DOI: https://doi.org/10.14710/presipitasi.v0i0.%25p

Forecasting Methane Emission Reduction through 3R Waste Treatment Facility: The Case of Janti-Sidoarjo

*Muhamad Khafid Rifai  -  Universitas Brawijaya, Indonesia
Hartati Kartikaningsih  -  Universitas Brawijaya, Indonesia
Agus Susilo  -  Universitas Brawijaya, Indonesia
Yoga Sita Kristama  -  University of Waikato, New Zealand

Citation Format:
Abstract
Municipal solid waste (MSW) generation contributes to methane emission, formed during anaerobic decomposition of organic. The MSW reduction can be forced into an integrated solid waste management site through 3R paradigm built in each municipal level area. This is because methane has a relatively short atmospheric lifetime of about 12 years compared to CO2. This means that reducing methane can quickly impact slowing global warming in the short term. This study aimed to achieve maximum results in reduction methane emission generated from an urban area through 3R waste treatment facility with measurable evaluation in 3 scenarios. The method used to gather the data analysis is Minitab 22 software, which forecasts an approach through trend method analysis, moving average, single exponential smoothing, and double exponential smoothing model to obtain the best-fit model for scenario analysis. The result showed that trend analysis has a high accuracy category with the smallest error (<10%: strong accuracy) in BaU, scenario 1, scenario 2, and scenario 3 with the MAPE value 0.002071 (0.2%), MAD value 0.00, and MSD value 0.00. Meanwhile, scenario 3 could decrease the amount of waste sent to landfills 188.03 tons/year in 2024 and 203.53 tons/year in 2033. Whilst, it could reduce methane up to 0.829126 tons-CO2-eq /year in 2024 and 0.897479 tons-CO2-eq /year in 2033 from total waste in Janti or 8-10% from the total methane emissions in Jabon landfill (65683,8 tons-CO2-eq /year). It concluded that optimizing the activities of 3R waste management facility units in each area, particularly in Sidoarjo, can significantly reduce the effect of methane emissions that arise in landfills.
Fulltext View|Download
Keywords: Methane emission; municipal solid waste; sustainable waste management; 3R waste treatment facility

Article Metrics:

