Solid waste composition and greenhouse gas potential from solid waste in Gubeng Distric Surabaya

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1 The 4th International Seminar Department of Environmental Engineering Department of Environmental Engineering, Institut Teknologi Sepuluh Nopember Public Health Program Study, Medical Faculty, Udayana University Solid waste composition and greenhouse gas potential from solid waste in Gubeng Distric Surabaya Yevi Putri Agustia a*, Welly Herumurti b a Department of Environmental Engineering, InstitutTeknologiSepuluhNopember, Surabaya, Indonesia b Laboratory Technology and Management of Solid and Hazardous Waste, Department of Environmental Engineering, InstitutTeknologiSepuluhNopember, Surabaya, Indonesia *yeviputriagustia@gmail.com Abstract Solid waste study was conducted to investigate the solid waste composition and characteristic in Gubeng District, Surabaya. Methane potentials from solid waste landfill were also determine based on IPCC 2006 method and LandGEM US EPA Model The estimation of methane potential from a landfill,depending on the solid waste compositionwaste.solid waste composition and characteristic data were obtained from five solid waste transfer depo in eight days using SNI method. Solid waste composition were 58%, 4%, 9%, 1%, 3%, 9% and 14% of food, garden, paper, wood, textile, nappies, and plastic/other inert waste, respectively. Benowo solid waste landfill was determined to unmanaged-deep solid waste landfill in IPCC 2006 method because currently operated by dumping method. Methane potential from Gubeng solid waste was kg CH 4 /ton solid waste. Whereas, based on LandGEM US EPA Model 2005 methane potential from solid waste landfill was kg CH 4 /ton solid waste and carbon dioxide was kg CO 2 /ton solid waste. Methane potential from both methods was quite different.the LandGEM model was underestimated in methane generation if compared with IPCC model. Keywords: IPCC, LandGem, methane, solid waste landfill 1 Introduction Municipal solid waste (MSW) is generated by daily activities at homes, hospitals, schools, businesses, and industries primarily from two sources, residential (55 65%) and commercial (35 45%) MSW is affected by multiple factors including changes in population, waste generation rates, technology, consumer behavior, and the state of the economy. In addition to demands imposed by increasing population and waste generation rates, collection service providers have expanded the frequency and type of waste collection. In general, commercial customers are provided two service lines-waste and recyclables, while residential customers are provided three service lines-waste, recyclables, and yard waste. The collection frequency for commercial customers varies depending on customer need. In contrast, the collection frequency for residential customers depends on climate, geography, competition, and the cost of the service (Maimounet al., 2013) MSW is formed of a mixture of household waste (residential and commercial waste), sweepings from streets, parks and gardens, and sludge. The amounts of CH 4, CO 2, and N 2 O emitted vary depending on the amount of garbage produced, the content of organic matter in its composition, and the anaerobic conditions of disposal. A sanitary landfill acts as a large bioreactor, where biodegradation of organic matter in waste occurs in a predominantly anaerobic environment. This degradation results in the generation of biogas (or landfill gas LFG), mainly composed of CO 2 and CH 4, which are greenhouse gases, the latter with global warming power 21 times larger than the former (Loureiro, et al., 2013). Mineralization and increase in ammonium concentration inhibits methanotrophic activity. However, this may not be true for landfills because, when N 2 O was kept as a constant, CO 2 and CH 4 were not well correlated It may be said that methane generation and its oxidation perhaps also influence emissions of N 2 O and CO 2 from landfills (Jha et al., 2008).

