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Spatial-temporal analysis of day-night time SUHI and its relationship between urban land use, NDVI, and air pollutants in Tehran metropolis

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Abstract

One of the basic factors that should be investigated and monitored in the field of urban heat islands is the exploration and detection of their spatiotemporal changes, which have been well addressed in spatial statistics. The current study aimed to detect the spatiotemporal changes of surface urban heat islands (SUHI) in Tehran metropolis during the daytime/nighttime at monthly and seasonal scales and over the warm and cold periods of the year. The consequences of many elements like as daytime/nighttime land surface temperature (LST) extracted by the MODIS/006/MOD11A1 and the NDVI extracted by MODIS/006/MOD13A2 over a 20-year period (2001–2020) were first investigated. Then, the SUHI index was computed for the study area. The correlations between the heat islands and urban land use (traffic, population density, airport, etc.), air pollutants (CO, NO2, SO2, etc.), and NDVI were investigated in the next stage. Finally, Moran’s algorithm was used to measure the spatial autocorrelation, and Gi statistic was used to analyze the cold and warm spots. The results indicated that the LST trend was constant during the daytime/nighttime, and the NDVI also had a slight rising trend. The results of the SUHI maps indicated that the zones with heat islands during the daytime over the seasons’ warm and cold times are located in the south, southeast, and west of the city. During the nighttime, the central zones of the city as well as some parts in the east and southeast have had higher heat islands. The results of the correlation between the heat islands and land use, vegetation, and air pollutants indicated a direct correlation between the heat islands and the airport and industrial land use over time, while it was inversely correlated with other land uses. During the nighttime, all land uses had a direct correlation with the heat islands. Regarding the air pollutants, PM2.5 and PM10 were most correlated with the heat islands during both daytime/nighttime while other pollutants have been inversely correlated. The heat islands and the NDVI were also inversely correlated during both daytime/nighttime. The OLS (ordinary least-squares) model results also indicated that the R2 values during the daytime/nighttime were 0.70 and 0.59, respectively, over the cold period of the year, compared to values of 0.69 and 0.68 over the warm period of the year. The results of global Moran’s I and G*i statistics also indicated that the heat islands of the Tehran metropolis had a spatial structure distributed in a cluster in which the southern, western, southwestern, and northern parts had warm spots during the daytime and cold spots during the nighttime. Moreover, the northern and northeastern parts had cold spots during the daytime, and the central and eastern parts had warm spots during the nighttime.

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References

  • Abutaleb K, Ngie A, Darwish A, Ahmed M, Arafat S, Ahmed F (2015) Assessment of urban heat island using remotely sensed imagery over Greater Cairo, Egypt. Adv Remote Sens 4:35

    Article  Google Scholar 

  • Aghazadeh F, Ghasemi M, Garajeh MK, Feizizadeh B, Karimzadeh S, Morsali R (2023) An integrated approach of deep learning convolutional neural network and Google Earth Engine for salt storm monitoring and mapping. Atmos Pollut Res 14(3):101689

    Article  Google Scholar 

  • Ahmed S (2018) Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. Egypt J Remote Sens Space Sci 21:15–25

    Google Scholar 

  • Alizadeh-Choobari O, Ghafarian P, Adibi P (2016) Inter-annual variations and trends of the urban warming in Tehran. Atmos Res 170:176–185

    Article  Google Scholar 

  • Aslan N, Koc-San D (2021) The use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries. ISPRS Int J Geo Inf 10:416

    Article  Google Scholar 

  • Bahrainy H (1997) Application of meteorology of air pollution in urban design of the city of Tehran. Journal of Environmental Studies. Sci Rep Ser Environ 22:17–31

    Google Scholar 

  • Bakhshi Lomer AR, Rezaeian M, Rezaei H, Lorestani A, Mijani N, Mahdad M, Raeisi A, Arsanjani JJ (2023) Optimizing emergency shelter selection in earthquakes using a risk-driven large group decision-making support system. Sustainability 15:4019. https://doi.org/10.3390/su15054019

    Article  Google Scholar 

  • Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A (2016) Assessment of urban heat island based on the relationship between LST and land use/land cover in Tehran. Sustain Cities Soc 23:94–104

