptive thermal heat balance model (ATHB). Build. Environ. 2015, 94, 252–262. [Google Scholar] [CrossRef]
- Kim, I.; Lee, J.; Yang, J.; Jang, N.; Ko, J.; Jeong, B. 2030 Seoul Master Plan; Seoul Metropolitan Government: Seoul, Korea, 2013; Volume 1. [Google Scholar]
- Dong, W.; Zeng, Q.; Ma, Y.; Li, G.; Pan, X. Impact of heatwave definitions on the added effect of heatwaves on cardiovascular mortality in Beijing, China. Int. J. Environ. Res. Public Health 2016, 13, 933. [Google Scholar] [CrossRef] [Green Version]
- Shafiei Shiva, J.; Chandler, D.G.; Kunkel, K.E. Localized Changes in Heat Wave Properties Across the United States. Earth’s Futur. 2019, 7, 300–319. [Google Scholar] [CrossRef] [Green Version]
- Heatwave definition in Korea; National Disaster Management Research Institute. Available online: http://www.ndmi.go.kr/promote/knowledge/nature.jsp?link=9 (accessed on 4 May 2019).
- Hyun, C.-S.; Roh, S.-H.; Kim, D.-H.; Son, S.-Y.; Baek, Y.J.; Kim, K.R.; Lee, J.-Y. Comparison of the Perception of Summer Heat Wave and Thermoregulatory Behavior between Adult Males Living in Seoul and in Daegu. Korean J. Community Living Sci. 2018, 29, 17–32. [Google Scholar] [CrossRef]
- Gill, S.E.; Handley, J.F.; Ennos, A.R.; Pauleit, S. Adapting cities for climate change: The role of the green infrastructure. Built Environ. 2007, 33, 115–133. [Google Scholar] [CrossRef] [Green Version]
- Kim, H.M.; Han, S.S. Seoul. Cities 2012, 29, 142–154. [Google Scholar] [CrossRef]
- Christenson, M.; Geiger, S.D.; Phillips, J.; Anderson, B.; Losurdo, G.; Anderson, H.A. Heat vulnerability index mapping for milwaukee and Wisconsin. J. Public Heal. Manag. Pract. 2017, 23, 396–403. [Google Scholar] [CrossRef] [PubMed]
- Brooks, N. Vulnerability, risk and adaptation: A conceptual framework. Tyndall Cent. Clim. Chang. Res. Work. Pap. 2003, 38, 1–16. [Google Scholar]
- Manangan, A.P.; Uejio, C.K.; Saha, S.; Schramm, P.J.; Marinucci, G.D.; Hess, J.J.; Luber, G. Assessing Health Vulnerability to Climate Change. Cent. Dis. Control Prev. 2015, 1, 1–23. [Google Scholar]
- Jeong, C.; Korea, M.L.P. Adaptation Policy to Climate Change; Head Office: Boryeongbuk-ro, Korea, 2017. [Google Scholar]
- Inostroza, L.; Palme, M.; De La Barrera, F. A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for Santiago de Chile. PLoS ONE 2016, 11, 1–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lundgren, K.; Kuklane, K.; Gao, C.; Holmer, I. Effects of Heat Stress on Working Populations when Facing Climate Change. Ind. Health 2013, 51, 3–15. [Google Scholar] [CrossRef] [Green Version]
- Glick, P.; Stein, B.A.; Edelson, N.A. Scanning the Conservation Horizon: A Guide to Climate Change Vulnerability Assessment. Available online: https://repository.library.noaa.gov/view/noaa/10088/noaa_10088_DS1.pdf (accessed on 4 January 2020).
- Kumar, P.; Geneletti, D.; Nagendra, H. Spatial assessment of climate change vulnerability at city scale: A study in Bangalore, India. Land Use Policy 2016, 58, 514–532. [Google Scholar] [CrossRef]
- Saaty, T.L.; Vargas, L.G. Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. In The Seven Pillars of the Analytic Hierarchy Process; Springer: Boston, MA, USA, 2012; Volume 175, pp. 23–40. ISBN 9781461435976. [Google Scholar]
- ESRI Data classification methods in GIS. Available online: https://pro.arcgis.com/en/pro-app/help/mapping/layer-properties/data-classification-methods.htm (accessed on 31 December 2019).
