SavedAdd your journal

Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2021 / Cilt: 10 Sayı: 2

Investigation of generation of municipal solid waste with global and local spatial autocorrelations

Evsel katı atık oluşum miktarlarının küresel ve yerel mekânsal otokorelasyon yöntemleri ile incelenmesi

Gülden Gök(Aksaray Üniveristesi Mühendislik Fakültesi Çevre Mühendisliği Bölümü)0000-0002-1692-8722
Orhan Gürbüz(Niğde Ömer Halisdemir Üniversitesi Niğde Sosyal Bilimler Meslek Yüksekokulu)0000-0001-7511-5628
Pages
472–478
Publication Date
2021-07-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The high leveled values were showed a clustered pattern in North Western Thrace and Aegean Regions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The spatial analysis of the municipal solid waste is significant to clearfy how this issue behaves regionally.
📊
Key Findings & Data:According to the global autocorrelation analysis results, solid waste generation rates showed statistically significant positive autocorrelation.
💡
Conclusion & Impact:The high leveled values were showed a clustered pattern in North Western Thrace and Aegean Regions.
🔬Design:Araştırma Makalesi
📍Scope:2014 Verileri
⚙️Variables:Mekansal Otokorelasyon · Moran’nın I indeksi · LISA

Abstract

The spatial analysis of the municipal solid waste is significant to clearfy how this issue behaves regionally. The munisipal solid waste (kg) per capita per day was tested both global and local spatial autocorrelation methods. According to the global autocorrelation analysis results, solid waste generation rates showed statistically significant positive autocorrelation. The highest of the calculated Moran's I indexes was found for 2014. Among the local spatial autocorrelation methods, the local Moran's I index (LISA) was used. According to the LISA results, it was observed that the solid waste generation rate was clustered with low leveled values in the Southeastern Anatolia Region and the Eastern Mediterranean regions. The high leveled values were showed a clustered pattern in North Western Thrace and Aegean Regions.

Özet

Katı atık oluşumlarının mekânsal olarak incelenmesi evsel katı atık sorunun bölgesel olarak nasıl davrandığını belirtmek açısından oldukça önemlidir. Bu çalışmada, ortalama günlük kişi başı katı atık miktarları(kg) 2014,2016 ve 2018 yılları için küresel ve yerel mekansal otokorelasyon yöntemleri ile incelenmiştir. Küresel otokorelasyon yöntem sonuçlarına göre katı atık oluşum oranları, istatistiksel olarak anlamlı pozitif otokorelasyon göstermiştir. Moran’nın I indeksi en yüksek 2014 yılı için 0,2980 olarak hesaplanmıştır. Yerel mekansal otokorelasyon yöntemlerinden, yerel Moran’nın I indeksi (LISA) kullanılmıştır. LISA sonuçlarına göre katı atık oluşum oranı düşük seviye olarak Güneydoğu Anadolu Bölgesi ve Doğu Akdeniz bölgelerinde kümelendiği görülmüştür. Yüksek seviyede kümelenme ise Kuzey Batı Trakya ve Ege Bölgelerinde görülmüştür.

🔖

Cite This Article

Gök, G., Gürbüz, O. (2021). Investigation of generation of municipal solid waste with global and local spatial autocorrelations. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 10 Sayı: 2, 472–478. https://doi.org/10.28948/ngumuh.871183

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (37)

