Assessment of methane emission and oxidation at Air Hitam Landfill site cover soil in wet tropical climate

Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 g m^?2 day^?1, respectively. The total CH₄ emissions from the studied area were 53.8 kg day^?1. The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year^?1. Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.     

Investigation of integrated municipal solid waste management strategies for Oman: an assessment of waste diversion,electricity generation and greenhouse-gas emissions

Journal of Material Cycles and Waste Management - The need of an integrated municipal solid waste (MSW) management system to maximize resource recovery and simultaneously reduce greenhouse-gas...     

Solid waste forecasting using modified ANFIS modeling

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R2). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R2 were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R2 = 0.98.

Implications: To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.     

Empirical gas emission and oxidation measurement at cover soil of dumping site: example from Malaysia

Methane (CH₄) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO₂), risking human health and the environment. Microbial CH₄ oxidation in landfill cover soils may constitute a means of controlling CH₄ emissions. The study was intended to quantify CH₄ and CO₂ emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH₄ oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH₄ to CO₂ emissions was 25.4 %, indicating higher CO₂ emissions than CH₄ emissions. Also, the average CH₄ oxidation in the landfill cover soil was 52.5 %. The CH₄ and CO₂ emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH₄ emissions and oxidation (R ²?=?0.46). It can be concluded that the variation in the CH₄ oxidation was mainly attributed to the properties of the landfill cover soil.     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.