Log in

Influence of population, income and electricity consumption on per capita municipal solid waste generation in São Paulo State, Brazil

  • ORIGINAL ARTICLE
  • Published:
Journal of Material Cycles and Waste Management Aims and scope Submit manuscript

Abstract

Predicting municipal solid waste (MSW) generation is fundamental in choosing and scaling the processes involved in municipal management. The challenge for financial sustainability is to create indicators that enable MSW fees to be charged in proportion to the amount generated by each resident. Mathematical functions were tested to adjust the per capita waste generation rate (PCWG) in the municipalities of the state of São Paulo, based on population (P), per capita income (PCI) and per capita energy consumption (PCE). The dataset involved 238 municipalities in 2013 and 251 municipalities in 2014 that routinely weighed their wastes. The averaged PCWG increased from 0.65 to 0.90 kg inh.− 1 day− 1 (increment of 38%) when population enhanced from the range of 0–25,000 to 100,001–500,000 inh., mean per capita income grew from 10.1 to 13.6 USD inh.− 1 day− 1, and mean per capita electricity consumption expanded from 6.9 to 10.9 kWh inh.− 1 day− 1. The equation that best represented the data set resulted in r of 0.49, R 2 of 0.24, RMSE of 0.224 kg inh.− 1 day− 1 and E p of − 12.3%. Despite the relatively low R 2, it was demonstrated by Student’s t test that the proposed equation was able to represent mean values and result in the same variance with more than 99% probability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Brazilian Federal Law No. 12,305 of 02 Aug (2010) Establishes the National Solid Waste Policy; amends Law No. 9,605 of 12 Feb 1998, and makes other provisions (in Portuguese)

  2. Brazilian Federal Decree No. 7,404/2010 of 23 Dec (2010) Regulates Law No. 12,305 of 02 Aug 2010, which establishes the national solid waste policy, creates the inter-ministerial committee of the national solid waste policy, the steering committee for the implementation of reverse logistics systems, and makes other provisions (in Portuguese)

  3. Daskalopoulos E, Badr O, Probert SD (1998) Municipal solid waste: a prediction methodology for the generation rate and composition in the European Union countries and the United States of America. Resour Conserv Recycl 24:155–166

    Article  Google Scholar 

  4. Chowdhury M (2009) Searching quality data for municipal solid waste planning. Waste Manag 29:2240–2247

    Article  Google Scholar 

  5. Purcell M, Magette WL (2009) Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region. Waste Manag 29:1237–1250

    Article  Google Scholar 

  6. Liu C, Wu X (2010) Factors influencing municipal solid waste generation in China: A multiple statistical analysis study. Waste Manag Res 29:371–378

    Google Scholar 

  7. Keser S, Duzgunb S, Aksoy A (2012) Application of spatial and non-spatial data analysis indetermination of the factors that impact municipal solid waste generation rates in turkey. Waste Manag 32:359–371

    Article  Google Scholar 

  8. Ghinea C, Dragoi EN, Comanita ED, Gavrilescu M, Câmpean T, Curteanu S, Gavrilescu M (2016) Forecasting municipal solid waste generation using prognostic tools and regression analysis. J Environ Manage 182:80–93

    Article  Google Scholar 

  9. Kawai K, Tasaki T (2016) Revisiting estimates of municipal solid waste generation per capita and their reliability. J Mater Cycles Waste Manag 18:1–13

    Article  Google Scholar 

  10. Hockett D, Lober DJ, Pilgrim K (1995) Determinants of per capita municipal solid waste generation in the Southeastern United States. J Environ Manag 45:205–217

    Article  Google Scholar 

  11. Xu L, Gao P, Cui S, Liu C (2013) A hybrid procedure for MSW generation forecasting at multiple time scales in **amen City China. Waste Manag 33:1324–1331

    Article  Google Scholar 

  12. Benítez SO, Lozano-Olvera G, Morelos RA, Veja CA (2008) Mathematical modeling to predict residential solid waste generation. Waste Manag 28:S7 – S13

    Article  Google Scholar 

  13. Navarro-Esbrí J, Diamadopoulos E, Ginestar D (2002) Time series analysis and forecasting techniques for municipal solid waste management. Resour Conserv Recycl 35:201–214

