Relation of waste generation and composition to socio-economic factors: a case study.
To develop an effective waste management strategy for a given region, it is important to know the amount of waste generated and the composition of the waste stream. Past research has shown that the amount of waste generated is proportional to the population and the average mean living standards or the average income of the people. In addition, other factors may affect the amount and composition of waste. These are climate, living habits, level of education, religious and cultural beliefs, and social and public attitudes. This paper presents the findings of a study carried out in a suburban municipal area in Sri Lanka to determine the solid waste generation rate and waste composition based on field surveys and to determine the related socio-economic factors. A database was developed that included information on the quantity and composition of waste generated in a sample of households in the study area over a time period. The collected data was analysed to relate waste generation and composition data to various socio-economic factors. Over 400 sample households were selected for the study using a stratified random sampling methodology based on municipal wards and property values. A technique that considers both the number of households in a particular income group (property value range) and the standard deviation of property values within a given income group was used to determine the appropriate sample size for each municipal ward. Through category and regression analyses, the quantities of waste and waste composition were related to several socio-economic factors. The paper describes the basis for the sample selection, the methodology adopted for data collection, the socio-economic parameters used for the analysis, and the relationships developed from the analysis.
Bandara
Nilanthi J G J
NJ
University of Sri Jayewardenepura, Nugegoda, Sri Lanka. nbandara@sltnet.lk
Hettiaratchi
J Patrick A
JP
Wirasinghe
S C
SC
Pilapiiya
Sumith
S
D002980:Climate; D003625:Data Collection; D004787:Environmental Pollution / Q000032:analysis / Q000706:statistics & numerical data*; D006801:Humans; D025341:Principal Component Analysis; D012037:Refuse Disposal / Q000379:methods / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012959:Socioeconomic Factors; D013188:Sri Lanka; D018505:Waste Management / Q000379:methods / Q000706:statistics & numerical data*