Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Okayama University, Graduate school of Environmental and Life Science, Department of Environmental Science 3-1-1 Tsushima, Kita, Japan

2 Waste Management Research Center Okayama University, 3-1-1 Tsushima, Kita, Okayama 700-8530, Japan

3 Faculty of Environmental Engineering, National University of Civil Engineering, 55 Giai Phong Road, Hai Ba Trung, Ha Noi, Viet Nam

Abstract

A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70–80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.

Graphical Abstract

Highlights

  • The waste generation predictive model is proposed as multivariable linear regression  
  • Urban-rural topology is the most important explanatory variables of waste generation
  • Home business has a significant effect on waste management in the future in Hoi An

Keywords

Main Subjects

Abbasi, M.; Abduli, M.; Omidvar, B.; Baghvand, A., (2013). Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. Int. J. Environ. Res., 7(1): 27-38 (12 pages).

Abdoli, M.A.; Rezaei, M.; Hasanian, H., (2016). Integrated solid waste management in megacities. Global J. Environ. Sci. Manage., 2(3): 289-298 (10 pages).

Abu Qdais, H.A.; Hamoda, M.F.; Newham, J., (1997). Analysis of Residential Solid Waste At Generation Sites. Waste. Manage. Res., 15(4): 395-406 (11 pages).

Akhtar, S.; Saleem, W.; Nadeem, V.M.; Shahid, I.; Ikram, A., (2017). Assessment of willingness to pay for improved air quality using contingent valuation method. Global J. Environ. Sci. Manage., 3(3): 279-286 (17 pages).

Bach, H.; Mild, A.; Natter, M.; Weber, A., (2004). Combining socio-demographic and logistic factors to explain the generation and collection of waste paper. Resour. Conserv. Recyc., 41(1): 65–73 (9 pages).

Bdour, A.; Altrabsheh, B.; Hadadin, N.; Al-Shareif, M., (2007). Assessment of medical wastes management practice: a case study of the northern part of Jordan. Waste. Manage., 27(6): 746–759 (14 pages).

Beigl, P.; Lebersorger, S.; Salhofer, S., (2008). Modelling municipal solid waste generation: A review. Waste Manage, 28(1): 200-214 (15 pages).

Benítez, S.O.; Lozano-Olvera, G.; Morelos, R.A.; Vega, C.A.d., (2008). Mathematical modeling to predict residential solid waste generation. Waste Manage., 28, Supplement 1: S7-S13 (7 pages).

Bernache-Pérez, G.; Sánchez-Colón, S.; Garmendia, A.M.; Dávila-Villarreal, A.; Sánchez-Salazar, M.E., (2001). Solid waste characterisation study in the Guadalajara Metropolitan Zone, Mexico. Waste Manage. Res., 19(5): 413– 424 (15 pages).

Bleeker, S.E.; Moll, H.A.; Steyerberg, E.W.; Donders, A.R.T.; Derksen-Lubsen, G.; Grobbee, D.E.; Moons, K.G.M., (2003). External validation is necessary in prediction research:: A clinical example. J. Clin Epidemiol., 56(9): 826-832 (7 pages).

Bolaane, B.; Ali, M., (2004). Sampling Household Waste at Source: Lessons Learnt in Gaborone. Waste Manage. Res., 22(3): 142–148 (7 pages).

Boulet, S.; Boudot, E.; Houel, N., (2016). Relationships between each part of the spinal curves and upright posture using Multiple stepwise linear regression analysis. J. Biomec., 49(7): (7 pages).

Brown, L.C.; Mac Berthouex, P., (2002). Statistics for environmental engineers. CRC press.

Buenrostro, O.; Bocco, G.; Vence, J., (2001). Forecasting Generation of Urban Solid Waste in Developing Countries—A Case Study in Mexico. J. Air. Waste Manage. Assoc., 51(1): 86-93 (8 pages).

Chai, T.; Draxler, R.R., (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci. Model Dev., 7(3): 1247-1250 (14 pages).

Chang, Y.F.; Lin, C.J.; Chyan, J.M.; Chen, I.M.; Chang, J.E., (2007). Multiple regression models for the lower heating value of municipal solid waste in Taiwan. J. Environ. Manage., 85(4): 891-899 (9 pages).

Chen, H.W.; Chang, N.-B., (2000). Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resour. Conserv. Recyc., 29(1-2): 1-18 (18 pages).

Chu, M.T., (2014). Báo cáo tổng kết đề tài: Xây dựng mô hình đồng quản lý rác thải tại hai xã, phường Cẩm Hà và Cẩm Phô, thành phố Hội An (Project report: building co-operative waste management model in two wards, Cam Ha and Cam Pho, Hoi An city). Management board of Cham islands MPA, Hoi An, Viet Nam.

Cook, R.D., (1977). Detection of Influential Observation in Linear Regression. Technometrics, 19(1): 15-18 (4 pages).