2 Yevi Putri Agustia, Welly Herumurti The total carbon content of MSW can be divided into two main categories biogenic carbon and fossil carbon. Biogenic carbon is mainly found in biodegradable fractions, such as organic kitchen waste and cardboard (bio-waste), and paper. Fossil carbon is, in general, non-degradable and is found in plastic and synthetic fabric. The remaining dry recyclable material in MSW isprimarily metal and glass which may be recycled and contain verylittle or no carbon, respectively. MSW also contains other inert materials including aggregates and soils (Couth et al., 2011).The composition of solid waste was determined in this research. Greenhouse gas (GHG) potential from solid waste GHG emissions was calculated using based on IPCC 2006 method and LandGEM US EPA Model Method 2.1 The IPCC Method The CH 4 emissions from solid waste disposal for a single year can be estimated using equations below. CH 4 isgenerated as a result of degradation of organic material under anaerobic conditions. Part of the CH4 generated isoxidized in the cover of the SWDS, or can be recovered for energy or flaring. The CH 4 actually emitted from theswds will hence be smaller than the amount generated. CCCC 4 EEEEEEEEEEEEEEEEEE = CCCC 4 gggggggggggggggggg xx,tt RR TT xx (1 OOOO TT ) CH4 Emissions = CH4 emitted in year T, Gg T = inventory year x = waste category or type/material R T = recovered CH4 in year T, Gg OX T = oxidation factor in year T, (fraction) xx DDOCm W DOC DOC f MCF DDDDDDDD mm = WW xx DDDDDD xx DDDDDD ff xx MMMMMM = mass of decomposable DOC deposited, Gg = mass of waste deposited, Gg = degradable organic carbon in the year of deposition, fraction, Gg C/Gg waste = fraction of DOC that can decompose (fraction) = CH 4 correction factor for aerobic decomposition in the year of deposition (fraction) LL oo = DDDDDDDD mm xx FF xx 16/12 L o = CH 4 generation potential, Gg CH 4 DDOC m = mass of decomposable DOC, Gg F = fraction of CH 4 in generated landfill gas (volume fraction) 16/12 = molecular weight ratio CH 4 /C (ratio) DDDDDD = (DDDDDD ii xx WW ii ) ii DOC = fraction of degradable organic carbon in bulk waste, Gg C/Gg waste DOC i = fraction of degradable organic carbon in waste type i e.g., the default value for paper is 0.4 (wet weight basis) W i = fraction of waste type i by waste category e.g., the default value for paper in MSW in Eastern Asia is (wet weight basis)

3 The 4thInternational Seminar Department of Environmental Engineering 2.2 The LandGem Method LandGEM is based on a first-order decomposition rate equation for quantifying emissionsfrom the decomposition of landfilled waste in MSW landfills. The software provides a relatively simple approach to estimating landfill gas emissions. Model defaults are based on empirical data from U.S. landfills.landgem uses the following first-order decomposition rate equation to estimate annual emissions over a time period that you specify. nn 1 QQ CCCC4 = kkll oo MM ii 10 ee kkkk iiii ii=1 jj =0,1 where QCH 4 = annual methane generation in the year of the calculation (m 3 /year) i = 1 year time increment n = (year of the calculation) - (initial year of waste acceptance) j = 0.1 year time increment k = methane generation rate (year-1) Lo = potential methane generation capacity (m3/mg) M i = mass of waste accepted in the ith year (Mg) t ij = age of the jth section of waste mass M i accepted in the i th year (decimal years, e.g.,3.2 years) QQ CCCC2 = QQ CCCC4 xx 1/ PP CCCC4 /100 1 The methane generation rate, k, determines the rate of methane generation for the mass of waste in the landfill. The higher the value of k, the faster the methane generation rate increases and then decays over time. The value of k is primarily a function of four factors: Moisture content of the waste mass, Availability of the nutrients for microorganisms that break down the waste to form methane and carbon dioxide, ph of the waste mass, and Temperature of the waste mass. 3 Results and discussion 3.1 Demographic Research Area Gubeng districts including both densely populated areas in Surabaya East with a population of people with the population density of people/km² (BPS Surabaya, 2011).A number of household in GubengDistrict is households and an average of 3.52 people per house. Detail of number of households and the average family size in Gubeng District is shown in Table 1. Table 1 Population and density of population in Gubeng District Surabaya Total population Population density Total Number of family Sub-district (people) (people/km²) Household members Mojo ,28 Kertajaya ,50 PucangSewu ,36 Barata Jaya ,41 Gubeng ,56 Airlangga ,32 Total ,52

