Simulation and prediction of urban heat island effect of urban high-speed rail construction

Simulation and prediction of urban heat island effect of urban high-speed rail construction

Hong JiaoYachun Fang 

Northeast Forestry University, Urban and Rural Planning Design Research Center, Harbin 150040, China

Corresponding Author Email: 
fangyachun0614@163.com
Page: 
1438-1442
|
DOI: 
https://doi.org/10.18280/ijht.360436
Received: 
8 April 2018
|
Accepted: 
28 August 2018
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

This paper aims to disclose the impact of urban high-speed rail (HSR) construction on the urban heat island (UHI) effect and predict the UHI effect in future. For this purpose, the radiation transmission method was adopted to investigate the UHI effect in south-eastern China’s Nanchang city, and forecasted the UHI effect from 2018 to 2025 in Nanchang via longitudinal greyscale simulation. The research results show that: the surface temperature is closely related to the urban HSR construction; the maximum surface temperature increases with the built-up area of the HSR; different factors have different effects on the UHI effect, among which population is the leading influencing factor; the mean temperature in 2018~2025 indicates the HSR construction in Nanchang has a severe HSR effect. The research finding shed new light on the studies of UHI effect and its influencing factors.

Keywords: 

urban heat island (UHI) effect, high-speed rail (HSR) construction, urbanization; surface temperature, population, greyscale theory

1. Introduction
2. Relevant Theories
3. UHI Effect of Urban HSR Construction
4. Factors Impacting UHIEffect of Urban HSR Construction and Prediction Model
5. Conclusions
  References

[1] Gagliano A, Nocera F, Aneli S. (2017). Computational fluid dynamics analysis for evaluating the urban heat island effects. Energy Procedia 134: 508-517. http://doi.org/10.1016/j.egypro.2017.09.557

[2] Cannistraro G, Cannistraro M, Cao J, Ponterio L. (2018). New technique monitoring and transmission environmental data with mobile systems. Instrumentation, Mesure, Métrologie 17(4): 549-562, http://dx.doi.org/10.3166/I2M.17.549-562

[3] Mathew A, Khandelwal S, Kaul N. (2018). Analysis of diurnal surface temperature variations for the assessment of surface urban heat island effect over Indian cities. Energy Buildings 159: 271-295. http://doi.org/10.1016/j.enbuild.2017.10.062

[4] Zhang Y, Wang Z, Sun Y. (2009). Analysis of urban heat island effect using an improved cttc and sttc model. Transactions of Tianjin University 15(3): 201-205. http://doi.org/10.1007/s12209-009-0035-0

[5] Pal S, Xueref-Remy I, Ammoura L, Chazette P, Gibert F, Royer P. (2012). Spatio-temporal variability of the atmospheric boundary layer depth over the Paris agglomeration: an assessment of the impact of the urban heat island intensity. Atmospheric Environment 63(15): 261-275. http://doi.org/10.1016/j.atmosenv.2012.09.046

[6] Theophilou MK, Serghides D. (2015). Estimating the characteristics of the urban heat island effect in Nicosia, Cyprus, using multiyear urban and rural climatic data and analysis. Energy & Buildings 108: 137-144. http://doi.org/10.1016/j.enbuild.2015.08.034

[7] Bonafoni S, Baldinelli G, Verducci P. (2017). Sustainable strategies for smart cities: analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustainable Cities & Society 29: 211-218. http://doi.org/10.1016/j.scs.2016.11.005

[8] Memon RA, Leung DYC, Liu CH, Leung MKH. (2011). Urban heat island and its effect on the cooling and heating demands in urban and suburban areas of Hong Kong. Theoretical & Applied Climatology 103(3-4): 441-450. http://doi.org/10.1007/s00704-010-0310-y

[9] Krüger E, Emmanuel R. (2013). Accounting for atmospheric stability conditions in urban heat island studies: The case of Glasgow, UK. Landscape & Urban Planning 117(3): 112-121. http://doi.org/10.1016/j.landurbplan.2013.04.019

[10] Magli S, Lodi C, Contini FM, Muscio A, Tartarini P. (2016). Dynamic analysis of the heat released by tertiary buildings and the effects of urban heat island mitigation strategies. Energy & Buildings 114(1): 164-172. http://doi.org/10.1016/j.enbuild.2015.05.037

[11] Mao W, Wang X, Cai J, Zhu M. (2016). Multi-dimensional histogram-based information capacity analysis of urban heat island effect using Landsat 8 data. Remote Sensing Letters 7(10): 925-934. http://doi.org/10.1080/2150704X.2016.1182656

[12] Vardoulakis E, Karamanis D, Fotiadi A, Mihalakakou G. (2013). The urban heat island effect in a small Mediterranean city of high summer temperatures and cooling energy demands. Solar Energy 94(4): 128-144. http://doi.org/10.1016/j.solener.2013.04.016

[13] Li CF, Shen D, Dong JS, Yin JY, Zhao JJ, Xue D. (2014). Monitoring of urban heat island in shanghai, china, from 1981 to 2010 with satellite data. Arabian Journal of Geosciences 7(10): 3961-3971. http://doi.org/10.1007/s12517-013-1053-8

[14] Balcerak E. (2014). Statistical analysis describes urban heat island effect in Europe. Eos Transactions American Geophysical Union 95(6): 60. http://doi.org/10.1002/2014EO060010

[15] Xu S. (2009). An approach to analyzing the intensity of the daytime surface urban heat island effect at a local scale. Environmental Monitoring & Assessment 151(1-4): 289-300. http://doi.org/10.1007/s10661-008-0270-1

[16] Ramamurthy P, Bou-Zeid E. (2017). Heatwaves and urban heat islands: a comparative analysis of multiple cities. Journal of Geophysical Research Atmospheres 122(1): 168-178. http://doi.org/10.1002/2016JD025357

[17] Kang HQ, Zhu B, Zhu T, Sun JL, Ou JJ. (2014). Impact of megacity shanghai on the urban heat-island effects over the downstream city Kunshan. Boundary-Layer Meteorology 152(3): 411-426. http://doi.org/10.1007/s10546-014-9927-1