LASMAP, Ecole Polytechnique de Tunisie, University of Carthage, Tunisia.
Department of Civil and Environmental Engineering, Florida State University, Tallahassee, FL, USA.
J Air Waste Manag Assoc. 2020 Apr;70(4):410-424. doi: 10.1080/10962247.2020.1728423. Epub 2020 Mar 9.
As part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.
作为量化和管理人为温室气体排放的全球努力的一部分，人们对量化城市固体废物填埋场中的甲烷排放非常感兴趣。目前有多种分析和实验方法用于这项任务。本文采用基于优化的估计方法来评估逸散性垃圾填埋场甲烷排放。该方法将逆羽流建模与环境空气中甲烷浓度测量相结合。测试并比较了三种不同的测量方法。该方法与表面排放监测（SEM）、地面无人机排放监测（DEM）和下风羽流排放监测（DWPEM）相结合。该方法首先在手动案例研究中使用合成数据集进行了试用和验证。还提出了一项现场研究，其中测试和比较了 SEM、DEM 和 DWPEM。根据使用的测量类型，两天测量期间的甲烷通量估计值在 228 至 350 g/s 之间。与 SEM 相比，使用无人机系统（UAS）可以快速全面地覆盖整个场地。然而，正如这项工作所表明的那样，基于无人机的甲烷采样的进展受到 UAS 兼容测量仪器进展的限制。下风羽流排放监测导致的估计通量小于 SEM 和 DEM，因为没有关于垃圾填埋场主要泄漏点位置的信息。尽管如此，该方法对于垃圾填埋场甲烷筛选仍然简单快速。最后，最初为 SEM 开发的基于优化的方法在与基于无人机收集的数据和下风浓度测量相结合时显示出有希望的结果。研究案例还发现了所研究采样策略的局限性，这为更有效地评估逸散性垃圾填埋场甲烷排放提供了改进策略和建议。
