The impact of heat waves on surface urban heat island and local economy in Cluj-Napoca city, Romania

Abstract

The association between heat waves and the urban heat island effect can increase the impact on environment and society inducing biophysical hazards. Heat stress and their associated public health problems are among the most frequent. This paper explores the heat waves impact on surface urban heat island and on the local economy loss during three heat periods in Cluj-Napoca city in the summer of 2015. The heat wave events were identified based on daily maximum temperature, and they were divided into three classes considering the intensity threshold: moderate heat waves (daily maximum temperature exceeding the 90th percentile), severe heat waves (daily maximum temperature over the 95th percentile), and extremely severe heat waves (daily maximum temperature exceeding the 98th percentile). The minimum length of an event was of minimum three consecutive days. The surface urban heat island was detected based on land surface temperature derived from Landsat 8 thermal infrared data, while the economic impact was estimated based on data on work force structure and work productivity in Cluj-Napoca derived from the data released by Eurostat, National Bank of Romania, and National Institute of Statistics. The results indicate that the intensity and spatial extension of surface urban heat island could be governed by the magnitude of the heat wave event, but due to the low number of satellite images available, we should consider this information only as preliminary results. Thermal infrared remote sensing has proven to be a very efficient method to study surface urban heat island, due to the fact that the synoptic conditions associated with heat wave events usually favor cloud free image. The resolution of the OLI_TIRS sensor provided good results for a mid-extension city, but the low revisiting time is still a drawback. The potential economic loss was calculated for the working days during heat waves and the estimated loss reached more than 2.5 mil. EUR for each heat wave day at city scale, cumulating more than 38 mil. EUR for the three cases considered.

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References

  1. Apostol L, Alexe C, Sfîcă L (2012) Thermic differentiations in the Iaşi municipality during a heat wave. Case Study July 10–20 2011. Present Environ Sustain Dev 1:395–404. Atmospheric Correction Parameter Calculator, URL http://atmcorr.gsfc.nasa.gov/. Accessed 8 May 2016

  2. Barsi JA, Schott JR, Palluconi FD, Hook SJ (2005) Validation of a web-based atmospheric correction tool for single thermal band instruments. In: Proc. SPIE 5882, Earth Observing Systems X, 58820E. p 58820E–58820E–7. doi:10.1117/12.619990

  3. Basara JB, Basara HG, Illston BG, Crawford KC (2010) The impact of the urban Heat Island during an intense heat wave in Oklahoma City. Adv Meteorol 2010:1–10. doi:10.1155/2010/230365

    Article  Google Scholar 

  4. Ben-Dor E, Saaroni H (1997) Airborne video thermal radiometry as a tool for monitoring microscale structures of the urban heat island. Int J Remote Sens 18:3039–3053. doi:10.1080/014311697217198

    Article  Google Scholar 

  5. Bottyan ZS, Kircsi A, Szegedi S, Unger J (2005) The relationship between built-up areas and the spatial development of the mean maximum urban heat island on Debrecen, Hungary. Int J Climatol 25:405–418. doi:10.1002/joc.1138

    Article  Google Scholar 

  6. Campetella C, Rusticucci M (1998) Synoptic analysis of an extreme heat wave over Argentina in March 1980. Met Apps 5:217–226. doi:10.1017/S1350482798000851

    Article  Google Scholar 

  7. Cao MC, Rosado P, Lin ZH, Levinson R, Millstein D (2015) Cool roofs in Guangzhou, China: outdoor air temperature reductions during heat waves and typical summer conditions. Environ Sci Technol 49(24):14672–14679. doi:10.1021/acs.est.5b04886

    Article  Google Scholar 

  8. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252. doi:10.1016/S0034-4257(97)00104-1

    Article  Google Scholar 

  9. Chander G, Markham B (2003) Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Trans Geosci Remote Sens 41:2674–2677. doi:10.1109/TGRS.2003.818464

    Article  Google Scholar 

  10. Cheval S, Dumitrescu A (2015) The summer surface urban heat island of Bucharest (Romania) retrieved from MODIS images. Theor Appl Climatol 121:631–640. doi:10.1007/s00704-014-1250-8

    Article  Google Scholar 

  11. Cheval S, Dumitrescu A (2009) The July urban heat island of Bucharest as derived from modis images. Theor Appl Climatol 96:145–153. doi:10.1007/s00704-008-0019-3

    Article  Google Scholar 

  12. Cheval S, Dumitrescu A, Bell A (2009) The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor Appl Climatol 97:391–401. doi:10.1007/s00704-008-0088-3