Cook, R.D., (1979). Influential Observations in Linear Regression. J. Am. Stat. Assoc., 74(365): 169-174 (6 pages).

Daskalopoulos, E.; Badr, O.; Probert, S.D., (1998). Municipal solid waste: a prediction methodology for the generation rate and composition in the European Union countries and the United States of America. Res. Conserv Recyc., 24(2): 155-166 (12 pages).

David, M.L., (2007). Influential Observations, Online Statistics Education: A Multimedia Course of Study, Rice University Publication.

Dennison, G.J.; Dodd, V.A.; Whelan, B., (1996). A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland — II. Waste quantities. Resour. Conserv. Recyc., 17(3): 245-257 (13 pages).

Derksen, S.; Keselman, H.J., (1992). Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. Brit. J. Math. Stat. Psychol., 45(2):  265–282 (18 pages).

Faber, N.M.; Rajkó, R., (2007). How to avoid over-fitting in multivariate calibration—The conventional validation approach and an alternative. Anal. Chim. Acta., 595(1–2): 98-106 (9 pages).

Fernández, C.; Ley, E.; Steel, M.F.J., (2001). Model uncertainty in cross-country growth regressions. J. Appl. Econ., 16(5): 563-576 (14 pages).

Ghinea, C.; Drăgoi, E.N.; Comăniţă, E.-D.; Gavrilescu, M.; Câmpean, T.; Curteanu, S.; Gavrilescu, M., (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. J. Environ. Manag., 182: 80-93 (14 pages).

Gomez, G.; Meneses, M.; Ballinas, L.; Castells, F., (2008). Characterization of urban solid waste in Chihuahua, Mexico. Waste Manage., 28(12): 2465-2471 (7 pages).

Grömping, U., (2006). Relative importance for linear regression in R: the package relaimpo. J. Stat. Software, 17(1): 1-27 (27 pages).

Grazhdani, D., (2016). Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park. Waste Manage., 48: 3-13 (11 pages).

Grossman, D.; Hudson, J.; Marks, D., (1974). Waste generation models for solid waste collection. J. San. Eng. Division, 100(6): 1219-1230 (12 pages).

Hamby, D.M., (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assessment, 32(2): 135–154 (10 pages).

HASD, 2013. Statistical Yearbook - Hoi An city, Hoi An Statistical Department, Viet Nam.

Hoang, M.G.; Fujiwara, T.; Pham Phu, S.T., (2017). Municipal waste generation and composition in a tourist city - hoi an, vietnam. J. SCE, 5(1): 123-132 (10 pages).

Hockett, D.; Lober, D.J.; Pilgrim, K., (1995). Determinants of Per Capita Municipal Solid Waste Generation in the Southeastern United States. J. Environ. Manage., 45(3): 205-217 (13 pages).

Hoeting, J.A.; Madigan, D.; Raftery, A.E.; Volinsky, C.T., (1999). Bayesian model averaging: A Tutorial. Stat. Sci., 14(4): 382-401 (20 pages).

Johnson, J.W.; LeBreton, J.M., (2004). History and use of relative importance indices in organizational research. Organ. Res. Method., 7(3): 238-257 (20 pages).

Karpušenkaitė, A.; Denafas, G.; Ruzgas, T., (2016). Forecasting hazardous waste generation using short data sets: Case study of Lithuania. Science–future of Lithuania/Mokslas–lietuvos ateitis, 8(4): 357–364 (8 pages).

Kolekar, K.A.; Hazra, T.; Chakrabarty, S.N., (2016). A review on prediction of municipal solid waste generation models. Procedia Environ. Sci., 35: 238-244 (7 pages).

Lebersorger, S.; Beigl, P., (2011). Municipal solid waste generation in municipalities: quantifying impacts of household structure, commercial waste and domestic fuel. Waste Manage., 31(9-10): 1907-1915 (9 pages).

Lebersorger, S.; Schneider, F.; Hauer, W., (2003). Waste generation in households–models in theory and practical experience from a case study of multifamily dwellings in Vienna. Proceedings in Sardinia, (4 pages).

Madigan, D.; Raftery, A.E., (1994). Model selection and accounting for model uncertainty in graphical models using Occam's window. J. Am. Stat. Assoc., 89(428): 1535-1546 (12 pages).

Mbande, C., (2003). Appropriate approach in measuring waste generation, composition and density in developing areas. J. S. Afr. Inst. Civil Eng., 45(3): 2-10 (9 pages).

Memarianfard, M.; Hatami, A.M.; Memarianfard, M., (2017). Artificial neural network forecast application for fine particulate matter concentration using meteorological data. Global J. Environ. Sci. Manage., 3(3): 333-340 (8 pages).

