تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

ارزیابی میزان تاثیر ذرات معلق و پوشش‌گیاهی بر تشکیل جزایر گرمایی و خنک در شهر تهران

نویسندگان
سید کامیار مرتضوی اصل 1
نوید سعیدی 2
محمود رضایی 3
1 دانشگاه آزاد اسلامی، واحد امارات متحده عربی
2 دانشگاه آزاد اسلامی، واحد قزوین
3 دانشگاه آزاد اسلامی، واحد تهران مرکزی
چکیده
گرمایش جهانی و جزایر حراتی شهرها یکی از بزرگ­ترین چالش ­های جهان امروز است. جزایر سرمایی(جزایر خنک) واژه­ایست که در مقابل جزایر گرمایی قرار می­گیرد و بیان کننده مناطقی از سطح شهر است که نسبت به نواحی اطراف دارای دمای پایین­تری است. در این تحقیق برای بررسی عوامل موثر بر شکل­گیری جزایر خنک و گرمایی شهری، ابتدا با استفاده از پردازش تصاویر لندست و استفاده از الگوریتم تک­کانل دمای سطح زمین به­دست آمد. سپس برای بررسی پارامترهای موثر بر تغییرات دمای سطح زمین؛ معیارهای تغییرات ذرات معلق و تغییرات پوشش­گیاهی درنظر گرفته شد. برای پوشش­گیاهی از شاخص NDVI و برای میزان ذرات معلق از الگوریتم ارائه شده توسط Saraswat و همکاران استفاده شد. مطابق نتایج، بالاترین میزان جزیره­حرارتی به ترتیب در محله بوستان ولایت، شهرک شهید باقری و فرودگاه بودند و پایین­ترین میزان جزایر خنک به ترتیب در بهاران، نیاوران و دربند بود. ضریب پیرسون به­دست آمده از رابطه بین دمای سطح و پوشش­گیاهی 21.29-درصد بود که نشان دهنده رابطه معکوس بین دما و پوشش­گیاهی است، همچنین میزان شاخص پوشش­گیاهی در مناطق گرم و سرد بیانگر این موضوع است. در خصوص رابطه دمای سطح زمین و آلودگی هوا، همبستگی بین این دو پارامتر، برابر با 19.31 درصد بود و مقایسه میزان شاخص آلودگی در مناطق دارای جزایر خنک و گرم نشان داد که رابطه معناداری بین کاهش آلاینده­های هوا و جزایر خنک وجود دارد اما عکس این قضیه چندان صادق نیست.
کلیدواژه‌ها
جزایر خنک
شهر تهران
LST
آلودگی‌هوا

عنوان مقاله English

Evaluation of the effect of particulate matter and vegetation on the formation of heat and cold islands in Tehran

نویسندگان English

Seyed Kamyar Mortazavi-Asl 1
navidsaeidirezvani saeidirezvani 2
Mahmud Rezaei 3
1 Islamic Azad University, UAE
2 Islamic Azad University, Qazveen
3 Islamic Azad University, Central Tehran Branch
چکیده English

Evaluation of the effect of particulate matter and vegetation on the formation of heat and cold islands in Tehran

Seyed Kamyar Mortazavi Asl: PhD Student in Urban Planning, Islamic Azad University, UAE

Dr. Navid Saeedi Rezvani: Assistant Professor, Department of Urban Planning, Faculty of Architecture and Urban Planning, Islamic Azad University, Qazvin, Iran

Dr. Mahmud Rezaei: Associate Professor, Department of Urban Planning, Faculty of Architecture and Urban Planning, Islamic Azad University, Tehran, Iran



Abstract:

Global warming and the heat islands of cities are one of the biggest challenges in the world today. Cold islands is a word that stands in front of heat islands and refers to areas of the city that have lower temperatures than the surrounding areas. In this study, in order to investigate the factors affecting the formation of cool and heat islands of the city, it was first obtained by using Landsat image processing and using the single-channel surface temperature algorithm. Then to investigate the parameters affecting the land surface temperature changes; Criteria for changes in particulate matter and changes in vegetation were considered. The NDVI index was used for vegetation and the algorithm proposed by Saraswat et al. was used for the amount of particulate matter. According to the results, the highest-ranking neighborhood for heat islands were in Bustan, Shahid Bagheri township and the airport, respectively, and the lowest amount of cool islands were in Baharan, Niavaran and Darband, respectively. Pearson coefficient obtained from the relationship between surface temperature and vegetation was -21.29%, which indicates the inverse relationship between temperature and vegetation, as well as the amount of vegetation index in hot and cold regions. Regarding the relationship between land surface temperature and air pollution, the correlation between these two parameters was equal to 19.31% and comparing the pollution index in areas with cold and warm islands showed that there is a significant relationship between reducing air pollutants and cold islands but the opposite is not true.