    Article  Google Scholar 

  • Chakraborty T, Hsu A, Manya D, Sheriff G (2020) A spatially explicit surface urban heat island database for the United States: characterization, uncertainties, and possible applications. ISPRS J Photogramm Remote Sens 168:74–88

    Article  Google Scholar 

  • Chakraborty TC, Lee X, Ermida S, Zhan W (2021) On the land emissivity assumption and Landsat-derived surface urban heat islands: a global analysis. Remote Sens Environ 265:112682

    Article  Google Scholar 

  • Choudhury D, Kalikinkar D, Arijit D (2019) Assessment of land use land cover changes and its impact on variations of LST in Asansol-Durgapur Development Region. Egypt J Remote Sens Space Sci 22:203–218

    Google Scholar 

  • Chow WTL, Roth M (2006) Temporal dynamics of the urban heat island of Singapore. Int J Climatol 2260:2243–2260

    Article  Google Scholar 

  • da Silva JM, Prasad S, Diniz-Filho JA (2017) The impact of deforestation, urbanization, public investments, and agriculture on human welfare in the Brazilian Amazonia. Land Use Policy 65:135–142

    Article  Google Scholar 

  • Dousset B, Gourmelon F (2003) Satellite multi-sensor data analysis of urban LSTs and landcover. ISPRS J Photogramm Remote Sens 58:43–54

    Article  Google Scholar 

  • Egbo MN, Bartholomew DC (2018) Forecasting students’ enrollment using neural networks and ordinary least squares regression models. J Adv Stat 3(4):45–57. https://doi.org/10.22606/jas.2018.34001

    Article  Google Scholar 

  • Erdem U, Cubukcu KM, Sharifi A (2021) An analysis of urban form factors driving urban heat island: the case of Izmir. Environ, Dev Sustain 23:7835–7859

    Article  Google Scholar 

  • Estoque RC, Murayama Y (2014) Measuring sustainability based upon various perspectives: a case study of a hill station in Southeast Asia. Ambio 43:943–956

    Article  Google Scholar 

  • Farhadi H, Faizi M, Sanaieian H (2019) Mitigating the urban heat island in a residential area in Tehran: investigating the role of vegetation, materials, and orientation of buildings. Sustain Cities Soc 46:101448

    Article  Google Scholar 

  • Fast JD, Torcolini JC, Redman R (2005) Pseudovertical temperature profiles and the urban heat island measured by a temperature datalogger network in Phoenix. Arizona J Appl Meteorol 44:3–13

    Article  Google Scholar 

  • Franco S, Mandla VR, Rao KR (2017) Urbanization, energy consumption and emissions in the Indian context a review. Renew Sustain Energy Rev 71:898–907

    Article  Google Scholar 

  • Gelfand AE, Diggle P, Guttorp P, Fuentes M (2010) Handbook of spatial statistics, 1st edn. CRC Press, Boca Raton (FL). https://doi.org/10.1201/9781420072884

    Book  Google Scholar 

  • Ghanbari Ghozikali M, Heibati B, Naddafi K, Kloog I, Oliveri Conti G, Polosa R, Ferrante M (2016) Evaluation of chronic obstructive pulmonary disease (COPD) attributed to atmospheric O3, NO2, and SO2 using air q model (2011–2012 year). Environ Res 144:99–105

    Article  Google Scholar 

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27

    Article  Google Scholar 

  • Gu K, Zhou Y, Sun H, Dong F, Zhao L (2021) Spatial distribution and determinants of PM2. 5 in China’s cities: fresh evidence from IDW and GWR. Environ Monit Assess 193:1–22

    Article  Google Scholar 

  • Gunathilaka MDKL, Harshana WTS (2021) Evaluation of urban heat island (UHI) spatial change in freshwater lakes with hot spot analysis (GI statistics). Int J Environ, Eng Education 3:48–58

    Article  Google Scholar 

  • Hao Y, Zheng S, Zhao M, Wu H, Guo Y, Li Y (2020) Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China—new evidence using the dynamic threshold panel model. Energy Rep 6:28–39