    Article  Google Scholar 

  13. Costa H, Floater G, Hooyberghs H, Verbeke S, De Ridder K (2016) Climate change, heat stress and labour productivity: a cost methodology for city economies, Centre for Climate Change Economics and Policy Working Paper No. 278 Grantham Research Institute on Climate Change and the Environment Working Paper No. 248. Available at: http://www.lse.ac.uk/GranthamInstitute/wp-content/uploads/2016/07/Working-Paper-248-Costa-et-al.pdf. Accessed 8 Oct 2016

  14. Croitoru AE, Antonie RI, Rus A (2014) Heat waves and their estimated socio-economic impact in Bucharest City, Romania. Proceedings of the International Multidisciplinary Scientific GeoConference-SGEM. Book Group Author(s):SGEM GEOCONFERENCE ON ENERGY AND CLEAN TECHNOLOGIES, VOL II. Book Series: International Multidisciplinary Scientific GeoConference-SGEM: 375–382. doi:10.5593/sgem2014B41. Available at: http://connection.ebscohost.com/c/articles/101013777/heat-waves-their-estimated-socio-economic-impact-bucharest-city-romania. Accessed 7 Aug 2016

  15. Ćurić M (2012) Measuring system of adverse weather phenomena. Proceedings of the International Conference Air and Water Components of the Environment: 68–73. Available at: http://aerapa.conference.ubbcluj.ro/2012/pdf/09%20Curic%20.pdf. Accessed 5 Aug 2016

  16. Department of the Interior U.S. Geological Survey (2016) Landsat 8 data users handbook V 2.0. EROS: Sioux Falls, South Dakota. Available at: https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf. Accessed 5 Sept 2016

  17. Dousset B, Gourmelon F, Laaidi K, Zeghnoun A, Giraudet E, Bretin P, Maurid E, Vandentorren S (2011) Satellite monitoring of summer heat waves in the Paris metropolitan area. Int J Climatol 31:313–323. doi:10.1002/joc.2222

    Article  Google Scholar 

  18. European Community (2015) Eurostat platform. Available at: http://ec.europa.eu/eurostat/web/products-datasets/-/tsdec310. Accessed 8 Aug 2016

  19. Founda D, Pierros F, Petrakis M, Zerefos C (2015) Interdecadal variations and trends of the urban heat island in Athens (Greece) and its response to heat waves. Atmos Res 161–162:1–13. doi:10.1016/j.atmosres.2015.03.016

    Article  Google Scholar 

  20. Gabriel KMA, Endlicher WR (2011) Urban and rural mortality rates during heat waves in Berlin and Brandenburg, Germany. Environ Pollut 159:2044–2050. doi:10.1016/j.envpol.2011.01.016

    Article  Google Scholar 

  21. Gallo KP, McNab AL, Karl TR et al (1993) The use of NOAA AVHRR data for assessment of the urban heat island effect. J Appl Meteorol 32:899–908. doi:10.1175/1520-0450(1993)032<0899:TUONAD>2.0.CO;2

    Article  Google Scholar 

  22. Hoelscher MT, Nehls T, Janicke B, Wessolek G (2016) Quantifying cooling effects of facade greening: shading, transpiration and insulation. Energ Buildings 114(Special Issue):283–290. doi:10.1016/j.enbuild.2015.06.047

    Article  Google Scholar 

  23. Hu LQ, Monaghan AJ, Brunsell NA (2015) Investigation of urban air temperature and humidity patterns during extreme heat conditions using satellite-derived data. J Appl Meteorol Climatol 54(11):2245–2259. doi:10.1175/JAMC-D-15-0051

    Article  Google Scholar 

  24. Hu Y, Jia G (2009) Influence of land use change on urban heat island derived from multi-sensor data. Int J Climatol 30:1382–1395. doi:10.1002/joc.1984

    Google Scholar 

  25. Icaza LE, Van den Dobbelsteen A, Van der Hoeven F (2016) Surface thermal analysis of North Brabant cities and neighbourhoods during heat waves. TEMA-J Land Use Mobil Environ 9(1):66–90. doi:10.6092/1970-9870/3741

    Google Scholar 

  26. Jiménez-Muñoz JC, Sobrino JA, Skoković D et al (2014) Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett 11:1840–1843. doi:10.1109/LGRS.2014.2312032