Article Info
Section: Regional Case Study
Language : EN
Recent articles
Reclaiming Former Coal Mines with Goat Manure and Rice Husk Charcoal for Oil Palm Growth The Efficacy of Anaerobic Biofilter and Pre-Aeration with Microbubble Generator for Tofu Wastewater Treatment Optimizing Nutrient Removal in Agriculture Wastewater Using Electrocoagulation Technology Forecasting Methane Emission Reduction through 3R Waste Treatment Facility: The Case of Janti-Sidoarjo Impact of Electric Vehicle Transition Scenarios on Road Transport Emission in Semarang City Spatial Modeling for River Quality Assessment to Enhance Sustainable Water Resource Management Regulations More recent articles
Most cited articles
ELECTRICITY GENERATION FROM LANDFILL GAS KUALITAS UDARA DALAM RUANG DI DAERAH PARKIR BASEMENT DAN PARKIR UPPERGROUND (STUDI KASUS DI SUPERMARKET SEMARANG) Kajian Dampak Rencana Pembangunan Kawasan Wisata Marina di Pesisir Kabupaten Belitung terhadap Kualitas Lingkungan Sekitarnya Inventarisasi dan Sebaran Emisi Aktivitas Pelabuhan dengan Aermod View PERKEMBANGAN BIOFILM NITRIFIKASI DI FIXED BED REACTOR PADA SALINITAS TINGGI More cited articles
  1. Armi, & Mandasari, D. (2017). Managing Organic Waste into Methane Gas. Serambi Saintia, 5(1), 1–11
  2. Artiningrum, T. (2017). Methane (CH4) Emission Potential from Waste Generation in Bandung City. Geoplanart, 1(1), 36–44
  3. Azman, M. M. (2019). Comparative analysis of moving average and exponential smoothing accuracy values for revenue forecasting systems at XYZ company. Jurnal Sistem and Informatika, 13(2), 36–45
  4. Bian, B., Shen, Y., Hu, X., Tian, G., & Zhang, L. (2020). Reduction of sludge by a biodrying process: Parameter optimization and mechanism. Journal Chemosphere, 248
  5. BPS. (2023). Regional Statistics of East Java Provincer. BPS-Statistics
  6. BPS Sidoarjo. (2022). Waru sub-district in 2022. BPS-Statictics Sidoarjo
  7. BPS Sidoarjo. (2023). Kecamatan Waru dalam angka 2023. BPS-Statistics Sidoarjo
  8. Budihardjo, M. A., Ardiansyah, S. Y., & Ramadan, B. S. (2022). Community-driven material recovery facility (CdMRF) for sustainable from Semarang City. Journal Habitat International, 119
  9. Budihardjo, M. A., Humaira, N. G., Putri, S. A., Ramadan, B. S., Syafrudin, S., & Yohana, E. (2021). Sustainable solid waste management strategies for higher education institutions: Diponegoro university, Indonesia case study. Sustainability (Switzerland), 13(23)
  10. Budihardjo, M. A., Humaira, N. G., Ramadan, B. S., Wahyuningrum, I. F. S., & Huboyo, H. S. (2023). Strategies to reduce greenhouse gas emissions from municipal solida waste management in Indonesia: The case of Semarang City. Alexandria Engineering Journal, 67, 771–783
  11. Cahyani, P. P., Rini, I., Ari, D., & Wijayanti, W. P. (2020). Potential for Waste Reduction in TPST Janti, Waru District, Sidoarjo Regency. 9(1), 163–170
  12. Candradevi, A., & Puspitasari, N. . (2016). The Application of Material Required Planning (MRP) by Considering Lot Sizing in Raw Material Control at PT. Phapros, Tbk. PERFORMA: Media Ilmiah Teknik Industri, 15(1), 77–86. https://doi.org/10.20961/performa.15.1.13760
  13. Darajati, W. (2012). “Implementation of the National Action Plan to Reduce Greenhouse Gas Emissions”. Climate Change Panel Discussion
  14. Darmawan, D. A. (2018). Greenhouse Gas Emission Reduction Potentioal from Waste Bank Sector in Yogyakarta using IPCC Method. Universitas Islam Indonesia
  15. Demir, C., Yetiş, Ü., & Ünlü, K. (2019). Identification of waste management strategies and waste generation factors for thermal power plant sector wastes in Turkey. Waste Management and Research, 37(3), 210–218
  16. Dewi, R. G. (2011). Report of the latest NCs (Inventories) Recently Submitted Indonesia’s Second Nasional Communication, the 9th (2011) on GHG Inventories in Asia (WGIA9), Capacity building for measurability, reportability and verifiability, Phnom Penh
  17. Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., & Tanabe, K. (2006). IPCC Guidelines for National Greenhouse Gas Inventories: Volume 5 –Waste, Prepared by the national Greengouse Gas Invenstories Programme. IGES
  18. EPA. (2020). Global Methane Emissions and Mitigation Opprotunities. Environmental Protection Agency