- Geographical Atlas of Seoul 2013. Available online: http://data.si.re.kr/map-seoul-2013 (accessed on 4 January 2020).
- Offerle, B.; Grimmond, C.S.B.; Fortuniak, K. Heat storage and anthropogenic heat flux in relation to the energy balance of a central European city centre. Int. J. Climatol. 2005, 25, 1405–1419. [Google Scholar] [CrossRef]
- Holtslag, A.A.M.; Van Ulden, A.P. A Simple Scheme for Daytime Estimates of the Surface Fluxes from Routine Weather Data. J. Clim. Appl. Meteorol. 1983, 22, 517–529. [Google Scholar] [CrossRef]
- Pigeon, G.; Legain, D.; Durand, P.; Masson, V. Anthropogenic heat release in an old European agglomeration_Toulouse, France. R. Meteorol. Soc. 2007, 27, 1969–1981. [Google Scholar]
- Ng, Y. A Study of Urban Heat Island using “Local Climate Zones”—The Case of Singapore. Br. J. Environ. Clim. Chang. 2015, 5, 116–133. [Google Scholar] [CrossRef] [PubMed]
- Kwon, Y.J.; Lee, D.K. Thermal Comfort and Longwave Radiation over Time in Urban Residential Complexes. Sustainability 2019, 11, 2251. [Google Scholar] [CrossRef] [Green Version]
- Hansen, A.; Bi, P. Climate change adaptation: No one size fits all. Lancet Planet. Heal. 2017, 1, e353–e354. [Google Scholar] [CrossRef] [Green Version]
- Kraemer, H.C.; Kupfer, D.J. Size of Treatment Effects and Their Importance to Clinical Research and Practice. Biol. Psychiatry 2006, 59, 990–996. [Google Scholar] [CrossRef]
- Gavilán, P. Comparing Net Radiation Measurements Using Domeless and Domed Net Radiometers: Impact on ETo Estimations. J. Irrig. Drain. Eng. 2016, 142, 04016060. [Google Scholar] [CrossRef]
- Grimmond, C.S.B.; Cleugh, H.A.; Oke, T.R. an Objective Urban Heat Storage Model and Its. Atmos. Environ. 1991, 25, 311–326. [Google Scholar] [CrossRef]
- Roberts, S.M.; Oke, T.R.; Grimmond, C.S.B.; Voogt, J.A. Comparison of four methods to estimate urban heat storage. J. Appl. Meteorol. Climatol. 2006, 45, 1766–1781. [Google Scholar] [CrossRef]
- Grimmond, C.S.B.; Roth, M.; Oke, T.R.; Au, Y.C.; Best, M.; Betts, R.; Carmichael, G.; Cleugh, H.; Dabberdt, W.; Emmanuel, R.; et al. Climate and more sustainable cities: Climate information for improved planning and management of cities (Producers/Capabilities Perspective). Procedia Environ. Sci. 2010, 1, 247–274. [Google Scholar] [CrossRef] [Green Version]
Level | Criteria | Range |
---|---|---|
1 | Seriously vulnerable to heat | 0.00–0.08 |
2 | Vulnerable to heat | 0.08–0.32 |
3 | Mild | 0.32–0.49 |
4 | Not vulnerable to heat | 0.49–0.76 |
5 | Seriously not vulnerable to heat | 0.76–1.00 |
Index | Variable | Data Description | Year | Data Source |
---|---|---|---|---|
Sensitivity | Population density | Inhabitants per area * | 2015 | Seoul open dataset ** |
Older adults (over 65) living alone | Inhabitants per area * above 65 years old | 2015 | Seoul open dataset **, Dept. of welfare for seniors, Seoul | |
Population of under 5 | Inhabitants per area * under 5 years old | 2015 | Seoul open dataset ** | |
Heat-related illness | Inhabitants per area * with heat-related illness | 2015 | Seoul open dataset ** | |
