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Articles by M.F.M. Abushammala
Total Records ( 2 ) for M.F.M. Abushammala
  M.F.M. Abushammala , N.E.A. Basri , H. Basri , A.A.H. Kadhum and A.H. El-Shafie
  Decomposition of municipal solid waste in landfills under anaerobic conditions produces gas containing approximately 50-60% methane (CH4) and 30-40% carbon dioxide (CO2) by volume. CH4 is one of the most important greenhouse gases because its global warming potential is more than 21 times CO2, which has adverse effects on the environment and human life. The CH4 emission from landfills is continually increasing due to increasing population growth and per capita waste generation. This study attempted to assess, in quantitative terms, the amount of CH4 that would be emitted from landfills in Malaysia over the years 1981-2024 using the Inter-governmental Panel on Climate Change 2006 First Order Decay Model. Furthermore, it tends to assess the effects of landfill gas collection system and waste recycling on CH4 emission. In order to attain accurate CH4 emission estimation, waste generation estimation over the years 1981-2024 were performed in two scenarios. Each scenario was used by the model to estimate CH4 emission either taken into account CH4 capturing amounts and increasing waste recycling over the study period or not, to evaluate their effect on CH4 emission reductions. Based on this, global CH4 emission in 2024, included 1,078 and 1,365 Gg CH4 emission reduction from the emission estimated using the first and the second waste generation scenarios, respectively, which indicated that increasing landfill gas collection system projects and amount of waste recycling provide greatest potential for controlling CH4 emission from landfills.
  M.F.M. Abushammala , N.E.A. Basri and M.K. Younes
  Methane (CH4) emissions from landfills are continually increasing due to population growth and growing per capita waste generation. Microbial CH4 oxidation in landfill cover soils might provide a means of controlling CH4 emissions. This study proposes an Artificial Neural Network (ANN) approach to predict CH4 oxidation in sandy landfill cover soils based on the soil moisture content, soil temperature and oxygen (O2) concentration at a depth of 10 cm in the cover soil. The optimum ANN model giving the lowest Mean Square Error (MSE) was trained with the Levenberg-Marquardt algorithm and comprised of three layers, with 50 and 20 neurons at the first and the second hidden layers, respectively and a logistic sigmoid (logsig) transfer function between the hidden and output layers. This study revealed that the ANN oxidation model can predict CH4 oxidation with a MSE of 0.001475 and a coefficient of determination (R2) between the measured and predicted outputs of up to 0.91. In conclusion, the ANN oxidation model provides an effective tool for predicting the percentage of CH4 oxidized in landfill cover soil.
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