    Article  Google Scholar 

  • Hou L, Yue W, Liu X (2021) Spatiotemporal patterns and drivers of summer heat island in Beijing-Tianjin-Hebei Urban Agglomeration, China. IEEE J Sel Top Appl Earth Obs Remote Sens 14:7516–7527

    Google Scholar 

  • Hu T, Mallick K, Hulley GC, Planells LP, Göttsche FM, Schlerf M et al (2022) Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: temperature-based validation and cross-satellite comparison. Remote Sens Environ 282:113296

    Article  Google Scholar 

  • Imhoff ML, Zhang P, Wolfe RE, Bounoua L (2010) Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ 114:504–513

    Article  Google Scholar 

  • Kamaruddin SA, Aziz KNA, Roslani A, Zainol ZE, Ahmad A, Shaari MI, Tajam J (2022) The mapping of salinity level using the inverse distance weighted (IDW) interpolation method along the coastal area of Pulau Tuba, Langkawi. Malays J Sustain Environ 9:55–76

    Article  Google Scholar 

  • Kazemi Garajeh M, Blaschke T, Hossein Haghi V, Weng Q, Valizadeh Kamran K, Li Z (2022a) A comparison between Sentinel-2 and Landsat 8 OLI satellite images for soil salinity distribution mapping using a deep learning convolutional neural network. Can J Remote Sens 48:452–468

    Article  Google Scholar 

  • Kazemi Garajeh M, Feizizadeh B (2021) A comparative approach of data-driven split-window algorithms and MODIS products for land surface temperature retrieval. Appl Geomat 13:715–733

    Article  Google Scholar 

  • Kazemi Garajeh M, Weng Q, Hossein Haghi V, Li Z, Kazemi Garajeh A, Salmani B (2022b) Learning-based methods for detection and monitoring of shallow flood-affected areas: impact of shallow-flood spreading on vegetation density. Can J Remote Sens 48:481–503

    Article  Google Scholar 

  • Khaniabadi YO, Goudarzi G, Daryanoosh SM, Borgini A, Tittarelli A, De Marco A (2017) Exposure to PM10, NO2, and O3 and impacts on human health. Environ Sci Pollut Res 24:2781–2789

    Article  Google Scholar 

  • Kordi F, Hamzeh S, Atarchi S, Alavipanah SK (2018) Agricultural product classification for optimal water resource management using the data time series of Landsat8. Iran J Ecohydrol 5(4):1267–1283. https://doi.org/10.22059/ije.2018.264578.943

    Article  Google Scholar 

  • Kordi F, Yousefi H (2022) Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images. Remote Sens Appl: Soc Environ 27:100812

    Google Scholar 

  • Kordi F, Yousefi H, Ghasemi L, Tajrishy M (2021) Investigation and comparison of land use map database in the Urmia lake basin. Iran J Ecohydrol 8(4):891–905. https://doi.org/10.22059/ije.2021.324719.1514

    Article  Google Scholar 

  • Kordi F, Yousefi H, Tajrishi M (2022) Estimation of water consumption in the downstream agricultural area of Hasanlu Dam using METRIC algorithm. Water Irrig Manag 12(1):171–185. https://doi.org/10.22059/jwim.2022.331905.937

    Article  Google Scholar 

  • Liang Z, Huang J, Wang Y, Wei F, Wu S, Jiang H, Li S (2021) The mediating effect of air pollution in the impacts of urban form on nighttime urban heat island intensity. Sustain Cities Soc 74:102985

    Article  Google Scholar 

  • Liu X, Tian G, Feng J, Wang J, Kong L (2018) Assessing summertime urban warming and the cooling efficacy of adaptation strategy in the Chengdu-Chongqing metropolitan region of China. Sci Total Environ 610–611:1092–1102

    Article  Google Scholar 

  • Lloyd CD (2010) Local models for spatial analysis, 2nd edn. CRC Press, Boca Raton (FL)

    Book  Google Scholar 

  • Lun X, Wang Y, Zhao C, Wu H, Zhu C, Ma D, Meng F (2022) Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019. Infect Dis Poverty 11:1–17