  1. C. O. Ugwu, C. G. Ozoegwu, and P. A. Ozor, Solid waste quantification and characterization in university of Nigeria, Nsukka campus, and recommendations for sustainable management. Heliyon, 6 (6), e04255, 2020. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e04255
  2. E. Stunzenas and I. Kliopova, Optimizing municipal biodegradable waste management system to increase biogas output and nutrient recovery: a case study in Lithuania. Energy Procedia, 147, 641-648, 2018. https://doi.org/https://doi.org/10.1016/j.egypro.2018.07.083
  3. L. R. Luttenberger, Waste management challenges in transition to circular economy – Case of Croatia. Journal of Cleaner Production, 256, 120495, 2020. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120495
  4. G. Gök, Estimation of methane generation and energy potential of Nigde landfill site using first order mathematical modelling approaches. journal of engineering sciences and design, 7 (1), 126-135, 2019.
  5. S. Keser, S. Duzgun, and A. Aksoy, Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey. Waste Management, 32 (3), 359-71, 2012. https://doi.org/10.1016/j.wasman.2011.10.017
  6. Y. Li, Y. Cui, B. Cai, J. Guo, T. Cheng, and F. Zheng, Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data. Applied Energy, 266, 2020.https://doi.org/10.1016/j.apenergy.2020.114852
  7. F. Fan, H. Lian, X. Liu, and X. Wang, Can environmental regulation promote urban green innovation Efficiency? An empirical study based on Chinese cities. Journal of Cleaner Production, 2020. https://doi.org/10.1016/j.jclepro.2020.125060
  8. C. Zhang, L. Luo, W. Xu, and V. Ledwith, Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ, 398 (1-3), 212-21, 2008. https://doi.org/10.1016/ j.sci totenv.2008.03.011
  9. M. Agovino, M. D'Uva, A. Garofalo, and K. Marchesano, Waste management performance in Italian provinces: Efficiency and spatial effects of local governments and citizen action. Ecological Indicators, 89, 680-695, 2018. https://doi.org/10.1016/ j.ecolind.2018.02.045
  10. E. S. Thompson, P. Saveyn, M. Declercq, J. Meert, V. Guida, C.D. Eads, E.S.J. Robles, and M.M. Britton, Characterisation of heterogeneity and spatial autocorrelation in phase separating mixtures using Moran's I. J Colloid Interface Sci, 513, 180-187, 2018. https://doi.org/10.1016/j.jcis.2017.10.115
  11. L. S. Premo, Local spatial autocorrelation statistics quantify multi-scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, 31 (7), 855-866, 2004. https://doi.org/https://doi.org/10.1016/j.jas.2003.12.002
  12. E. M. Özgür and O. Aydın, Türkiye’de Evlilik Göçünün Mekânsal Veri Analizi Teknikleriyle Değerlendirilmesi (The Evaluation of Marriage Migration Using Spatial Data Analysis Techniques in Turkey). Coğrafi Bilimler Dergisi/Turkish Journal of Geographical Sciences, 9 (1), 29-40, 2011.
  13. D. Yüncü, İ.O. Coşkun, Y. Mert Kantar, S. Günay Aktaş, and H. Sezerel, Turist Çekicilikleri Ve Turist Akişi Arasindaki Ilişkilerin Mekansal Bağimliliğa Dayali Olarak Incelenmesi. e-Journal of New World Sciences Academy, 12 (4), 232-247, 2017. https://doi.org/10.12739/nwsa.2017.12.4.3c0168
  14. Ö. TÜRKŞEN, Mekansal istatistiklerin bir uygulaması: Simule edilmiş fay düzlemine ilişkin jeodezik noktaların mekansal analizi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 1-13, 2019. https://doi.org/ 10.25092/baunfbed.522967
  15. Ç. L. Uslu, Provincial Income Inequality and Spatial Autocorrelation Across Turkish Provinces: 1992-2013. Sosyoekonomi, 25 (34), 197-211, 2017. https://doi.org/ 10.17233/sosyoekonomi.315759
  16. M. A. Kalkhan, Spatial Statistics GeoSpatial Information Modeling and Thematic Mapping 2011, Boca Raton, FL: CRC Press. 178.
