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Modelling Municipal Solid Waste Generation Using Geographically Weighted Regression: A Case Study of Nigeria

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Author Affiliations

  • 11 Department of Urban and Regional Planning, Modibbo Adama University of Technology, Yola, NIGERIA
  • 2 Department of Surveying and Geoinformatics, Modibbo Adama University of Technology, Yola, NIGERIA
  • 3 Department of Geography, Adamawa State University, Mubi, NIGERIA

Int. Res. J. Environment Sci., Volume 4, Issue (8), Pages 98-108, August,22 (2015)

Abstract

This study is aimed at developing a spatial model for municipal solid waste (MSW) generation rate based on socio-economic, demographic and climatic variables for Nigeria. The outcome is targeted at effective forecasting and management of MSW in the country. Secondary data sources were used to obtain the variables, then screened and linked to the administrative boundaries of the 36 States and the Federal Capital Territory (Abuja). Geographically Weighted Regression (GWR) tool in ArcGIS 10.0® was used to analyse the data. The analysis gives an acceptable condition number of 16.63, while local R ranges from 0.54 to 0.90. The model also explains 65 per cent of the total variation in the dependent variables. The findings of this study revealed that nearer States tend to have similar coefficients than the distant ones and that dependent variables vary among States. In addition, the coefficient estimates of unemployment rate, employment in crop farming, literate adults above 15 years, per-capita average household expenditure on food and non-food items, and excess proceeds of crude oil to local government areas exhibit positive relationship with MSW throughout the country. Whereas, only rainfall variable exhibited positive and negative relationship in northern and southern part of the country, respectively. The paper contributed towards improving the understanding of factors affecting MSW generation rate in Nigeria.

