نوع مقاله : مقاله علمی پژوهشی
نویسندگان
گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Evaluation of environmental quality of urban life by spatial multi criteria analysis (case study: region 6 of Tehran)
1. Introduction
The environment is arena of human life and. In the last few decades, concerns about the quality of the living environment has been one of the most important problem of the society. Unsustainable and irregular development of cities that the producing the majority of the contaminants and are the center of all other human activities (Seyfadin and Mansourian, 2011, p53), cause different kind of crisis like environmental problem and reduced quality of the environment (Camp et al, 2003, p. 16). Thus improvement of the quality of urban living environment is one of the most important goals of any society. Quality of life is a complex and multi-dimensional concept that encompasses social, economic, environmental and physical dimensions of urban quality is one of the key dimensions of quality of life. Tehran is a metropolis that is in critical environmental conditions in many of the 22 regions (Farhadi and Taheri, 2009: 204). In 2017, the city ranked 199th out of 231 major cities in the world in terms of urban quality (Mercer Human Resources Advisor, 2017). At present, the continuation of the current trend can seriously affect the viability of Tehran in the not too distant future, therefore, special studies and investigations are needed to improve and improve the quality of Tehran's environment. The main objective of this study is to assess the quality of the environmental dimension of urban life with incorporation AHP-OWA method in different scenarios and based on different degrees of risk taking. As well as the analysis and study of dimensions and identifying effective indicators on the environmental dimension of urban life quality, and remarking the problems and deficiencies in order to help managers and urban planners are the goals of this study.
2. Method
In terms of the nature, this study is a descriptive-quantitative and analytical one because by providing information about studied option, we describe it, then analyzes the data through different procedures.In this method first criteria are extracted using satellite images, layers of information and pollution measurement stations data that contains: greenness, the temperature of the Earth's surface, air pollution, noise pollution and vulnerability of urban buildings. In the second stage, the analysis and comparison of two for two criteria done using hierarchical analysis method for the determination of the final criteria weights. Finally, we overlap the above mentioned indicators through the AHP-OWA consolidated method in the Arc GIS and final indicator the environmental of urban life quality resulted. Region 6 of Tehran is selected as the research territory and geography and statistical population resulted from this region. This is an area with a surface of 45.2138 hectares, approximately 3.3 percent of the city surface and in view of geographic location is located in the central district of Tehran.
3.Results
After providing the standard criteria affecting the final quality of the living environment, the final environmental dimension of quality of urban life derived using the analysis overlap. To calculate the weight of the criteria, Choice Expert software is used. Paired comparison matrix indicates that air pollution has the most and the temperature of the Earth's surface has the lowest importance in evaluating the quality of the urban environment. The amount of incompatibility in paired comparison criteria is 0.04 and shows being the comparison is acceptable.Findings of criteria map shows that Keshawarz Boulevard, Saei and Valfajr neighborhoods have appropriate greenness, while in Vanak neighborhood due to high density of buildings greenness is not sufficient. The Earth's surface temperature criteria suggests that Northern neighborhoods have the highest value of land surface temperature and the neighbors with sufficient vegetation have the minimum value. The amount of air pollution in the central and southern to southwest regions of Tehran is higher than north and northeast of city.In terms of noise pollution criteria, the neighborhoods located in the south of the city have worse situations than north and central localities. Finally the results of the vulnerability of the building shows that 42% of buildings have low vulnerability, 53% with the average vulnerability and 5% have high vulnerability.
4. Conclusion
Results in different scenarios suggest that 7% of the region is in very appropriate, 29% in appropriate, 17% in the medium condition, 22% in an inappropriate situation and 24% are in very bad situations. According to the environmental quality of urban life in designed scenarios, The environmental quality of urban life in the most pessimistic designed scenarios indicates that no neighborhood is in a very good group and 3 are in a very inappropriate group, 4 are in the middle group and 2 are in the appropriate group, while the most optimistic. The state has 6 neighborhoods in the appropriate group and 1 neighborhood in the inappropriate group. Also, 1 neighborhood in the inappropriate group, 2 neighborhoods in the middle group and 4 neighborhoods in the appropriate group. This indicates that in modeling the quality of life in this area even if the degree of risk taking in decision-making is increased or a very optimistic view of the environmental dimension of quality of life is still 1 neighborhood of this area namely Amirabad neighborhood with very poor quality of life and 1 neighborhood. That is, Ganjavi's Nezamy district has a poor quality of life.