Keywords: Cool Islands, Tehran, LST, Air Pollution


کلیدواژه‌ها English

Cool Islands
Tehran
LST
Air pollution
8. 8. Akbari, H. 2002. Shade trees reduce building energy use and CO2 emissions from power plants. Environmental pollution, 116: pp.S119-S126.
9. Amiri, R; Weng, Q. Alimohammadi, A. and Alavipanah, S.K. 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote sensing of environment, 113(12): pp.2606-2617.
10. Cerrai, D; Wanik, D.W. Bhuiyan, M.A.E. Zhang, X. Yang, J. Frediani, M.E. and Anagnostou, E.N. 2019. Predicting storm outages through new representations of weather and vegetation. IEEE Access, 7: pp.29639-29654.
11. Chakraborty. T. and Lee. X. 2019. A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation. 74: pp.269-280.
12. Chander, G.; Markham, B.L. and Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113(5): pp.893-903.
13. Erell, E.; and Williamson, T. 2007. Intra‐urban differences in canopy layer air temperature at a mid‐latitude city. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(9): pp.1243-1255.
14. Farhadi, H.; Faizi, M. and Sanaieian, H. 2019. Mitigating the urban heat island in a residential area in Tehran: Investigating the role of vegetation, materials, and orientation of buildings. Sustainable Cities and Society, 46: p.101448.
15. Harper, A.B.; Wiltshire, A.J., Cox, P.M., Friedlingstein, P., Jones, C.D., Mercado, L.M., Sitch, S., Williams, K. and Duran-Rojas, C. 2018. Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4. 6 with new plant functional types. Geoscientific Model Development, 11(7): pp.2857-2873.
16. Jiménez-Muñoz, J.C. and Sobrino, J.A. 2008. Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors. IEEE geoscience and remote sensing letters, 5(4): pp.806-809.
17. Jiménez-Muñoz, J.C. and Sobrino, J.A. 2009. A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, 7(1): pp.176-179.
18. Jiménez-Muñoz, J.C.; Sobrino, J.A., Skoković, D., Mattar, C. and Cristóbal, J. 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and remote sensing letters, 11(10): pp.1840-1843.
19. Ko, Y. 2018. Trees and vegetation for residential energy conservation: A critical review for evidence-based urban greening in North America. Urban Forestry & Urban Greening, 34: pp.318-335.
20. Lenney, M.P.; Woodcock, C.E., Collins, J.B. and Hamdi, H. 1996. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote sensing of environment, 56(1): pp.8-20.
21. Liao, J.; Wang, T., Jiang, Z., Zhuang, B., Xie, M., Yin, C., Wang, X., Zhu, J., Fu, Y. and Zhang, Y. 2015. WRF/Chem modeling of the impacts of urban expansion on regional climate and air pollutants in Yangtze River Delta, China. Atmospheric Environment, 106: pp.204-214.
22. Liu, J.; Gao, G., Wang, S., Jiao, L., Wu, X. and Fu, B. 2018. The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 28(1): pp.59-78.
23. Liu, N. and Morawska, L. 2020. Modeling the urban heat island mitigation effect of cool coatings in realistic urban morphology. Journal of Cleaner Production, 264: p.121560.
24. Lu, J.; Li, B., Li, H. and Al-Barakani, A. 2021. Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities, 108: p.102974.
25. Misni, A. 2018. Vegetation produce an extensive cooling effect. Asian J Qual Life, 3(10): pp.179-187.
26. Montávez, J.P.; González‐Rouco, J.F. and Valero, F. 2008. A simple model for estimating the maximum intensity of nocturnal urban heat island. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(2): pp.235-242.
27. Saraswat, I.; Mishra, R.K. and Kumar, A. 2017. Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India. Remote Sensing Applications: Society and Environment, 8: pp.251-257.
28. Stewart, I. 2011. Redefining the Urban Heat Island. Ph.D Thesis, The University of British Columbia, Vancouver
29. Ullah, M.; Li, J. and Wadood, B. 2020. Analysis of urban expansion and its impacts on land surface temperature and vegetation using RS and GIS, a case study in Xi’an City, China. Earth Systems and Environment, 4(3): pp.583-597.
30. Wang, X.; Chen, F., Wu, Z., Zhang, M., Tewari, M., Guenther, A. and Wiedinmyer, C. 2009. Impacts of weather conditions modified by urban expansion on surface ozone: Comparison between the Pearl River Delta and Yangtze River Delta regions. Advances in Atmospheric Sciences, 26(5): pp.962-972.
31. Wang, X.; Cheng, H., Xi, J., Yang, G., & Zhao, Y. 2018. Relationship between park composition, vegetation characteristics and cool island effect. Sustainability, 10(3): 587.
32. Zanter, K. 2016. Landsat 8 (L8) data users handbook. Landsat Science Official Website, 33.
33. Zhong, Q., Ma, J., Zhao, B., Wang, X., Zong, J., & Xiao, X. 2019. Assessing spatial-temporal dynamics of urban expansion, vegetation greenness and photosynthesis in megacity Shanghai, China during 2000–2016. Remote Sensing of Environment, 233: 111374.