M
Morton A. Barlaz
Researcher at North Carolina State University
Publications - 203
Citations - 18819
Morton A. Barlaz is an academic researcher from North Carolina State University. The author has contributed to research in topics: Municipal solid waste & Leachate. The author has an hindex of 55, co-authored 199 publications receiving 15571 citations. Previous affiliations of Morton A. Barlaz include University of Texas at Arlington & RTI International.
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Quantifying the Greenhouse Gas Emission Reductions Associated with Recycling Hot Mix Asphalt
TL;DR: In this paper, a life cycle inventory model was developed to evaluate three alternatives for the management of waste hot mix asphalt (HMA) including, (1) recycling as new aggregate, (2) recycling of new HMA, and (3) disposal in a landfill.
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Improved methodology to assess modification and completion of landfill gas management in the aftercare period
TL;DR: The improved gas module for theEPCC methodology emphasizes the reduction or elimination of fugitive methane by considering the methane oxidation capacity of the cover system, and allows for the installation of biologically active covers or other features designed to enhance methane oxidation.
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Finite-Element Modeling of Landfills to Estimate Heat Generation, Transport, and Accumulation
TL;DR: In North America, temperatures nearing 100°C have been reported in several municipal solid waste landfills as discussed by the authors, and the temporal and spatial-dependent processes that result in excessive he...
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Characterizing the biotransformation of sulfur-containing wastes in simulated landfill reactors
TL;DR: The objective of this study was to develop and demonstrate a laboratory-scale reactor method to measure the H2S production potential of a range of sulfur-containing wastes and to demonstrate the magnitude of the effect varied in reactors that were sparged with N2.
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Approaches to fill data gaps and evaluate process completeness in LCA—perspectives from solid waste management systems
TL;DR: A systematic evaluation of process completeness, identification of data gaps, and application of surrogate values to fill the gaps is suggested to take into account the relevance and importance of flows.