The results also suggest that of the south and southwest parts of the region are in better situation compared to the north and north-astern. According to the different scenarios in different degrees of risk taking, in this area, air pollution is the first and the most effective factor in reducing dimension of environmental life quality. So planning and preventive measures to reduce air pollution is proposed to solve this problem.
کلیدواژهها [English]
10. Fekri, R.; Rostami, Z. and Tahsini, H. (2018). Evaluation of the condition of air pollutants in mashhad city at different stations by using the inverse distance weighting method, Anthropocen. Pollut. J., 2: 26-32.
11. Farhadi, S. and Taheri, S. (2009). Interconnections of Urban Green Spaces and Environmental Quality of Tehran, International Journal of Environmental Research, 3(2): 199-208.
12. Gurram, M.K. (2016). Urban Environmental Quality Assessment at Ward Level Using AHP Based GIS Multi-Criteria Modeling–A Study on Hyderabad City, India, Asian Journal of Geoinformatics, 15(30): 16-29.
13. Guleid, A.; Gadain, H.M.; Muthusi, F.M. and Muchiri, P.W. (2007). Improving flood forecasting and earlywarning in Somalia, feasibility study, Nairobi, Kenya: FAO-SWALIM.
14. Gupta, K.; Kumar, P.; Pathan, S.K. and Sharma, K.P. (2012). Urban Neighborhood Green Index–A measure of green spaces in urban areas, Landscape and Urban Planning, 105(3): 325-335.
15. Gurjar, B.R.; Jain, A.; Sharma, A.; Agarwal, A.; Gupta, P.; Nagpure, A.S. and Lelieveld, J. (2010). Human health risks in megacities due to air pollution,Atmospheric Environment, 44(36): 4606-4613. https://doi.org/10.1016/j.atmosenv.2010.08.011
16. Hunashal, R.B. and Patil, Y.B. (2012). Assessment of Noise Pollution Indices in the City of Kolhapur, India,Procedia - Social and Behavioral Sciences, 37: 448-457. https://doi.org/10.1016/j.sbspro.2012.03.310
17. Istamto, T.; Houthuijs, D. and Lebret, E. (2014). Willingness to pay to avoid health risks from road-traffic-related air pollution and noise across five countries, Science of the Total Environment, 497: 420-429.
18. Jelokhani-Niaraki, M. and Malczewski, J. (2015). A group multicriteria spatial decision support system for parking site selection problem: A case study, Land Use Policy, 42: 492-508.
19. Joseph, M.; Wang, F. and Wang, L. (2014). GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti, Habitat International, 41: 33-40. https://doi.org/10.1016/j.habitatint.2013.06.009
20. Keles,R.,2012.The Quality of Life and the Enviroment, Socity and Bihavioral Sciences , V.35, pp.23-32
21. Krinitcyna, Z.V; Mikhailova,T. R.; German, M,V.(2016).Quality of life as the basis for achieving social welfare of the population. In SHS Web of Conferences (Vol.28). EDP Sciences
22. Li, Z.W.; Zeng, G.M.; Zhang, H.; Yang, B. and Jiao, S. (2007). The integrated eco-environment assessment of the red soil hilly region based on GIS-A case study in Changsha City, China, Ecological Modelling, 202(3-4): 540-546. https://doi.org/10.1016/j.ecolmodel.2006.11.014
23. Li, G. and Weng, Q. (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data, International Journal of Remote Sensing, 28(2): 249-267.
24. Liu,Y.;Yue,W.; Fan, P.; Zhang, Zh. and Huang, J. (2017). Assessing the urban environmental quality of mountainous cities: A case study in Chongqing, China, Ecological Indicator, 81(132-145). https://doi.org/10.1016/j.ecolind.2017.05.048
25. Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis, International Journal of Applied Earth Observation and Geoinformation, 8(4): 270-277. https://doi.org/10.1016/j.jag.2006.01.003
26. Marans, R.W. (2015). Quality of urban life and environmental sustainability studies: Future linkage opportunities, Habitat International, 45(P1): 47-52. https://doi.org/10.1016/j.habitatint.2014.06.019
27. Mahdi, M.A; Hosseine, A; Pourahmad, A; Hataminejad, H. (2016).Analysis of effective environmental factors an urban health, a case study of Qom, Iran.Habital International,55, 89-99
