CBS TABANLI ULAŞIM KAYNAKLI HAVA KİRLETİCİ EMİSYON MİKTARLARININ BELİRLENMESİ

Abstract

Kentsel alanlardaki ulaşım araçlarında küresel bir artış geçekleşmektedir. Bunun sonucu olarak, motorlu taşıtların egzozlarından kent atmosferine verilen kirleticilerin seviyeleri, bölgenin meteorolojik ve topoğrafik koşullarının etkisiyle zaman zaman insan sağlığını tehdit edici boyutlara ulaşabilmektedir. Bu çalışmanın amacı, ulaşımdan kaynaklı emisyon miktarlarının Coğrafi Bilgi Sistemleri (CBS) ile belirlendiği çalışmaların incelenmesi ve Eskişehir Teknik Üniversitesi’nin İki Eylül Kampüsüne giden araçlardan kaynaklanan hava kirletici emisyonlarının ağ analizi ile belirlenmesidir. Mevcut şehir içi otobüslerinin elektrikli olması durumlarında emisyonların azaltılması amacıyla kampüs içine Anadolu Üniversitesi Yunus Emre Kampüsü ile İki Eylül Kampüsü arasında iki farklı güzergahın emisyon etkisi network analizi ile hesaplanmıştır. Analiz sürecinde ArcGIS network analiz aracı kullanılmıştır. Çalışmada EMEP/CORINAIR emisyon faktörü veri tabanından taşıt kategorilerine, motor teknolojisine ve yakıt türlerine göre uygun emisyon faktörleri seçilmiş, seçilen emisyon faktörleriyle otobüs seferleri ve şahsi araç sayıları kullanılarak trafikten kaynaklı hava kirletici emisyon miktarları network analizi ile tahmin edilmiştir. Çalışmadaki ağ analizinin amacı yol tasarımını ve gelişimini yönlendirecek ideal bir ağ modeli bulmak için farklı modellerin trafik koşullarını karşılaştırmaktır.

Keywords

• Network analiz
• CBS
• hava kalitesi

GIS BASED DETERMINATION OF AIR POLLUTANT EMISSION QUANTITIES IN TRANSPORTATION

Abstract

There is a global increase in transportation in urban areas. As a result, the levels of pollutants from motor vehicles in the city atmosphere can sometimes reach levels that threaten human health because of the meteorological and topographical conditions of the region. The aim of this study are to investigate the studies on the emission-related emission amounts by Geographical Information Systems (GIS) and to determine the air pollutant emissions from the vehicles going to Eskişehir Technical University's İki Eylül Campus by network analysis. In order to reduce emissions in the existing electric city buses, the emission impact of two different routes between the Yunus Emre Campus and the İki Eylül Campus of the Anadolu University was calculated by network analysis. ArcGIS network analysis tool was used in the analysis process. In this study, appropriate emission factors were selected from the EMEP / CORINAIR emission factor database to vehicle categories, engine technology and fuel types, air traffic pollutant emissions, bus programs and personal vehicle numbers and network analyzes were performed. The aim of the network analysis in the study is to compare the traffic conditions of different models to find an ideal network model to guide road design and development.

Keywords

• Network analiz
• CBS
• air quality

References

1. Ahn, K, Rakha, H. (2008). The effects of route choice decisions on vehicle energy consumption and emissions Transp. Res. Part D: Transp. Environ., 13 (3) pp. 151-167.
2. Appleyard, B., Mckinstry, J. and Frost, A.R. (2016). Calculating the Campus CarbonFootprint: Measuring University Associeted Greenhouse Gas Emissions from Transport. Transportation Research Record.
3. ArcGIS Network Analyst | Overview. 6 http://www.esri.com/software/arcgis/extensions/networkanalyst/. Accessed Dec. 22, 2018.
4. Bektas,T and Laporte,G.(2011). The pollution-routing problem, Transp. Res. Part B Methodol., 45 (8), pp. 1232-1250.
5. Carins, S. (1999). The home delivery of shopping: the environmental consequences. TSU Working paper, ESRC Transport Studies Unit, University of London, London.
6. Desouky, M., Rahimi, M. and Weidner, M. (2003). Jointly optimizing cost, service and environmental performance in demand-responsive transtit scheduling, Transportation Research:Part D, 8(6), pp 433-465.
7. EEA (European Environment Agency), 2016. EMEP/CORINAIR Emission Inventory Guidebook–2016, https://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory guidebook
8. Ehmke, J.F., Campbell, A.M., and Thomas, B.W. (2016). Vehicle routing to minimize time-dependent emissions in urban areas, European Journal of Operational Research, (251), pp: 478-494.