    Article  Google Scholar 

  • Mamdoohi AR, Rezaei H, Irannezhad E, Saffarzadeh A, Abbasi M (2022) Hour and period based congestion pricing, case of Tehran mode choice. Q J Transp Eng 14(1):2233–2247. https://doi.org/10.22119/jte.2022.285329.2532

    Article  Google Scholar 

  • Meftahi M, Monavari M, Kheirkhah Zarkesh M, Vafaeinejad A, Jozi A (2022) Achieving sustainable development goals through the study of urban heat island changes and its effective factors using spatio-temporal techniques: the case study (Tehran city). In: Natural resources forum, vol 46. Blackwell Publishing Ltd, Oxford, UK, pp 88–115

    Google Scholar 

  • Mutiibwa D, Strachan S, Albright T (2015) LST and surface air temperature in complex terrain. IEEE J Sel Top Appl Earth Obs Remote Sens 8:4726–4774

    Article  Google Scholar 

  • Nascetti A, Di Rita M, Ravanelli R, Amicuzi M, Esposito S, Crespi M (2017) Free global DSM assessment on large scale areas exploiting the potentialities of the innovative Google Earth Engine platform. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-1/W1:627–633

    Article  Google Scholar 

  • Nejadrekabi M, Eslamian S, Zareian MJ (2022) Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province. Int J Environ Sci Technol 19:6573–6594

    Article  Google Scholar 

  • Oke TR, Mills G, Christen A, Voogt JA (2017) Urban climates. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  • Parlow E, Vogt R, Feigenwinter C (2014) The urban heat island of Basel-seen from different perspectives. DIE ERDE-J Geographical Soc Berlin 145(1-2):96–110

    Google Scholar 

  • Peng W, Wang R, Duan J, Gao W, Fan Z (2022) Surface and canopy urban heat islands: does urban morphology result in the spatiotemporal differences? Urban Clim 42:101136

    Article  Google Scholar 

  • Rahman M, Rony M, Hasan R, Jannat FA, Chandra Pal S, Islam M, Islam ARM (2022) Impact of urbanization on urban heat island intensity in major districts of Bangladesh using remote sensing and geo-spatial tools. Climate 10:3

    Article  Google Scholar 

  • Ravanelli R, Nascetti A, Cirigliano RV, Rico CD, Leuzzi G, Monti P, Crespi M (2018) Monitoring the impact of land cover change on surface urban heat island through Google Earth Engine: proposal of a global methodology, first applications and problems. Remote Sens (Basel) 10:1488

    Article  Google Scholar 

  • Rezaei H, Irannezhad E, Mamdoohi AR (2021) Heterogeneous analysis of mode choice behavior using latent class model. J Transp Res 18(3):61–74. https://doi.org/10.22034/tri.2021.115402

    Article  Google Scholar 

  • Rocklov J, Forsberg B, Ebi K, Bellander T (2014) Susceptibility to mortality related to temperature and heat and cold wave duration in the population of Stockholm County. Sweden Glob Health Action 12:22737

    Article  Google Scholar 

  • Saffarzadeh M, Rezaei H, majidi, M. Z. (2022) A pricing model for freeway tolls based on the share of mode shift, route shift, travel time change and users’ willingness to pay (case study: Tehran_Saveh Freeway). J Transp Res 19(3):359–370

    Google Scholar 

  • Santamouris M (2020) Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energ Buildings 207:109482

    Article  Google Scholar 

  • Sekertekin A (2019) Validation of physical radiative transfer equation-based land LST using Landsat 8 satellite imagery and SURFRAD in-situ measurements. J Atmos Solar-Terrestrial Phys 196:105161

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Weng Q, Liu H, Valavi R (2020) Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran. GIScience & Remote Sens 57(4):483–496

    Article  Google Scholar 

  • Shahmohamadia P, Che-Ania AI, Etessamb I, Mauludc KNA, Tawil NM (2011) Healthy environment: the need to mitigate urban heat island effects on human health. Procedia Eng 20:61–70

    Article  Google Scholar 

  • Sheffet O (2019) Differentially private ordinary least squares. J Priv Confid 9(1):1–43. https://doi.org/10.29012/jpc.654

