Waste generation in Malaysia (states under Act 672), GHG emissions, land use, and health risk assessment.
Description
The secondary data sets of MSW (tonnes) from the year 2014 to 2018 were obtained from the Domestic Waste and Public Cleansing Division of Solid Waste and Public Cleansing Corporation (SWCorp), Malaysia. The data was structured in Microsoft Excel by the area, state, year, and category and analysed by using mathematical equations and the SPSS software.
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Waste dumped in landfills was classified as mixed domestic waste or landfilled waste where the available data from 2014 to 2018 only at the time of data collection. Meanwhile, segregated wastes (plastic, paper, metal, aluminium, glass, and e-waste) were collected separately for recycling. As the mandatory waste segregation at source programme started in September 2015, the available data on segregated waste were selected from 2015 to 2018. The present study collected and analysed 82.07% of domestic waste that had been transported to the transfer station (weighed) and disposed of in 25 landfill sites managed by the study area. The volume of segregated waste (0.05%) represented the volume of recyclable waste that had been transferred and collected at the 38 drop-off points located in the states under Act 672. Bulky and garden waste (12.99%) and public cleansing waste (4.89%) were excluded from the analysis of the study. The study excluded the data on waste volume disposed of in the incinerators and 177 illegal dumping sites due to unavailable data. Thus, the volume of waste generated in the study area might be higher than reported in this study. The study used the Statistical Package for Social Sciences (SPSS) software to analyse and report the statistical data. Descriptive analysis was performed to report the mean and standard deviation (SD). Parametric tests (i.e. Analysis of variance [ANOVA] test) and non-parametric tests (i.e. Kruskal-Wallis test) were used to analyse the comparison of waste volume (i.e., landfilled waste and segregated waste between the study areas. The parametric tests were used as the data were normally distributed, and non-parametric tests were used as the data were not normally distributed. In this study, the normality test was done by using the Skewness and Kurtosis test to identify the Z-score (normal < ±1.96).