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.

中文翻译：

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使用表面羽化监测（SEM），无人机排放监测（DEM）和顺风羽状排放监测（DWPEM）获得的反羽建模，比较对垃圾掩埋场甲烷排放的估算。

作为量化和管理人为温室气体排放量的全球努力的一部分，人们对量化城市固体垃圾填埋场中的甲烷排放量非常感兴趣。当前，各种分析和实验方法都用于此任务。在本文中，基于优化的估计方法被用来评估逃逸垃圾填埋场甲烷排放量。该方法将反羽建模与环境空气中甲烷浓度测量结合在一起。测试并比较了三种不同的测量方法。该方法与地面排放监测（SEM），地上无人驾驶排放监测（DEM）和顺风羽流排放监测（DWPEM）相结合。该方法首先在人工生成的案例研究中使用合成数据集进行了试验和验证。还提供了一项现场研究，其中SEM，测试和比较了DEM和DWPEM。根据使用的测量类型，在两天的测量活动中，甲烷通量估计为228至350 g / s。与SEM相比，使用无人机系统（UAS）可以快速，全面地覆盖现场。但是，正如通过这项工作所表明的那样，基于DEM的甲烷采样技术的进步取决于与UAS兼容的测量仪器的进步。在没有有关垃圾填埋场主要泄漏点位置信息的情况下，与SEM和DEM相比，顺风羽流排放监测导致的通量估计值较小。即使这样，该方法对于掩埋场甲烷筛查也是简单快捷的。最后，最初为SEM开发的基于优化的方法，与基于无人机的采集数据和顺风浓度测量结果相结合时，显示出令人鼓舞的结果。研究的案例还发现了所研究的抽样策略的局限性，这些局限性被用于确定改进策略和建议，以更有效地评估逃犯性垃圾填埋场的甲烷排放量。