4 Yevi Putri Agustia, Welly Herumurti 3.2 Waste composition Table 2 shows the variations in the solid waste compositions of residential, market and solid waste composition based on IPCC for Region South-Eastern Asia (IPCC, 2006). The ratio of the food waste in Gubeng District was significantly higher than Region South-Eastern Asia. However, the ratio of the paper waste was lower. It might due to the composting program in Gubeng District is not work very well, beside the consumption of community was still used raw material. Table 2 Solid waste composition Solid waste composition IPCC (Region South- Eastern Asia) Residential solid waste Market solid waste Plastics 7,20% 12,45% 4,24% Food waste 43,50% 57,87% 73,48% Garden Waste - 4,24% 16,60% Paper 12,90% 8,59% 2,54% Metal 3,30% 0,78% 0,21% Glass 4,00% 1,24% 0,15% Textiles 2,70% 2,90% 0,63% Rubber 0,90% 0,59% 0,04% Wood 9,90% 0,90% 1,15% Diapers - 9,25% 0,49% Harzardous - 0,45% 0,02% Other 16,30% 0,74% 0,48% Total 100% 100% 100% 3.3 Greenhouse gas potential Based on LandGEM US EPA Model 2005 methane potential from solid waste landfill was kg CH 4 /ton solid waste and carbon dioxide was kg CO 2 /ton solid waste. The highest methane production was from food waste, whereas the lowest were from wood waste. Waste component kg CH 4 generated per ton waste Food Garden Paper Wood Textile Nappies MSW The LandGEM has a disadvantage whereby it cannot allow for changes in the composition of individual waste (Scharff and Jacobs, 2006). The annual methane potential for individual waste landfilled estimated by LandGEM was more influenced by the change in the quantity of waste than the composition. The LandGEM has a disadvantage whereby it cannotallow for changes in the composition of individual waste as asingle default L0 was generally used (Cho et al, 2012).No model perfectly matches themethane recovery data but some models fare better than others.the LandGEM model consistently underestimated methane

5 The 4thInternational Seminar Department of Environmental Engineering generation,but all other models typically overestimated methane generation (Thompson et al., 2009). Methane potential from Gubeng solid waste was kg CH 4 /ton solid waste from IPCC model. 4 Conclusion Solid waste composition in GubengDistric were 58%, 4%, 9%, 1%, 3%, 9% and 14% of food, garden, paper, wood, textile, nappies, and plastic/other inert waste, respectively. Methane potential from Gubeng solid waste was kg CH 4 /ton solid waste. Whereas, based on LandGEM US EPA Model 2005 methane potential from solid waste landfill was kg CH 4 /ton solid waste and carbon dioxide was kg CO 2 /ton solid waste.the LandGEM model was underestimated in methane generation if compared with IPCC model. References BSN Indonesia Methods of sample collection and measurement of the composition and urban waste (Indonesian). Jakarta. Cho, H. S., Moon, H. S., Kim, J. Y Effect of quantity and composition of waste on the prediction of annual methane potential from landfills.bioresource Technology Couth, R., Trois, C., Vaughan-Jones, S Modelling of greenhouse gas emissions from municipal solid waste disposal in Africa.International Journal of Greenhouse Gas Control 5, IPCC IPCC Guidelines for National Greenhouse Gas Inventories, the National Greenhouse Gas Inventories Programme, IGES, Japan. Jha, A. K., Sharma, C., Singh, N., Ramesh, R., Purvaja, R., Gupta, P. K Greenhouse gas emissions from municipal solid waste management in Indian mega-cities: A case study of Chennai landfill sites. Chemosphere 71, Loureiro, S. M., Rovere, E. L. L., Mahler, C. F Analysis of potential for reducing emissions of greenhouse gases in municipal solid waste in Brazil, in the state and city of Rio de Janeiro.Waste Management 33, Maimoun, M. A., Reinhart, D. R., Gammoh, F. T., McCauley Bush, P Emissions from US waste collection vehicles. Waste Management 33, Scharff, H., Jacobs, J., Applying guidance for methane emission estimation for landfills.waste Management 26, Thompson, S., Sawyer, J., Bonam, R,.Valdivi, J.E Building a better methane generation model: Validating models with methanerecovery rates from 35 Canadian landfills.waste Management 29. ( US-EPA Landfill Gas Emissions Model (LandGEM) Version 3.02 User s Guide. Washington.