Quantification of Atmospheric Ammonia Concentrations: A Review of Its Measurement and Modeling
Quantification of Atmospheric Ammonia Concentrations: A Review of Its Measurement and Modeling
复制标题
DOI：
10.20944/preprints202008.0468.v1
复制
发表时间：
2020-08
期刊：
影响因子：
2.9
通讯作者：
A. Nair;F. Yu
中科院分区：
文献类型：
--
作者：
A. Nair;F. Yu
氨（NH3）是大气中最普遍的碱性气体，在PM2.5形成、大气化学和新颗粒物形成中起着重要作用。本文综述了通过测量、卫星遥感和建模对[NH3]进行量化的研究，这些研究在500多份出版物中报道，旨在综合[NH3]的现有知识，重点关注时空变化、控制过程和量化问题。大多数测量是通过区域被动采样器网络进行的。[NH3]热点通常位于农业地区，如美国中西部和华北平原，浓度升高，月平均值分别达到20和74 ppbv。在印度-恒河平原、印度北部和美国圣华金河谷，地形影响显著增加[NH3]。测量在海洋上是稀疏的，其中[NH3]超过几十pptv，其变化可以影响气溶胶的形成。自2002年以来，卫星遥感（AIRS，CrIS，IASI，TANSO-FTS，TES）提供了柱和表面的全球[NH3]定量。建模是至关重要的，以提高对NH3的化学和运输，其时空变化，源解析，探索物理化学机制，并预测未来的情况。GEOS-Chem（全球）和FRAME（联合王国）模型通常用于此。参与式建模是为了更好地了解大气中的氨，这是从人类健康和生态系统的角度关注的一个协同的测量方法。
Ammonia (NH3), the most prevalent alkaline gas in the atmosphere, plays a significant role in PM2.5 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.