Abstract Atmospheric ammonia is an important compound in the atmosphere because of its role in aerosol formation and its importance to the global nitrogen cycle. Livestock feeding operations are a major source of ammonia emissions to the atmosphere, and ammonia concentrations near these large feedlots can be many orders of magnitude higher than background. These feedlots can impact regional ecology and air quality, but uncertainty in the ammonia surface fluxes adjacent to these major sources can make the extent of the feedlot's impact difficult to determine. Ammonia surface fluxes are generally challenging to quantify with direct flux measurement techniques due to the challenges in conducting ammonia measurements with sufficient temporal resolution. Feedlots housing ruminant livestock such as cattle are also sources of methane. Because methane does not undergo appreciable dry deposition and is chemically inert on relevant spatial scales, we can use it as a tracer to constrain the downwind dilution of feedlot ammonia emissions. The ratio of atmospheric ammonia to methane has been shown to decrease with increasing distance downwind of a feedlot due to deposition and aerosol partitioning of gas-phase ammonia. In atmospheric conditions where inorganic aerosol formation is slow, the ratio of ammonia to methane can be used to estimate of the fraction of ammonia that has undergone deposition downwind of the feedlot. We use a Large-Eddy Simulation turbulent dispersion model to generate realistic concentration fields of atmospheric ammonia and methane concentrations downwind of a feedlot, and we sample the model output to produce synthetic measurements of these tracers. We then use the synthetic observations of background-corrected the ammonia:methane concentration ratio to derive the ammonia deposition rate. In this study, we explore conducting measurements from two platforms: sensors deployed on a small unmanned aerial vehicle and on a surface-based mobile observation platform. We find that the surface-based platform overestimates of ammonia deposition by a factor of 1.5 due to sampling near the surface where ammonia concentrations are depleted. However, use of the aerial platform allows more accurate estimation of the deposition fraction (relative error

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探索使用大气浓度测量估算沉积物的新方法：饲养场下游氨气的建模案例研究

摘要 大气中的氨是大气中的一种重要化合物，因为它在气溶胶形成中的作用以及对全球氮循环的重要性。牲畜饲养作业是向大气排放氨的主要来源，这些大型饲养场附近的氨浓度可能比背景高许多数量级。这些饲养场会影响区域生态和空气质量，但与这些主要来源相邻的氨表面通量的不确定性会使饲养场的影响程度难以确定。由于在进行具有足够时间分辨率的氨测量方面存在挑战，因此使用直接通量测量技术量化氨表面通量通常具有挑战性。饲养反刍牲畜（如牛）的饲养场也是甲烷的来源。由于甲烷不会发生明显的干沉降，并且在相关空间尺度上具有化学惰性，因此我们可以将其用作示踪剂来限制饲养场氨排放的顺风稀释。由于气相氨的沉积和气溶胶分配，大气中氨与甲烷的比率已显示出随着饲养场下风向距离的增加而降低。在无机气溶胶形成缓慢的大气条件下，氨与甲烷的比率可用于估计在饲养场顺风处沉积的氨的比例。我们使用大涡模拟湍流扩散模型来生成饲养场顺风处的大气氨和甲烷浓度的真实浓度场，我们对模型输出进行采样以生成这些示踪剂的合成测量值。然后，我们使用背景校正的氨：甲烷浓度比的合成观察来推导氨沉积率。在这项研究中，我们探索从两个平台进行测量：部署在小型无人机上的传感器和基于地面的移动观测平台上的传感器。我们发现，由于在氨浓度耗尽的地表附近采样，地表平台高估了氨沉积 1.5 倍。然而，使用空中平台可以更准确地估计沉积分数（相对误差 部署在小型无人机和地基移动观测平台上的传感器。我们发现，由于在氨浓度耗尽的地表附近采样，地表平台高估了氨沉积 1.5 倍。然而，使用空中平台可以更准确地估计沉积分数（相对误差 部署在小型无人机和地基移动观测平台上的传感器。我们发现，由于在氨浓度耗尽的地表附近采样，地表平台高估了氨沉积 1.5 倍。然而，使用空中平台可以更准确地估计沉积率（相对误差