9. Ericsson,E. Larsson, H. Brundell-Freij, K. (2006), Optimizing route choice for lowest fuel consumption–potential effects of a new driver support tool Transp. Res. Part C: Emerg. Technol., 14 (6), pp. 369-383.
10. Figliozzi, M.A. (2010). Vehicle routing problem for emissions minimization, Transportation Res Rec 2197, 1-7.
11. Kesik, O. A., Aydınoğlu, A. Ç. ve Taştan B. (2015). Ağ Analizi Tekniklerini Kullanarak Afetlerle Başa çıkabilmede Erişebilirlik: İstanbul Fatih İlçesi Örneği, Eastern Geographical Review – 36.
12. Koca, H. ve ELBİR, T. (2013). Bir Üniversite Yerleşkeşi İçinde Karayolu Trafiğinden Kaynaklanan Hava Kalitesinin Belirlenmesi, Hava Kirliliği Araştırmaları Dergisi (HKAD) 2, 45-54.
13. Kono, T., Fushiki,T., Asada, K., Nakano, K. (2008). Fuel consumption analysis and prediction model for “Eco” route search 15th World Congress on Intelligent Transport Systems and ITS America’s 2008 Annual Meeting.
14. Lajunen, A. Lipman T. (2016). Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses, Energy, 106, pp. 329-342.
15. Li, H.C., Chiuch, P.T., Liu, S.P. Huang, Y.Y. (2017). Assessment of different route choice on commuters’ exposure to air pollution in Taipei, Taiwan, Environ. Sci. Pollut. Res. (24), pp: 3163-3171.
16. Mahmoud, M., Garnett, R., Ferguson, M., Kanaroglou, P. (2016). Electric buses: a review of alternative powertrains Renew. Sustain. Energy Rev., 62, pp. 673-684.
17. Mathez, A., K. Manaugh, V. Chakour, A. El-Geneidy, and M. Hatzopoulou (2013) How can we 29 alter our carbon footprint? Estimating GHG emissions based on travel survey information. 30 Transportation, Vol. 40, No. 1, pp. 131–149.
18. Naderipour, M. ve Alinaghian, M.(2016). Measurement, evaluation and minimization of CO2, NOx, and CO emissions in the open time dependent vehicle routing problem, Measurement, (90), pp:443-452.
19. Palmer, A. (2007). The Development of an Integrated Routing and Carbon Dioxide Emissions Model for Goods Vehicles, Ph.D. thesis Cranfield University.Sbihi,A and Eglese, R. (2007). Combinatorial optimization and green logistics 4OR: Q. J. Oper. Res., 5 (2), pp. 99-116.
20. Uçarol, H., Kural, E., Bahar,D. M., Özsu, E. ve Elcik, E.,(2009). Hibrid Ve Elektrikli Araçlar Ulaşımda Enerji Verimliliği İçin Bir Alternatif, TÜBİTAK Marmara Araştırma Merkezi, Enerji Enstitüsü.
21. Van Woensel, T., Creten, R., Vandaele, N. (2001). Managing the environmental externalities of traffic logistics: the issue of emissions, Prod. Oper. Manage., 10 (2), pp. 207-223.
22. Varol, S., Öztürk, Z. ve Öztürk, O. (2018). İstanbul’da Karayolu Yolcu Taşımacılığında Elektrikli Araç Kullanımının İncelenmesi, El-Cezerî Fen ve Mühendislik Dergisi Vol: 5, No: 2, 367-386.
23. Dreier,D., Silveira, S., Khatiwada, D., Fonseca, K.V.O., Nieweglowski, R., Schepanski, R. (2018). Well-to-Wheel analysis of fossil energy use and greenhouse gas emissions for conventional, hybrid-electric and plug-in hybrid-electric city buses in the BRT system in Curitiba, Brazil Transport. Res. Part D Transport Environ., 58, pp. 122-138.
24. Wygonik, E, Goodchild, A. (2011) Evaluating CO2 emissions, cost and service quality trade-offs in an urban delivery system case study, IATSS Research (35), pp:7-15.
25. Yomralıoğlu, T. 2000. Coğrafi Bilgi Sistemleri: Temel Kavramlar ve Uygulamalar.
26. Zeng,W., Miwa, T., Morikawa, T. (2016). Prediction of vehicle CO2 emission and its aplication to eco routing navigation. Transportation Research Part C (68), 194-214.
27. Zhang, Y., Wang, X., Zeng, P. ve Chen, X. (2011). Centrality Characteristics of Road Network Patterns of Traffic Analysis Zones. Transportation Research Record: Journal of the Transportation Research Board,No. 2256, 16–24.