Environmental Management
S. Syafrudin; J.M. Masjhoer; M. Maryono
Abstract
BACKGROUND AND OBJECTIVES: Population growth and economic activity in rural areas are factors driving the waste generation rate. Rural waste management generally still applies conventional patterns and has the potential to damage the environment and threaten human health. Challenges and remedial measures ...
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BACKGROUND AND OBJECTIVES: Population growth and economic activity in rural areas are factors driving the waste generation rate. Rural waste management generally still applies conventional patterns and has the potential to damage the environment and threaten human health. Challenges and remedial measures for solid waste management in rural areas differ from urban ones. The first step in planning a waste management system is to identify the generation and characteristics of waste. Unfortunately, data on waste generation and characteristics in rural areas in developing countries are still minimal. The problems are mainly caused by the development of the tourism industry, and it certainly requires waste management as the solution. However, due to the unavailability of waste generation data, this study aims to measure and analyze waste characteristics in the southern zone of Gunungkidul Regency.METHODS: Primary data collection was taken from 16 randomly selected villages in six sub-districts in Gunungkidul Regency. A door-to-door survey was carried to 110 residential and 160 non-residential samples for eight consecutive days using the Indonesian National Standard 19-3964-1994 method. The processed data were analyzed using a quantitative descriptive method.FINDINGS: The results showed that the average waste generation was 0.29 kilograms per person per day. It shows that the waste generation in the study area is categorized in small-town classification. 75 percent of solid waste generated is food waste and leaves. Meanwhile, paper, plastic, glass, wood, other materials, and fabrics were calculated at 11.8 percent, 10.1 percent, 1.7 percent, 0.5 percent, 0.5 percent, and 0.4 percent respectively. Housing produced less recycled waste as indicated by a high density of 110.6 kilograms per cubic meter. Waste generation and composition are influenced by socioeconomic factors such as economic activity and lifestyle, geographic conditions, and downtown attractiveness.CONCLUSION: The characteristics of the waste produced by the southern zone of Gunungkidul Regency are not much different from most rural areas in developing countries. Rural waste management needs to see organic waste as the main management material. Organic waste processing through composting can be a future solution, but the active role of residents determines its success. In addition, this method can help extend the life of the landfill capacity because the volume of organic waste will be reduced by half.
J.A. Araiza-Aguilar; M.N. Rojas-Valencia; R.A. Aguilar-Vera
Abstract
The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañón del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The ...
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The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañón del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The compiled database consisted of 9 variables with 118 specific data per variable, which were analyzed using a multicollinearity test to select the most important ones. Initially, different regression models were generated, but only 2 of them were considered useful, because they used few predictors that were statistically significant. The most important variables to predict the rate of waste generation in the study area were the population of each municipality, the migration and the population density. Although other variables, such as daily per capita income and average schooling are very important, they do not seem to have an effect on the response variable in this study. The model with the highest parsimony resulted in an adjusted coefficient of 0.975, an average absolute percentage error of 7.70, an average absolute deviation of 0.16 and an average root square error of 0.19, showing a high influence on the phenomenon studied and a good predictive capacity.
M.G. Hoang; T. Fujiwara; S.T. Pham Phu; K.T. Nguyen Thi
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 ...
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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.
A.R. Darban Astane; M. Hajilo
Abstract
The current study was carried out to evaluate the quantity and quality of rural domestic waste generation and to identify the factors affecting it in rural areas of Khodabandeh county in Zanjan Province, Iran. Waste samplings consisted of 318 rural households in 11 villages. In order to evaluate the ...
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The current study was carried out to evaluate the quantity and quality of rural domestic waste generation and to identify the factors affecting it in rural areas of Khodabandeh county in Zanjan Province, Iran. Waste samplings consisted of 318 rural households in 11 villages. In order to evaluate the quality and quantity of the rural domestic waste, waste production was classified into 12 groups and 2 main groups of organic waste and solid waste. Moreover, kriging interpolation technique in ARC-GIS software was used to evaluate the spatial distribution of the generated domestic waste and ultimately multiple regression analysis was used to evaluate the factors affecting the generation of domestic waste. The results of this study showed that the average waste generated by each person was 0.588 kilograms per day. with the share of organic waste generated by each person being 0.409 kilograms per day and the share of solid waste generated by each person being 0.179 kilograms per day. The results from spatial distribution of waste generation showed a certain pattern in three groups and a higher rate of waste generation in the northern and northwestern parts, especially in the subdistrict. The results of multiple regression analysis showed that the households’ income, assets, age, and personal attitude are respectively the most important variables affecting waste generation. The housholds’ attitude and indigenous knowledge on efficient use of materials are also the key factors which can help reducing waste generation.