  17. L. Anselin, Local Indicators of Spatial Association—LISA. 27 (2), 93-115, 1995. https://doi.org/https:// doi.org/10.1111/j.1538-4632.1995.tb00338.x
  18. A. Getis and K. Ord, The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24, 189-206, 1992. https:// doi.org/10.1111/j.1538-4632.1992.tb00261.x
  19. TÜİK. Çevre İstatistikleri. [cited 2020 22.11.2020]; Available from:https://tuikweb.tuik.gov.tr/PreTablo.do?alt_id=1019.
  20. TÜİK. Nüfus İstatistikleri. [cited 2020 22.11.2020]; Available from: https://tuikweb.tuik.gov.tr/ UstMenu.do?metod=temelist.
  21. K. Suryowati, R. D. Bekti, and A. Faradila, A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation. IOP Conference Series: Materials Science and Engineering, 335, 2018. https://doi.org/10.1088/1757-899x/335/1/012052
  22. R. E. Plant, Spatial Data Analysis in Ecology and Agriculture Using R Vol. Second Edition. 2019, Boca Raton, FL: CRC Press Taylor & Francis Group. 685.
  23. A. Ghasemi and S. Zahediasl, Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab, 10 (2), 486-9, 2012. https:// doi.org/10.5812/ijem.3505
  24. G. Tepanosyan, L. Sahakyan, C. Zhang, and A. Saghatelyan, The application of Local Moran's I to identify spatial clusters and hot spots of Pb, Mo and Ti in urban soils of Yerevan. Applied Geochemistry, 104, 116-123, 2019.https://doi.org/ 10.1016/ j.apgeochem.2019.03.022
  25. A. Neşe and M. Aytaç, Türkiye’de işsizliğin mekânsal analizi. Öneri Dergisi, 13 (49), 1-20, 2018.
  26. K. M. Çubukçu, Planlamada ve Coğrafyada Temel İstatistik ve Mekansal İstatistik. Vol. 2. Basım. 2019: Nobel 323.
  27. J. Lee, Wong, W. S., Statistical Analysis with ArcView GIS 2001, New York, NY: John Wiley & Sons.
  28. S. Hafeez, M. Amin, and B.A. Munir, Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore. Spat Spatiotemporal Epidemiol, 21, 77-85, 2017. https:// doi.org/10.1016/j.sste.2017.04.001
  29. M. A. Dereli, Polat, Nizar, Boşanma Verilerinin Coğrafi Bilgi Sistemleri Destekli Mekânsal İstatistiksel Yöntemler ile İrdelenmesi. Harran Üniversitesi Mühendislik Dergisi, 3 (3), 112-118, 2018.
  30. Y. Li, Y. Cui, B. Cai, J. Guo, T. Cheng, and F. Zheng, Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data. Applied Energy, 266, 2020.https://doi.org/10.1016/j.apenergy.2020.114852
  31. M. Agovino, M. D'Uva, A. Garofalo, and K. Marchesano, Waste management performance in Italian provinces: Efficiency and spatial effects of local governments and citizen action. Ecological Indicators, 89, 680-695, 2018. https://doi.org/10.1016/ j.ecolind.2018.02.045
  32. L. S. Premo, Local spatial autocorrelation statistics quantify multi-scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, 31 (7), 855-866, 2004. https://doi.org/https://doi.org/10.1016/j.jas.2003.12.002
  33. D. Yüncü, İ.O. Coşkun, Y. Mert Kantar, S. Günay Aktaş, and H. Sezerel, Turist Çekicilikleri Ve Turist Akişi Arasindaki Ilişkilerin Mekansal Bağimliliğa Dayali Olarak Incelenmesi. e-Journal of New World Sciences Academy, 12 (4), 232-247, 2017. https://doi.org/10.12739/nwsa.2017.12.4.3c0168
  34. TÜİK. Çevre İstatistikleri. [cited 2020 22.11.2020]; Available from:https://tuikweb.tuik.gov.tr/PreTablo.do?alt_id=1019.
  35. TÜİK. Nüfus İstatistikleri. [cited 2020 22.11.2020]; Available from: https://tuikweb.tuik.gov.tr/ UstMenu.do?metod=temelist.
  36. A. Ghasemi and S. Zahediasl, Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab, 10 (2), 486-9, 2012. https:// doi.org/10.5812/ijem.3505
  37. G. Tepanosyan, L. Sahakyan, C. Zhang, and A. Saghatelyan, The application of Local Moran's I to identify spatial clusters and hot spots of Pb, Mo and Ti in urban soils of Yerevan. Applied Geochemistry, 104, 116-123, 2019.https://doi.org/ 10.1016/ j.apgeochem.2019.03.022