Landfill gas emission prediction using Voronoi diagrams and importance sampling
Abbreviated Journal Title
Environ. Modell. Softw.
MSW landfill; Least squares; Air dispersion modeling; Kriging; Delaunay; tessellation; Voronoi diagram; MODEL; VALIDATION; FLUX; Computer Science, Interdisciplinary Applications; Engineering, ; Environmental; Environmental Sciences
Municipal solid waste (MSW) landfills are among the nation's largest emitters of methane, a key greenhouse gas, and there is considerable interest in quantifying the surficial methane emissions from landfills. There are limitations in obtaining accurate emissions data by field measurements, and in characterizing an entire landfill with only a few such emissions measurements. This paper proposes an emissions prediction approach using numerous ambient air volatile organic compound (VOC) measurements above the surface of a landfill that are more easily obtained. Many large landfills are already collecting ambient air methane data based on existing regulations. The proposed method is based on the inverse solution of the standard Gaussian dispersion equations. However, only the VOC concentrations and locations are required. The locations of maximum likelihood of the point sources are predicted using Voronoi diagrams, and importance sampling is performed to further refine the locations. Point source strengths are calculated using non-negative least squares, and the point emission rates are then summed to give the total landfill emission rate. The proposed method is successfully demonstrated on a series of four landfill case studies. Three hypothetical landfills were selected for validation studies by forward and backward solution of the dispersion equations. The fourth case study is an active central Florida MSW landfill. The proposed method shows promise in accurately and robustly predicting landfill gas emissions, and requires only measured ambient VOC concentrations and locations. (C) 2009 Elsevier Ltd. All rights reserved.
Environmental Modelling & Software
"Landfill gas emission prediction using Voronoi diagrams and importance sampling" (2009). Faculty Bibliography 2000s. 1855.