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Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

This paper presents a review of carbon dioxide (CO2) emissions from transportation in an attempt to establish a quick and suboptimal update of the methods used to calculate and analyze CO2 emissions from transportation. Transportation is the largest contributor to air pollution through the release of high amounts of CO2 gas into the atmosphere. The methods for calculating and analyzing the carbon footprint of transportation; which is of critical importance in the management of greenhouse gases that contribute to global warming; are still being developed. However; there are some differences in the definitions and methods used to calculate the carbon footprint of transportation in previous studies. This review focuses on the similarities of the methods used to measure CO2 emissions as well as the analyses used to evaluate the emissions. This paper will also highlight the advantages and limitations of each research work. By doing this; the present study contributes to the selection of appropriate methods for calculating CO2 emissions from transportation and draws attention to environmental issues. It is hoped that the implementation of the most appropriate framework will help to reduce CO2 emissions from transportation

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The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.

The increase in solid waste generation is caused primarily by the global population growth that resulted in urban sprawl, economic development, and consumerism. Poor waste management has adverse impacts on the environment and human health. The recent years have seen increasing interest in using black soldier fly (BSF), Hermetia illucens, as an organic waste converter. Black soldier fly larvae (BSFL) feed voraciously on various types of organic waste, including food wastes, agro-industrial by-products, and chicken and dairy manure, and reduce the initial weight of the organic waste by about 50% in a shorter period than conventional composting. The main components of the BSFL system are the larvero, where the larvae feed and grow, and the fly house, where the adults BSF live and reproduce. It is essential to have a rearing facility that maintains the healthy adult and larval BSF to provide a sufficient and continuous supply of offspring for organic waste treatment. The BSF organic waste processing facility consists of waste pre-processing, BSFL biowaste treatment, the separation of BSFL from the process residue, and larvae and residue refinement into marketable products. BSFL digest the nutrients in the wastes and convert them into beneficial proteins and fats used to produce animal feed, and BSFL residue can be used as an organic fertilizer. This review summarizes the BSFL treatment process to provide an in-depth understanding of the value of its by-products as animal feed and organic fertilizer.

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The Analytic Hierarchy Process (AHP) has been used widely to solve multi-criteria selection problem. It is a technique that allows the decision makers to set their priorities and help make the best selection when both tangible and intangible aspects need to be considered. This study uses the AHP to select the best composting technology for the UKM composting centre where the accumulation of organic wastes are generated daily from the cafeteria and landscape activities within the UKM campus. Experts who are familiar and who have some years of experience on solid waste management at UKM were interviewed to do the pair wise comparisons which are structured with four criteria namely environmental, economy, social and technical aspects. These criteria then expanded into a few more sub-criteria. The alternatives for the composting technology are windrow composting and in-vessel composting. The analysis is done using the Super Decisions software. The result shows that technical factor is the most important factor with (0.5000), followed by environmental (0.2517), economy (0.1941) and social (0.0542) factors. The end result shows that windrow composting is the best composting technology according to these four factors with the priority of 0.6236 while composting in—vessel has the priority of 0.3765.