28. MHRC (2017). Mercer Human Resource Consulting, Quality of Living Global City Rankings Mercer Survey.
29. Musse, M.A.; Barona, A.D. and Rodriguez, S.M.L. (2018). Urban environmental quality assessment using remote sensing and census data, Int J APPI Earth Obs Geoinformation, 71: 95-108. https://doi.org/10.1016/j.jag.2018.05.010
30. Mohit, M. A. (2014). Present Trend and Future Direction of Quality-of-Life. Procedia-Social and Behavioral Sciences, 153, 655-665
31. Myrtho, J.; Fahui, W. and Lei, W. (2017). GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti, Habitat International, 41, 33-40.
32. Nichol, J.E. and Wong, M.S. (2018). Assessing Urban Environmental Quality with Multiple Parameters, Urban remote sensing, 253.
33. Nichol, J. and Wong, M.S. (2005). Modeling urban environmental quality in a tropical city, Landscape and urban planning, 73(1): 49-58.
34. Rahman, M.R.; Shi, Z.H. and Chongfa, C. (2014). Assessing regional environmental quality by integrated use of remote sensing, GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration, Environmental Monitoring and Assessment, 186(11): 6993-7009. https://doi.org/10.1007/s10661-014-3905-4
35. Renard, K.G.; Foster, G.R.; Weesies, G.A.; McCool, D.K. and Yoder, D.C. (1997). Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE), Agriculture Handbook, 703, Washington
36. Robinson, D.T.; Murray-Rust, D.; Rieser, V.; Milicic, V. and Rounsevell, M. (2012). Modelling the impacts of land system dynamics on human well-being: Using an agent-based approach to cope with data limitations in Koper, Slovenia. Computers, Environment and Urban Systems, 36(2): 164-176. https://doi.org/10.1016/j.compenvurbsys.2011.10.002
37. Saaty. T.L. (2004) Decision making the Analytic Hierarchy and Network Processes (AHP/ANP) ,Journal of Systems Science and Systems Engineering, 13 (1) (2004). pp. 1–35
38. Saaty,T.L. and Vargas, L.G. (1991). Prediction, Projection and Forecasting, Kluwer Academic Publishers, Dordrecht, pp 251.
39. Streimikiene, D.(2015). Enviroment indicator for the assessment of quality of life. Intellectual Economic. 9(1), 67-79
40. Shih, H.S.; Shyur, H.J. and Lee, E.S. (2007). An extension of TOPSIS for group decision making, Mathematical and Computer Modelling, 45(7-8): 801-813. https://doi.org/10.1016/j.mcm.2006.03.023
41. Sluiter, R. (2008). Interpolation Methods For Climate Data, Literature Review. Knmi., R & D Information And Observation Technology, De Bilt (1-29).
42. Sotoudeheian, S. and Arhami, M. (2014). Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran, Journal of Environmental Health Science and Engineering, 12(1): 122. doi:10.1186/s40201-014-0122-6
43. Theofilou, P. (2013). Quality of life: difination and measurment Europe Journal Psycholo, 9(1), 62-150
44. USGS (2013). Using the USGS Landsat 8 Product, Available online: http://landsat.usgs.gov/Landsat8 Using Product.php_ (accessed: 19 December 2014).
45. Van Kamp, I.; Leidelmeijer, K.; Marsman, G. and De Hollander, A. (2003). Urban environmental quality and human well-being, Landscape and Urban Planning, 65(1-2): 5-18. https://doi.org/10.1016/S0169-2046(02)00232-3
46. Wagner, E.D. (2002). Public key infrastructure (PKI) and virtual private network (VPN) compared using autility function and the analytic hierarchy process (AHP). M.Sc.
47. Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends, ISPRS Journal of Photogrammetry and Remote Sensing, 64(4): 335-344.
48. Yager, R.R. (1996). Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11(1): 49-73. https://doi.org/10.1002/(SICI)1098-111X(199601)11:1<49: AID-INT3>3.3.CO;2-L
49. Yong, L.; Wenze, Y.; Peilei, F.; Zhengtao, Z. and Jingnan, H. (2017). Assessing the urban environmental quality of mountainous cities: A case study in Chongqing, China, Ecological Indicators, 81: 132-145.