  19. Fadzoli, T., Subekti, R., & Waluyo. (2023). The Impact of Waste Management Policy as a Parameter of Government Performance in Environmental Sector. Jurnal Ilmu Hukum dan Administrasi Negara, 1(3), 28–36
  20. Gaol, L., & Warmadewanthi, I. D. D. A. (2017). Lumban Gaol, M., & Warmadewanthi, I. D. A. A. (2017). Prediction of Environmental Impacts of Waste Management in Jabon Landfill, Sidoarjo Regency. Jurnal Teknik ITS, 6(2), 462–466. https://doi.org/10.12962/j23373539.v6i2.25127
  21. Goh, E., Esfandiar, K., Jie, F., Brown, K., & Djajadikerta, H. (2022). Please sort out your rubbish! An integrated structural model approach to examine antecedents of residential households’ waste
  22. separation behaviour. Journal of Cleaner Production, 355, 1–9
  23. Hermawati, W., Hartiningsih., Maulana, I., Wahyono, S., & Purwanto, W. (2015). Waste Management and Utilization in Urban Areas. Graha Ilmu
  24. Hyndman, R. J., & Athanasopoulos, G. (2013). Forecasting: Principles and Practice. Otexts
  25. IPCC. (2006). IPCC Guidelines for National Greenhouse Gas Inventories: Volume -5 Waste, Prepared by the National Greenhouse Gas Inventories Programme (H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, & K. Tanabe (eds.)). IGES
  26. Jain, P. K., Quamer, W., & Pamula, R. (2018). Electricity Consumption Forecasting Using Time Series Analysis. Springer Nature Singapore, 327–335. https://doi.org/https://doi.org/10.1007/978-981-13-1813-9_33
  27. Jiang, W., Wu, X., Y., G., Yu, W., & Zhong, X. (2020). Holt-Winters Smoothing Enhanced by Fruit Fly Optimization Algorithm to Forecast Monthly Electricity Consumption. Journal Energy, 193. https://doi.org/https://doi.org/10.1016/j.energy.2019.116779
  28. Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: a global snapshot of solid waste management to 2050. Urban Development
  29. Kittipongvises, S., & Vassanadumrongdee, S. (2018). Factors influencing source separation intention and willingness to pay for improving waste management in Bangkok, Thailand. Journal Sustainable Environment Research, 28(2), 90–99
  30. Koderi, K., Suyadi, S., Said, A., & Muhaimin, A. W. (2018). Knowledge, Action, Perception and Attitude of Management of Talangagung Landfill toward Edu-Tourism Program: A Community Perspective
  31. Journal of Indonesian Tourism and Development Studies, 6(1), 41–48
  32. Kristanti, D. (2015). Forecasting the Amount of Fuel Oil Distribution in PT Pertamina (Persero) Region III Depot Malang Using Winter Method and Decomposition Method. Jurnal Matematika, 1(2), 52–67
  33. Kristanto, G. A., & Koven, W. (2019). Estimating Greenhouse Gas Emissions From Municipal Solid Waste Management in Depok, Indonesia. City and Environment Interactions, 4, 1–8
  34. Gaol,M.L., & Warmadewanthi, I.D.A.A. (2017). Prediction of Environmental Impacts of Waste Management in Jabon Landfill, Sidoarjo Regency. Jurnal Teknik ITS, 6(2). https://doi.org/10.12962/j23373539.v6i2.25127
  35. Lee, U., Han, J., & Wang, M. (2017). Evaluation of land fi ll gas emissions from municipal solid waste land fi lls for the life-cycle analysis of waste-to-energy pathways. 166, 0–7
  36. Lewis, C. D. (1982). Industrial and Business Forecasting Methodes: A Potential Guide to Exponential Smoothing and Curve Fitting. London ; Boston : Butterworth Scientific
  37. Lu, H. R., Qu, X., & EL Hanandeh, A. (2020). Towards a better environment - the municipal organic waste management in Brisbane: Environmental life cycle and cost perspective. Journal of Cleaner Production, 258(120756). https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120756
  38. Luo, R., Wang, J., & Gates, I. (2024). Forecasting Methane Data Using Multivariate Long Short-Term Memory Neural Networks. Environmental Modeling and Assessment, 29(3), 441–454. https://doi.org/10.1007/s10666-024-09957-x
  39. Makridakis, S., Wheelwright, C., & Hyndman, R. (1978). Forecasting: Methods and Applications. John Wiley & Sons, Inc
  40. Malik, A., Khokhar, M., Hussain, E., & Baig, S. (2020). Forecasting CO2 emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor
  41. Greenhouse Gases: Science and Technology, 10(2), 380–289. https://doi.org/https://doi.org/10.1002/ghg.1968
  42. Mariani, T., & Rosyida, I. (2023). Implemtasi Metode Double Exponential Smoothing untuk Peramalan Luas Panen Padi di Kabupaten Pati dengan Bantuan Software Minitab 16. PRISMA: Prosiding Seminar Nasional Matematika 6, 707–713. https://journal.unnes.ac.id/sju/index.php/prisma/
