基于地基遥感观测的大气气溶胶成分及混合状态联合反演研究

The composition and mixing state of atmospheric aerosol mainly determine its optical and microphysical properties and therefore is now a very important subject in the research fields of global climate change and atmospheric environment effect. Ground-based remote sensing technology, known for its abilities of acquiring column aerosol properties in real time and large scale while maintaining the aerosol natural state in ambient environment, is therefore a potentially effective approach to detect aerosol composition information. However, the research on aerosol composition and mixing state from its apparent properties is proved to be a posteriori progress, which needs some complex models and calculations. And so far we haven't seen many solid and referable research results in this field. Therefore, this research primarily focuses on how to utilize the high-precision aerosol optical and microphysical properties acquired from ground-based measurements to quantitatively obtain the aerosol composition and to clearly delineate the particulate mixing state. In this study, we plan to establish an aerosol composition model that is able to sufficiently describe atmospheric aerosol and is also identifiable in remote sensing. Then we investigate the retrieval method for each component in this model and establish a new aerosol mixing model at the same time. Based on the new model, we can perform the forward modelling from composition and mixing state to optical and microphysical properties. After that we can get the aerosol composition and mixing state information by realizing the joint retrieval based on least squares principle, and ultimately verify the retrieval results through synchronous experiments of sun/sky radiometer, chemical sampling and Lidar measurements. For the first time, this research adds scattering organic carbon, which is a very important component in the atmosphere, to the composition model to investigate its composition information. This research also intends to establish an innovative aerosol mixing model based on internal-external mixing combination, which can offer a better description for the mixing state of atmospheric aerosol than other models. The proposed research scheme has good portability and can be easily transplanted to satellite remote sensing, and therefore is able to provide methodological support for acquiring temporal-spatial distribution of aerosol composition at the global scale based on satellite observation in the near future.

大气气溶胶成分及混合状态决定了气溶胶的光学/微物理特性，是气候变化和大气环境研究的重要内容。气溶胶地基遥感技术具有不破坏气溶胶自然状态、实时获取大范围、整层大气气溶胶信息的优势，为气溶胶成分研究提供了可能。由气溶胶表观特性追溯其成分及混合状态的研究还不多见。因此如何利用高精度的气溶胶特性参数，准确获得气溶胶成分含量并清晰描述粒子混合状态，是本项目重点关注的科学问题。本项目拟建立遥感可识别的、能较好描述大气气溶胶组成的成分模型，发展每种成分的遥感反演方法，开展气溶胶成分混合模型研究，建立由气溶胶成分组成与混合状态到特性参数的正向模型，基于最小二乘原理实现二者的联合反演，并利用同步观测验证气溶胶成分反演结果。本项目首次将散射性有机碳加入到成分模型中，并拟建立基于内、外混合组合的新型混合模型。本项目的研究方案具有较好的可移植性，可为未来基于卫星数据获取全球尺度的气溶胶成分时空分布信息提供支撑。

大气气溶胶来源广泛，种类繁多，化学成分随时空变化显著，而气溶胶不同成分具有显著的光学、物理和化学特性差异，因此成为气候变化等领域研究中不确定性最大的因素。遥感具有实时获取、整层大气和非破坏式的探测优势，逐步成为气溶胶成分快速探测的重要途径，可以为定量评估气溶胶气候效应和环境效应提供关键支撑。.本项目系统性的开展了气溶胶成分遥感估计研究。首先，建立了能较好描述对流层大气气溶胶组成的成分遥感模型，包括黑碳（BC）、光学吸收性有机碳（BrC）、矿物沙尘（DU）、硫酸铵类（AS）、光学非吸收性有机成分（POM）、海盐（SS）和水分（AW）七种成分，基本涵盖了整层大气气溶胶的不同来源和主要类型。其次，建立了较符合气溶胶自然状态、普适性更优的气溶胶内、外混合组合模型，并给出了不同情况下适用的气溶胶混合状态描述。第三，发展了由气溶胶成分组成到光学/微物理特性的正向模型，以及基于最小二乘优化策略同时反演获得气溶胶成分含量及混合状态的遥感方法，获得了整层大气气溶胶成分的体积百分比和质量浓度。第四，开展了同步观测实验，通过地基/飞行等多种观测手段以验证气溶胶成分的遥感反演结果。第五，在典型区域开展了气溶胶成分遥感估计的应用研究，包括中国多种典型地区、全球变化关键区域——北极地区等，为气候变化和环境监测等相关领域的研究提供重要数据支持。.本项目在人才培养、参加学术会议、国内外合作交流等方面均取得了进展，发表高水平学术论文3篇（其中SCI收录2篇，影响因子均大于3）。