MOC, (2010). National Technical Regulation Chapter 9: Solid waste collection, separation, transportation, treatment system and public toilet, Hanoi, Vietnam, QCVN 07:2010/BXD

MOC, (2016). National Technical Regulation, Chapter 9: Technical Infrastructure Works, Solid Waste Treatment and Public Toilet, QCVN 07:2016/BXD.

Monavari, S.; Omrani, G.; Karbassi, A.; Raof, F., (2012). The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries (a case study: Ahvaz, Iran). Environ. Monit. Asses., 184(4): 1841–1846 (6 pages).

Ngoc, U.N.; Schnitzer, H., (2009). Sustainable solutions for solid waste management in Southeast Asian countries. Waste Manage, 29(6): 1982-1995 (14 pages).

Nguyen, D.L.; Hoang, M.G.; Bui, X.T., (2013). Challenges for municipal solid waste management practices in Vietnam. Waste Technol., 1(1): 17-21 (5 pages).

Noori, R.; Karbassi, A.; Salman Sabahi, M., (2010). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. J. Environ. Manage., 91(3): 767-771 (5 pages).

Oumarou, M.B.; Dauda, M.T.; Abdulrahim, A.; Abubakar, A.B., (2012). Municipal solid waste generation, recovery and recycling: A case study. World J. Eng. Pure Appl. Sci., 2(5): 143-147 (11 pages).

Parizeau, K.; Maclaren, V.; Chanthy, L., (2006). Waste characterization as an element of waste management planning: Lessons learned from a study in Siem Reap, Cambodia. Resour. Conserv. Recyc., 49(2): 110-128 (19 pages).

Prades, M.; Gallardo, A.; Ibàñez, M.V., (2014). Factors determining waste generation in Spanish towns and cities. Environ. Monit. Assess., 187(1): 4098-4105 (8 pages).

Qu, X.-Y.; Li, Z.-s.; Xie, X.-y.; Sui, Y.-m.; Yang, L.; Chen, Y., (2009). Survey of composition and generation rate of household wastes in Beijing, China. Waste Manage., 29(10): 2618-2624 (7 pages).

Raftery, A.E.; Madigan, D.; Hoeting, J.A., (1997). Bayesian model averaging for linear regression models. J. Am. Stat. Assoc., 92(437): 179-191 (3 pages).

Shamshiry, E.; Bin Mokhtar, M.; Abdulai, A., (2014). Comparison of artificial neural network (ANN) and multiple regression analysis for predicting the amount of solid waste generation in a tourist and tropical area—Langkawi Island. Pproceeding of International Conference on Biological, Civil, Environmental Engineering (BCEE): 161-166 (6 pages).

Sukholthaman, P.; Chanvarasuth, P.; Sharp, A., (2015). Analysis of waste generation variables and people’s attitudes towards waste management system: a case of Bangkok, Thailand. J. Mater. Cycle. Manage.: 645–656 (12 pages).

Thøgersen, J., (1996). Wasteful food consumption: Trends in food and packaging waste. Scand. J. Mgmt., 12(3): 291-304 (15 pages).

Thanh, N.P.; Matsui, Y., (2011). Municipal solid waste management in Vietnam: Status and the strategic actions. Int. J. Environ. Res., 5(2): 285-296 (12 pages).

Thanh, N.P.; Matsui, Y.; Fujiwara, T., (2010). Household solid waste generation and characteristic in a Mekong Delta city, Vietnam. J. Environ. Manage., 91(11): 2307-2321 (5 pages).

Thompson, B., (1995). Stepwise Regression and Stepwise Discriminant Analysis Need Not Apply here: A Guidelines Editorial. Educ. Psychol. Meas., 55(4):525-534 (10 pages).

van de Klundert, A.; Anschütz, J.; Scheinberg, A., (2001). Integrated sustainable waste management: the concept. Tools for decision-makers. experiences from the urban waste expertise programme (1995-2001). Waste.

Vesely, S.; Klöckner, C.A.; Dohnal, M., (2016). Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model. Waste Manage., 49: 530-536 (7 pages).

Wang, M.C.; Bushman, B.J., (1998). Using the normal quantile plot to explore meta-analytic data sets. Psychol. Method., 3(1): 46-54 (9 pages).

Wilk, M.B.; Gnanadesikan, R., (1968). Probability plotting methods for the analysis for the analysis of data. Biometrika, 55(1): 1-17 (17 pages).

Zurbrugg, C.; Gfrerer, M.; Ashadi, H.; Brenner, W.; Kuper, D., (2012). Determinants of sustainability in solid waste management--the Gianyar Waste Recovery Project in Indonesia. Waste Manage., 32(11): 2126-2133 (8 pages).

 

HOW TO CITE THIS ARTICLE: 

Hoang, M.G.; Fujiwara, T.; Pham Phu, S.T.; Nguyen Thi, K.T., (2017). Predicting waste generation using Bayesian model averaging. Global. J. Environ. Sci. Manage., 3(4): 385-402 (18 pages).


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