    Article  Google Scholar 

  14. Campos HKT (2012) Evolution of income and per capita generation of solid wastes in Brazil (in Portuguese). Eng Sanit Ambient 17:171–180

    Article  Google Scholar 

  15. Dias DM, Martinez CB, Barros RTV (2014) Generation estimate of municipal solid waste as subsidy actions aimed to environmental sustainability (in Portuguese). R Bras Cienc Ambient 33:13–20

    Google Scholar 

  16. Melo LA, Sautter KD, Janissek PR (2009) Scenario studies for the management of urban solid waste in Curitiba (in Portuguese). Eng Sanit e Ambient 14:551–558

    Article  Google Scholar 

  17. Dias DM, Martinez CB, Barros PTV, Libânio M (2012) Model to domestic solid waste generation estimative in urban areas based on socioeconomic conjuncture variables (in Portuguese). Eng Sanit Ambient 17:325–332

    Article  Google Scholar 

  18. Pinto MR, Pereira DRM, Freitas RC (2012) Factors social, economic and demographic associated with household waste generation in the city of Belo Horizonte (in Portuguese). Reuna 17:27–44

    Google Scholar 

  19. Silva H, Barbieri AF, Mór RLM (2012) Demography of urban consumption: a study on the generation of solid waste in the city of Belo Horizonte (in Portuguese). R Bras Est Pop 29(2): 421–449

    Article  Google Scholar 

  20. Foundation state system of data analysis—SEADE (2016). http://www.seade.gov.br/. Accessed 20 May 2016

  21. National Secretary of Environmental Sanitation – SNIS (2016). National Information System on Sanitation: Diagnosis of municipal solid waste management—2014 (in Portuguese). Ministry of Cities, Brasilia

    Google Scholar 

  22. Environmental Company of the São Paulo State–CETESB (2015) State inventory of household solid waste 2014 (in Portuguese). http://cetesb.sp.gov.br/residuossolidos/wpcontent/uploads/sites/26/2013/11/residuosSolidos2014.pdf. Accessed 29 Nov 2017

  23. São Paulo State Secretariat of the Environment (2014). Solid waste plan of State of São Paulo. Environmental Company of the São Paulo State. http://www.ambiente.sp.gov.br. Accessed 10 Sep 2017

  24. National Secretary of Environmental Sanitation – SNIS (2015). National Information System on Sanitation: Diagnosis of municipal solid waste management—2013 (in Portuguese). Ministry of Cities, Brasilia

    Google Scholar 

  25. Data Geo (2017) Infrastructure of environmental spatial data of the State of São Paulo—IDEA—SP. http://datageo.ambiente.sp.gov.br. Accessed 20 Ma 2017

  26. Israel GD (1992) Determining sample size. Florida Cooperative Extension Service, University of Florida, Fact sheet PEOD-6, November, pp 1–5

  27. Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241

    Article  Google Scholar 

  28. Moriasi DN, Arnold JG, Liew MWV, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50:885–890

    Google Scholar 

  29. Ayres M (2007) BioEstat Statistical applications in the areas of biomedical sciences (in Portuguese), 5 edn. Federal University of Pará, Belém

    Google Scholar 

  30. Cohen J (1988) Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates, Hillsdale

    MATH  Google Scholar 

  31. Dancey C, Reidy J (2006) Statistics without math for psychology: using SPSS for Windows (in Portuguese). Artmed, Porto Alegre

    Google Scholar 

  32. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New York

    MATH  Google Scholar 

  33. National Secretary of Environmental Sanitation–SNIS (2017). National Information System on Sanitation: Diagnosis of municipal solid waste management—2015 (in Portuguese). Ministry of Cities, Brasilia

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reinaldo Pisani Jr..

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pisani, R., Alves de Castro, M.C.A. & da Costa, A.A. Influence of population, income and electricity consumption on per capita municipal solid waste generation in São Paulo State, Brazil. J Mater Cycles Waste Manag 20, 1216–1227 (2018). https://doi.org/10.1007/s10163-017-0687-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10163-017-0687-0

Keywords

Navigation