    Article  Google Scholar 

  • Şimşek K, Alp S (2022) Evaluation of landfill site selection by combining fuzzy tools in GIS-based multi-criteria decision analysis: a case study in Diyarbakır, Turkey. Sustainability 14(16):9810

    Article  Google Scholar 

  • Sobrino JA, Oltra-Carrió R, Sòria G, Bianchi R, Paganini M (2012) Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens Environ 117:50–56

    Article  Google Scholar 

  • Stathopoulou M, Cartalis C (2009) Downscaling AVHRR land LSTs for improved surface urban heat island intensity estimation. Remote Sens Environ 113:2592–2260

    Article  Google Scholar 

  • Statistical Centre of Iran (2016) Census information. Statistical Centre of Iran, Tehran https://amar.sci.org.ir/indexe.aspx. Accessed 18 Jan 2022

    Google Scholar 

  • Streutker DR (2003) Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens Environ 85:282–289

    Article  Google Scholar 

  • Ullah N, Siddique MA, Ding M, Grigoryan S, Zhang T, Hu Y (2022) Spatiotemporal impact of urbanization on urban heat island and urban thermal field variance index of Tianjin City. China Buildings 12(4):399

    Article  Google Scholar 

  • United Nations (2018) Revision of world urbanization prospects. https://www.un.org/development/desa/publications/2018-revision-of-world-urbanization-prospects.html. Accessed 18 Jan 2022

    Google Scholar 

  • Vailshery LS, Jaganmohan M, Nagendra H (2013) Effect of street trees on microclimate and air pollution in a tropical city. Urban For Urban Green 12:408–415

    Article  Google Scholar 

  • Wan J, Yong B, Zhou X (2022) Spatial and temporal analysis of the increasing effects of large-scale infrastructure construction on the surface urban heat island. Ecotoxicol Environ Saf 237:113521

    Article  Google Scholar 

  • Wang L, Hou H, Weng J (2020) Ordinary least squares modelling of urban heat island intensity based on landscape composition and configuration: a comparative study among three megacities along the Yangtze River. Sustain Cities Soc 62:102381

    Article  Google Scholar 

  • Xiong L, Li S, Zou B, Peng F, Fang X, Xue Y (2022) Long time-series urban heat island monitoring and driving factors analysis using remote sensing and geodetector. Front Environ Sci 9:828230

    Article  Google Scholar 

  • Yao R, Wang L, Huang X, Zhang W, Li J, Niu Z (2018) Interannual variations in surface urban heat island intensity and associated drivers in China. J Environ Manage 222:86–94

    Article  Google Scholar 

  • Yu Z, Yao Y, Yang G, Wang X, Vejre H (2019) Spatiotemporal patterns and characteristics of remotely sensed region heat islands during the rapid urbanization (1995-2015) of Southern China. Sci Total Environ 674:242–254

    Article  Google Scholar 

  • Zhang CY, Zhao L, Zhang H, Chen MN, Fang RY, Yao Y, Wang Q (2022) Spatial-temporal characteristics of carbon emissions from land use change in Yellow River Delta region. China Ecol Indic 136:108623

  • Zhao W, He J, Wu Y, Xiong D, Wen F, Li A (2019) An analysis of land LST trends in the Central Himalayan Region based on MODIS products. Remote Sens (Basel) 11:900

    Article  Google Scholar 

  • Zhou D, Zhang L, Li D, Huang D, Zhu C (2016) Climate-vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environ Res Lett 11(7):074009

    Article  Google Scholar 

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Acknowledgements

The authors thank the anonymous reviewers for their constructive comments and suggestions on the earlier versions of this article.

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F.A., S.B., M.K.G., and M.G. are the major author who has organized the research, running the research methodology, and wrote the manuscript. S.M., E.K., and B.F. have collaborated in writing the sections, improving, and editing the manuscript.

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Correspondence to Firouz Aghazadeh.

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Aghazadeh, F., Bageri, S., Garajeh, M.K. et al. Spatial-temporal analysis of day-night time SUHI and its relationship between urban land use, NDVI, and air pollutants in Tehran metropolis. Appl Geomat 15, 697–718 (2023). https://doi.org/10.1007/s12518-023-00515-w

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