    Article  Google Scholar 

  27. Kawashima S, Ishida T, Minomura M, Miwa T (2000) Relations between surface temperature and air temperature on a local scale during winter nights. J Appl Meteorol 39:1570–1579. doi:10.1175/1520-0450(2000)039<1570:RBSTAA>2.0.CO;2

    Article  Google Scholar 

  28. Klein Tank AMG et al (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment. Int J Climatol 22:1441–1453. doi:10.1002/joc.773

    Article  Google Scholar 

  29. Laaidi K, Zeghnoun A, Dousset B et al (2012) The impact of Heat Islands on mortality in Paris during the August 2003 heat wave. Environ Health Perspect 120:254–259. doi:10.1289/ehp.1103532

    Article  Google Scholar 

  30. Larsen L (2015) Urban climate and adaptation strategies. Front Ecol Environ 13(9):486–492. doi:10.1890/150103

    Article  Google Scholar 

  31. Li D, Bou-Zeid E (2013) Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts. J Appl Meteorol Climatol 52:2051–2064. doi:10.1175/JAMC-D-13-02.1

  32. McKinnon M, Buckle E, Gueye K, Toroitich I, Ionesco D, Mach E, Maiero M (eds) (2016) Kjellstrom T, Otto M, Lemke B, Hyatt O, Briggs D, Freyberg C, Lines L (Technical authors). Climate change and labour: impacts of heat in the workplace, climate change, workplace and environmental conditions, occupational, health risks, and productivity—an emerging global challenge to decent works, sustainable development and social equity. International Labour Organization. Available at: http://www.ilo.org/wcmsp5/groups/public/---ed_emp/---gjp/documents/publication/wcms_476194.pdf. Accessed 1 Nov 2016 

  33. Meteomanz (2016) Archive of SYNOP/BUFR observations. Data by days. Available at: http://www.meteomanz.com. Accessed 30 Aug 2016

  34. National Bank of Romania (2015a) Monthly bulletin July 2015, XXIII, 261. Available at: http://www.bnr.ro/Regular-publications-2504.aspx.e2015bl07.pdf. Accessed 15 Nov 2016

  35. National Bank of Romania (2015b) Monthly bulletin August 2015, XXIII, 262. Available at: http://www.bnr.ro/Regular-publications-2504.aspx.e2015bl08.pdf. Accessed 15 Nov 2016

  36. Nichol JE, Fung WY, Lam K, Wong MS (2009) Urban heat island diagnosis using ASTER satellite images and “in situ” air temperature. Atmos Res 94:276–284. doi:10.1016/j.atmosres.2009.06.011

    Article  Google Scholar 

  37. OU 99 (2000) Ordonanta de urgenta nr. 99/2000 din 29/06/2000, privind masurile ce pot fi aplicate in perioadele cu temperaturi extreme pentru protectia persoanelor incadrate in munca. Monitorul Oficial 304/4 iulie 2000. Available at: https://www.iprotectiamuncii.ro/legi/oug-99-2000.pdf. Accessed 30 Sept 2016

  38. Parsons K (2009) Maintaining health, comfort and productivity in heat wave global health action. Glob Health Action 2. doi:10.3402/gha.v2i0.2057

  39. Pogacar T, Zalar M, Crepinsek Z, Bogataj K, Ciuha U, Mekjavic I (2016) Impact of heat waves on labour productivity—case study for industry, EMS annual meeting abstracts vol. 13, EMS2016-510, 2016 16th EMS / 11th ECAC © Author(s) 2016. CC Attribution 3.0 License. EMS Annual Meeting European Conference on Applied Climatology ECAC

  40. Prihodko L, Goward SN (1997) Estimation of air temperature from remotely sensed surface observations. Remote Sens Environ 60:335–346. doi:10.1016/S0034-4257(96)00216-7

    Article  Google Scholar 

  41. Radinovic D, Curic M (2013) Measuring system of adverse weather phenomena. (Abstract). Disaster Adv 6(3):19–23

    Google Scholar 

  42. Reliable Prognosis (2016) Weather archive in Cluj-Napoca. Available at: https://rp5.ru/Weather_archive_in_Cluj-Napoca. Accessed 19 Aug 2016

  43. Robine J-M, Cheung SLK, Le Roy S et al (2008) Death toll exceeded 70,000 in Europe during the summer of 2003. Comptes Rendus Biologies 331:171–178. doi:10.1016/j.crvi.2007.12.001