References

  1. Shamshiry E., Nadi B., Bin Moktar M., Komoo I. and Hashim H.S., Urban solid waste management based geoinformatics technology, J. of Public Health and Epidemiology,3(2), 54-60 (2011)
  2. Hakami B.A. and Abu Seif E.S., Household solid waste composition and management in Jeddah City, Saudi Arabia: a planning model, Int. Res. J. of Environment Sci.,4(1), 1-10 (2015)
  3. Prabhaker G. and Chapla J., Solid waste management studies in Karimnagar town, Telangana, India, Int. Res. J. of Environment Sci.,4(1), 16-18 (2015)
  4. Velsivasakthivel S. and Nandini N., Airborne multiple drug resistant bacteria isolated from concentrated municipal solid waste dumping site of Bangalore, Karnataka, India, Int. Res. J. of Environment Sci.,3(10), 43-46 (2014)
  5. Devi K.S., Swamy A.V.V.S. and Krishna R.H., Studies on the solid waste collection by rag pickers at Greater Hyderabad municipal corporation, India, Int. Res. J. of Environment Sci.,3(1), 13-22, (2014)
  6. Bandara N.J., Hettiaratchi J.P., Wirasinghe S.C. and Pilapiiya S., Relation of waste generation and composition to socio-economic factors: a case study, Environmental Monitoring and Assessment, 135(1-3), 31-39 (2007)
  7. Katsamaki A., Willems S. and Diamadopoulos E., Time series analysis of municipal solid waste generation rates, J. of Environmental Engineering, 124 (2), 178-183 (1998)
  8. Adedibu A.A., Spatial pattern of solid waste generation of Ilorin-Nigeria, Environmental Monitoring and Assessment, 5(3), 237-247 (1985)
  9. Abu Qdais H., Hamoda M.F. and Newham J., Analysis of residential solid waste at generation sites, Waste Management and Research, 15(4), 395-406 (1997)
  10. Beigl P., Wassermann G., Schneider F. and Salhofer S., Forecasting municipal solid waste generation in major European cities. iEMSs International Conference: ‘‘Complexity and Integrated Resources Management. 14-17 June 2004, Osnabrueck, Germany. [Online] Available: http://www.iemss.org/iemss2004/pdf/regional/ beigfore.pdf, 15, 2015, (2004)
  11. Hockett D., Lober D.J. and Pilgrim K., Determinants of per capita municipal solid waste generation in the Southeastern United States, J. of Environmental Management,45(3), 205-217 (1995)
  12. Owusu-Sekyere E., Harris E. and Bonyah E., Forecasting and planning for solid waste generation in the Kumasi Metropolitan area of Ghana: an ARIMA time series approach, Int. J. of Sciences,, 69-83 (2013)
  13. UN, Information provided by Government of Nigeria to the United Nations Commission on Sustainable Development: Chapter 21, [Online] Available: http://www.un.org/dpcsd/earthsummit (March 3, 2015, (1997)
  14. Ezekiel E., Ecological fund: Federal Government disburses N3 billion in two years, [Online] Available: http://olusegunadekoya.wordpress.com/2010/08/19/nigeria-ecological-fund-issues/ (January 15, 2015, (2010)
  15. Ismaila A.B., Evaluating land use change in rapidly urbanizing Nigeria: case study of Yola, Adamawa State, In: Ivan, I., Longley, P., Horak, J., Fritsch, D., Cheshire, J., Inspektor, T. (eds.). GIS Ostrava 2013 - Geoinformatics for City Transformations. Ostrava: VŠB - Technical University of Ostrava, 21 - 23 January, 2013. ISBN 978-80-248-2974-6, 93 - 107 (2013)
  16. Goli A., Kermany F.S. and Askarian M., Spatial prevalence of intellectual disability and related socio-demographic factors in Iran, using GWR: case study, Int. J. Prev Med,, 313-24 (2006)
  17. Yildirim J., Öcal N. and Korucu N., Analysing the determinants of terrorism in Turkey using geographically weighted regression, [Online] Available: http://www.ub.edu/sea2009.com/Papers/10.pdf (February 2, 2015, (2009)
  18. Cai R., Yu D. and Oppenheimer M., Estimating the effects of weather variations on Corn yields using geographically weighted panel regression, Mathematical Problems in Engineering, [Online] Available: http://dx.doi.org/10.1155/2012/369539 (March 20, 2015, (2012)
  19. Cho S., Kim S.G., Clark C.D. and Park W.M., Spatial analysis of rural economic development using a locally weighted regression model, Agricultural and Resource Economics Review, 36(1), 24–38 (2007)
  20. Mennis J. and Jordan L., The distribution of environmental equity: exploring spatial nonstationarity in multivariate models of air toxic releases, Annals of the Assoc. of American Geographers,95, 249-268 (2005)
  21. Calvo C. and Escolar M., The Local Voter: A Geographically Weighted Approach to Ecological Inference, American J. of Political Science,47, 189-204 (2003)
  22. National Bureau of Statistics, Annual abstract of statistics, Federal Republic of Nigeria (2011)
  23. World Bank, [Online] Available: http://data.worldbank.org/country/nigeria, (January 10,2015, (2012)
  24. National Bureau of Statistics, Annual abstract of statistics, Federal Republic of Nigeria (2009)
  25. Hoornweg D. and Bhada-Tata P., What a waste: A global review of solid waste management, [Online] Available: http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2012/07/25/000333037_ 20120725004131/Rendered/PDF/681350WP0REVIS0at0a0Waste20120Final.pdf (January 29, 2015, (2012)
  26. DIVA GIS, [Online] Available: http://www.diva-gis.org/Data, (2015)
  27. ArcGIS 10.1 Help, Interpreting OLS results. [Online] Available: http://resources.arcgis.com/en/help/main/10.1/ index.html#//005p00000030000000 (2015)
  28. Leeb, What I learned about OLS and GWR. [Online] Available: http://blogs.oregonstate.edu/geo599 spatialstatistics /author/leeb/(2013)
  29. Goodchild M.F., Spatial autocorrelation - concepts and techniques in modern geography, Norwich (CT): Geo Books (1984)
  30. Llyod C.D., Local models for spatial analysis (2nd ed.). CRC Press, New York, USA (2011)
  31. Tobler W.R.,A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234-240 (1970)
  32. Anselin L., and Griffith D., Do spatial effects really matter in regression analysis?, Papers of the Regional Science Association,65, 11-34 (1988)
  33. Burt J.E. and Barber G.M., Elementary statistics for geographers (2nd Edition), The Guilford Press, New York, USA, (1996)
  34. Cliff A.D. and Ord J.K., Spatial autocorrelation, London: Pion, (1973)
  35. Cliff A.D. and Ord J.K., Spatial processes: models and applications, London: Pion, (1981)
  36. Moran P., The interpretation of statistical maps, J.R. Stat Soc, 10(2), 243-51 (1948)
  37. Charlton M., Fotheringham S. and Brunsdon C., Geographically weighted regression, ESRC National Centre for Research Methods NCRM Methods Review Papers NCRM/006. [Online] Available: http://eprints.ncrm.ac.uk/90/1/ Methods ReviewPaperNCRM-006.pdf, (2006)
  38. Luo J. and Wei D.Y., Modeling spatial variations of urban growth patterns in Chinese cities: the case of Nanjing, Landscape and Urban Planning,91(2), 51-64 (2009)
  39. Yaylac E.D., Ismaila A.B., Uskay O. and Düzgün ., Spatial analyses of electricity supply and consumption in Turkey for effective energy management and policy-making, In: Schmidt, M., Onyango, V., Palekhov, D. (eds). Implementing Environmental and Resource management. Springer-Verlag, Berlin. Page 153-168 (2011)
  40. Keser S., Investigation of the spatial relationship of municipal solid waste generation in Turkey with socio-economic, demographic and climatic factors. Msc. Thesis, Department of Environmental Engineering, Middle East Technical University, Ankara – Turkey, (2010)