  43. Meidiana, C., Sekito, T., & Sasongko, W. (2021). Determining factors of community participation on waste bank. Earth and Environmental Science
  44. Mustafa, A. B., Dong, H., Zhang, C., & Fuji, M. (2022). Life cycle environmental benefit and waste to energy potential of municipal solid waste management scenarios in Indonesia. Jurnal of Material Cycles and Waste Management, 24, 1859–1877. https://doi.org/http://dx.doi.org/10.1007/s10163-022-01441-6
  45. Nagong, A. (2021). Study of Waste Management by DLH Samarinda City Based on Regional Regulation No. 02 2011 Concerning Waste Management. Jurnal Administrative Reform, 8(2), 105. https://doi.org/10.52239/jar.v8i2.4540
  46. Nevrlý, V., Šomplák, R., Putna, O., & Pavlas, M. (2019). Location of mixed municipal waste treatment facilities: Cost of reducing greenhouse gas emissions. Journal Cleaner Production, 239(118003). https://doi.org/https://doi.org/10.1016/j.jclepro.2019.118003
  47. Ooi, J. ., Woon, K. ., & Hasyim, H. (2021). A multi-objective model to optimize country-scale municipal solid waste management with economic and environmental objectives: A case study in Malaysia
  48. Journal of Cleaner Production, 316(128366). https://doi.org/https://doi.org/10.1016/j.jclepro.2021.128366
  49. Prakoso, M. B. A., & Gunarta, I. K. (2021). Feasibility Study of Municipal Solid Waste Management System
  50. (MSW-MS) in Sidoarjo District. Jurnal Teknik ITS, 9(2). https://doi.org/10.12962/j23373539.v9i2.55496
  51. Premakumara, D. G. J., Menikpura, S. N. M., Singh, R. K., Hengesbaugh, M., Magalang, A. A., Ildefonso, E. T., Valdez, M. D. C. M., & Silva, L. C. (2018). Reduction of greenhouse gases (GHGs) and short-lived climate pollutants (SLCPs) from municipal solid waste management (MSWM) in the Philippines: Rapid review and assessment. Waste Management, 80, 397–405
  52. Ramachandra, T. V, Bharath, H. A., Kulkarni, G., & Sheng, S. (2018). Municipal solid waste : Generation , composition and GHG emissions in Bangalore , India. 82(September 2017), 1122–1136
  53. Ratih, A., Lanti, Y., & Prabang, R. D. (2015). The Effect of Exposure Methane (CH4), Carbondioxide (CO2) and Hydrogen Sulfide (H2S) on the Respiratory Disorders of Scanvenger in Final Disposal Site Klotok Kediri. Jurnal Ekosains, 105–116
  54. Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map based scenarios with TOPSIS. Expert Systems with Aplications, 39(3), 2443–2450. https://doi.org/https://doi.org/10.1016/j.eswa.2011.08.094
  55. Sauve, G., & Van Acker, K. (2020). The environmental impacts of municipal solid waste landfills in Europe:
  56. A life cycle assessment of proper reference cases to support decision making. Journal of
  57. Environmental Management, 261(110216). https://doi.org/https://doi.org/10.1016/j.jenvman.2020.110216
  58. Sugiyono. (2017). Business Research Methods: Quantitative, Qualitative, Combined, dan r&d,” 3rd ed. Alfabeta
  59. Tsai, F. M., Bui, T.-D., Tseng, M.-L., Lim, M. ., & Hu, J. (2020). Municipal Solid Waste Management in Circular Economy: A data-driven bibliometric analysis. Journal of Cleaner Production, 275(124132). https://doi.org/10.1016/j.jclepro.2020.124132
  60. Tudor, C., & Sova, R. (2021). Benchmarking ghg emissions forecasting models for global climate policy. Electronics (Switzerland), 10(24), 1–28. https://doi.org/10.3390/electronics10243149
  61. Xu, L., Ling, M., Lu, Y., & Shen, . (2017). Understanding household waste separation behaviour: testing the roles of moral, past experience, and perceived policy effectiveness within the theory of planned behaviour. Sustainability (Switzerland), 625
  62. Yao, X., Guo, Z., Liu, Y., Li, J., & Gao, Y. (2019). Reduction potential of GHG emissions from municipal solid waste incineration for power generation in Beijing. Journal of Cleaner Production, 241(118283). https://doi.org/https://doi.org/10.1016/j.jclepro.2019.118283
  63. Zhou, Z., Wang, L., L., A., Zhang, M., & Niu, Z. (2019). Innovative Trend Analysis of Solar Radiation in China during 1962-2015. Journal Renewable Energy, 119, 675–689
  64. Zubair, A., & Haeruddin. (2011). Studi Potensi Daur Ulang Sampah di TPA Tamanggapa Kota Makasar

Last update:

No citation recorded.

Last update: 2024-12-03 16:04:10

No citation recorded.