Population below poverty line (BPL) | National Basic Livelihood Act recipients per area * | 2015 | Seoul open dataset ** | |
Heat-related death | Inhabitants per area * with heat-related death | 2015 | Seoul open dataset ** | |
Adaptive capacity | Hospitals | Number of medical institutes | 2015 | Seoul open dataset ** |
Income | Monthly income | 2015 | KOSIS **** | |
Medical insurance budget | Annual budget | 2015 | Seoul open dataset ** | |
Exposure | Daytime air temperature | Average daytime *** temperature | 2015 | SKTech X (249 stations) |
Daytime sensible heat flux | Average daytime *** sensible heat flux | 2015 | Estimation | |
Spatial attributes (see Figure A1) | Subdivided land cover classification map (green, wetland, impervious surface), building shp. File, widths of roads shp. File | 2015 | Ministry of Environment, Statistical Geographic Information Service, Seoul Information Communication Plaza |
Heat-Related Disease | RR | |
---|---|---|
Respiratory | Pneumonia | 1.2 |
Chronic lower resp. dis. | 1.2 | |
Other resp. dis. | 1.3 | |
Cardiovascular | Heart: Ischaemic | 1.2 |
Cerebrovascular | 1.2 | |
Atherosclerosis | 1.4 | |
Hypertensive | 1.3 | |
Digestive system | Ulcers | 1.0 |
Liver diseases | 1.2 | |
RR: Basagaña et al. [63] |
Correlation | RMSE ** | Average Error | |
---|---|---|---|
Max.* Temperature (˚C) | 0.303 | 0.229241081 | −0.20112 |
Max.* Sensible Heat flux (W/m2) | 0.734 | 0.184579627 | −0.17102 |
Community | SHVI * (Rank) | Sensible Heat Flux(W/m2) | Sensitivity | Adaptive Capacity | Exposure | Mortality ** (Total (n)/Mortality Rate (ratio) | Attributes | ||
---|---|---|---|---|---|---|---|---|---|
Mean | Max | Min | |||||||
Wolgea 3 | 1 | 207.60 | 511.50 | 133.27 | 0.93 | 0.13 | 0.79 | 9 (0.08) | Old town |
Oryu | 2 | 200.30 | 536.79 | 105.57 | 0.91 | 0.27 | 0.87 | 12 (0.07) | mixed residential district |
Noryangjin | 3 | 241.98 | 489.70 | 116.91 | 0.82 | 0.07 | 0.72 | 10 (0.08) | Farmers & fishery market |
Cheongdam | 436 | 216.23 | 292.09 | 104.58 | 0.17 | 1.0 | 0.11 | 1 (0.01) | New developed residential area |
Booam | 437 | 197.47 | 305.49 | 127.87 | 0.17 | 0.67 | 0.15 | 1 (0.02) | Old low-rise residential area |
Pyungchang | 438 | 180.79 | 304.79 | 122.99 | 0.28 | 0.84 | 0.15 | 1 (0.01) | Old low-rise residential area |
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Kwon, Y.J.; Lee, D.K.; Kwon, Y.H. Is Sensible Heat Flux Useful for the Assessment of Thermal Vulnerability in Seoul (Korea)? Int. J. Environ. Res. Public Health 2020, 17, 963. https://doi.org/10.3390/ijerph17030963
Kwon YJ, Lee DK, Kwon YH. Is Sensible Heat Flux Useful for the Assessment of Thermal Vulnerability in Seoul (Korea)? International Journal of Environmental Research and Public Health. 2020; 17(3):963. https://doi.org/10.3390/ijerph17030963
Chicago/Turabian StyleKwon, You Jin, Dong Kun Lee, and You Ha Kwon. 2020. "Is Sensible Heat Flux Useful for the Assessment of Thermal Vulnerability in Seoul (Korea)?" International Journal of Environmental Research and Public Health 17, no. 3: 963. https://doi.org/10.3390/ijerph17030963