Monitoring air quality from space: fast retrievals of short-lived atmospheric pollutants and precursors

气候变化和空气质量是我们一生中最紧迫的环境问题，它们是密不可分的。氮氧化物(主要通过燃烧过程产生)和挥发性有机化合物是对流层臭氧的前体，臭氧是一种强烈的温室气体，也是气候辐射强迫(仅次于二氧化碳和甲烷)的最大组成部分之一。它们也是二次气溶胶的前体，也被称为颗粒物，是最有害的空气污染形式，特别是如果直径小于2.5微米(PM2.5)，并能够深入肺部、血流和大脑，导致健康不良和过早死亡。这些气溶胶具有反射性，将太阳辐射散射回太空，并倾向于对气候产生降温作用。因此，改善空气质量的努力可能会导致气温进一步上升。另一方面，这些温度升高将导致与对流层臭氧形成相关的化学变化。气温升高还将导致植物排放的VOCs增加，从而提供更多的臭氧和PM2.5前兆。一些低辐射噪声和高光谱分辨率的大气探测仪测量热红外光谱区域的上升辐射；根据这些测量，我们可以确定许多短期污染物和前体的浓度，例如氨，主要来自农业来源(主要是氨基化肥和动物粪便)，它对PM2.5的形成有重要贡献；异戊二烯，植被排放的主要生物挥发性有机化合物，它具有化学反应，导致对流层臭氧和二次气溶胶的产生；以及火灾产生的大量污染物，如甲醇、甲酸、过氧乙酰硝酸盐、乙烯和乙炔。我们生活在一个以卫星为基础的大气成分测量变得越来越普遍的时代。可获得的数据量不断增加，未来只会增加，这将需要更智能、更快的计算方法来从这些观测中提取有意义的信息。传统的基于逐行辐射传输模型的反演方法速度较慢，但精度较高。挑战是在显著提高速度的同时保持这种准确性。

Climate change and air quality are the most pressing environmental issues of our lifetime, and they are inextricably linked.Nitrogen oxides (primarily produced through combustion processes) and volatile organic compounds are precursors of tropospheric ozone, a strong greenhouse gas and one of the largest components of the radiative forcing of climate (after carbon dioxide and methane). They are also precursors of secondary aerosols, also known as particulate matter, the most harmful form of air pollution, particularly if smaller than 2.5 micron in diameter (PM2.5) and are able to penetrate deep into the lungs, blood streams and brain, leading to ill health and premature death. These aerosols are reflective, scattering solar radiation back to space, and tend to have a cooling effect on climate. Therefore, efforts to improve air quality will likely lead to further increases in temperature. On the other hand, these temperature increases will lead to changes in the chemistry associated with tropospheric ozone formation. Increases in temperature will also lead to an increase in the VOCs emitted from vegetation, providing more ozone and PM2.5 precursor.A number of atmospheric sounders, with low radiometric noise and high spectral resolution, measure upwelling radiances in the thermal infrared spectral region; from these measurements we can determine the concentrations of many short-lived pollutants and precursors, e.g. ammonia, largely arising from agricultural sources (primarily ammonia-based fertilizers and animal manure), which significantly contributes to the formation of PM2.5; isoprene, the dominant biogenic volatile organic compound emitted by vegetation, which is chemically reactive and leads to the production of tropospheric ozone and secondary aerosols; as well as a wealth of pollutants produced from fires, such as methanol, formic acid, peroxyacetyl nitrate, ethene, and ethyne. We live in an age when satellite-based measurements of atmospheric composition are becoming more and more ubiquitous. The ever increasing amounts of data available, which are only going to increase in the future, will require smarter and faster computational methods to extract meaningful information from these observations. Traditional retrieval methods based on line-by-line radiative transfer models are slow but accurate. The challenge is to maintain this accuracy whilst significantly improving the speed.