Title

Landfill Gas Emission Prediction Using Voronoi Diagrams And Importance Sampling

Keywords

Air dispersion modeling; Delaunay tessellation; Kriging; Least squares; MSW landfill; Voronoi diagram

Abstract

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. © 2009 Elsevier Ltd. All rights reserved.

Publication Date

10-1-2009

Publication Title

Environmental Modelling and Software

Volume

24

Issue

10

Number of Pages

1223-1232

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.envsoft.2009.04.003

Socpus ID

67349261227 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/67349261227

This document is currently not available here.

Share

COinS