    Article  Google Scholar 

  44. Roth M, Oke TR, Emery WJ (1989) Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology—International Journal of Remote Sensing—Volume 10, Issue 11. Int J Remote Sens 10:1699–1720

    Article  Google Scholar 

  45. Sfîcă L, Croitoru AE, Iordache I, Ciupertea AF (2017) Synoptic conditions generating heat waves and warm spells in Romania. Atmosphere 8:50. doi:10.3390/atmos8030050

    Article  Google Scholar 

  46. Sobrino JA, Jimenez-Munoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90:434–440

    Article  Google Scholar 

  47. Stewart ID (2011) A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol 31:200–217. doi:10.1002/joc.2141

    Article  Google Scholar 

  48. Stoll MJ, Brazel AJ (1992) Surface-air temperature relationships in the urban environment of phoenix, Arizona. Phys Geogr 13:160–179. doi:10.1080/02723646.1992.10642451

    Google Scholar 

  49. Tan J, Zheng Y, Tang X et al (2010) The urban heat island and its impact on heat waves and human health in Shanghai. Int J Biometeorol 54:75–84. doi:10.1007/s00484-009-0256-x

    Article  Google Scholar 

  50. Unger J, Gál T, Rakonczai J, Mucsi L, Szatmári J, Tobak Z, van Leeuwen B, Fiala K (2010) Modeling of the urban heat island pattern based on the relationship between surface and air temperatures. Időjárás 114:287–302

    Google Scholar 

  51. Unger J, Savic SM, Gál T, Milosevic DD (2014) Urban climate and monitoring network system in European cities. University of Novi Sad, Faculty of Sciences (UNSPMF) and University of Szeged, Department of Climatology and Landscape Ecology (SZTE), Novi-Sad (Serbia)—Szeged (Hungary)

  52. USGS (2016) EarthExplore. Available at: http://earthexplorer.usgs.gov. Accessed 8 May 2016

  53. Ward K, Lauf S, Kleinschmit B, Endlicher W (2016) Heat waves and urban heat islands in Europe: a review of relevant drivers. Sci Total Environ 569:527–539. doi:10.1016/j.scitotenv.2016.06.119

    Article  Google Scholar 

  54. Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483. doi:10.1016/j.rse.2003.11.005

    Article  Google Scholar 

  55. Wetterzentrale (2016) Archiv. Reanalyse. Available at: http://www.wetterzentrale.de. Accessed 5 Aug 2016

  56. Xiong Y, Huang S, Chen F et al (2012) The impacts of rapid urbanization on the thermal environment: a remote sensing study of Guangzhou, South China. Remote Sens 4:2033–2056. doi:10.3390/rs4072033

    Article  Google Scholar 

  57. Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106:375–386. doi:10.1016/j.rse.2006.09.003

    Article  Google Scholar 

  58. Zander KK, Botzen WJW, Oppermann E, Kjellstrom T, Garnett ST (2015) Heat stress causes substantial labour productivity loss in Australia. Nat Clim Chang 5:647–651. doi:10.1038/nclimate2623

    Article  Google Scholar 

  59. Zhou Y, Shepherd JM (2010) Atlanta’s urban heat island under extreme heat conditions and potential mitigation strategies. Nat Hazards 52:639–668. doi:10.1007/s11069-009-9406-z

    Article  Google Scholar 

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Acknowledgements

This research was developed under the framework of the project Extreme weather events related to air temperature and precipitation in Romania, project code PN-II-RU-TE-2014-4-0736, funded by the Executive Unit for Financing Higher Education, Research, Development, and Innovation (UEFISCDI) in Romania. The work was also partially supported by the Sectorial Operational Program for Human Resources Development 2007-2013, co-financed by the European Social Fund, under the project number POSDRU/159/1.5/S/132400 titled Young successful researchers—professional development in an international and interdisciplinary environment. The authors acknowledge the USGS for freely provided LANDSAT 8 imagery, the reliable prognosis 5 days for weather data for Cluj-Napoca weather station, the National Bank of Romania, the National Institute of Statistics, and Eurostat for freely provided economic data. The authors bring kind acknowledgements to the three anonymous reviewers for their useful comments and suggestions, which helped us to improve the quality of this paper.

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Correspondence to Adina-Eliza Croitoru.

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Herbel, I., Croitoru, AE., Rus, A.V. et al. The impact of heat waves on surface urban heat island and local economy in Cluj-Napoca city, Romania. Theor Appl Climatol 133, 681–695 (2018). https://doi.org/10.1007/s00704-017-2196-4

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