Ammonia (NH3), the most prevalent alkaline gas in the atmosphere, plays a significant role in PM_2.5 formation, atmospheric chemistry, and new particle formation. This paper reviews quantification of [NH3] through measurements, satellite-remote-sensing, and modeling reported in over 500 publications towards synthesizing the current knowledge of [NH3], focusing on spatiotemporal variations, controlling processes, and quantification issues. Most measurements are through regional passive sampler networks. [NH3] hotspots are typically over agricultural regions, such as the Midwest US and the North China Plain, with elevated concentrations reaching monthly averages of 20 and 74 ppbv, respectively. Topographical effects dramatically increase [NH3] over the Indo-Gangetic Plains, North India and San Joaquin Valley, US. Measurements are sparse over oceans, where [NH3] ≈ a few tens of pptv, variations of which can affect aerosol formation. Satellite remote-sensing (AIRS, CrIS, IASI, TANSO-FTS, TES) provides global [NH3] quantification in the column and at the surface since 2002. Modeling is crucial for improving understanding of NH3 chemistry and transport, its spatiotemporal variations, source apportionment, exploring physicochemical mechanisms, and predicting future scenarios. GEOS-Chem (global) and FRAME (UK) models are commonly applied for this. A synergistic approach of measurements↔satellite-inference↔modeling is needed towards improved understanding of atmospheric ammonia, which is of concern from the standpoint of human health and the ecosystem.

中文翻译：

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大气氨浓度的量化：其测量和建模的回顾

氨（NH3）是大气中最普遍的碱性气体，在PM _2.5中起着重要作用形成，大气化学和新粒子形成。本文通过测量，卫星遥感和建模等500多个出版物中对[NH3]的定量进行综述，以综合[NH3]的当前知识，重点是时空变化，控制过程和定量问题。大多数测量是通过区域无源采样器网络进行的。[NH3]热点通常位于农业区域，例如美国中西部和华北平原，其浓度升高分别达到每月平均20 ppbv和74 ppbv。在印度北部的印度恒河平原和美国的圣华金河谷，地形影响急剧增加[NH3]。在[NH3]≈几十个pptv的海洋上，测量稀疏，其变化会影响气溶胶的形成。自2002年以来，卫星遥感技术（AIRS，CrIS，IASI，TANSO-FTS，TES）在色谱柱和地表进行了全球[NH3]定量分析。建模对于增进对NH3化学和运移，时空变化，来源的理解至关重要。分摊，探索理化机制并预测未来情况。GEOS-Chem（全球）和FRAME（英国）模型通常用于此目的。为了更好地了解大气中的氨，需要一种测量，卫星推论和建模的协同方法，这从人类健康和生态系统的角度来看是值得关注的。其时空变化，源分配，探索物理化学机制以及预测未来情景。GEOS-Chem（全球）和FRAME（英国）模型通常用于此目的。为了更好地了解大气中的氨，需要一种测量，卫星推论和建模的协同方法，这从人类健康和生态系统的角度来看是值得关注的。其时空变化，源分配，探索物理化学机制以及预测未来情景。GEOS-Chem（全球）和FRAME（英国）模型通常用于此目的。为了更好地理解大气中的氨，需要一种测量，卫星推论和建模的协同方法，这从人类健康和生态系统的角度来看是值得关注的。