基于多组分的颗粒物吸湿增长模型研究

One of the key issues in the satellite remote sensing method for estimating surface-level particulate matter is the hygroscopic growth of particles. at present. The particle hygroscopic growth model obtained by long-term ground-based observations. Due to temporal and spatial distribution differences of the components and mixed state of particles, this site-based Observatory style has innate limitations in space. It will take a great deal of uncertainty for the final results. Based on components of particulate matter simulated by atmospheric model, and in the support of the in situ measurement, this subject will analyze the regularity in the extinction characteristics of particulate matter vary with the influence factors which include the components of particulate matter and mixed state between different components. Base on the existing research results of single-component particles hygroscopic growth, This study will establish a dynamic hygroscopic growth model which can match each satellite image pixel and has broad applicability in time and space. At last, The model will be verified and its accuracy will be assessment in three typical district which includes beijing-tianjin-hebei, Yangzi Delta and Pearl River Delta. This model established in this study can reduce the uncertainty in the remote sensing method of retrieving particulate matter at a large regional scale, and solve the bottleneck in humidity correction of particulate matter retrieving method. And lay the foundation for improving the accuracy of retrieving particulate matter. It has importance scientific sense and application value.

颗粒物吸湿增长问题是颗粒物卫星遥感反演的关键问题之一。目前的吸湿增长模型通过长期地基观测获得。由于颗粒物组分及混合状态分布的时空差异，这种站点式观测决定了其空间上的局限性,为最终颗粒物反演结果带来很大的不确定性。本课题在观测实验的支持下，分析颗粒物的构成组分、不同组分之间的混合状态等因素在不同湿度条件下对颗粒物消光特性的影响规律；结合现有的单一组分颗粒物吸湿增长研究成果，研究基于多组分的颗粒物吸湿增长模型。借助于大气模式模拟的颗粒物组分格网,建立能和卫星像元相匹配的，具有时间和空间广泛适用性的颗粒物吸湿增长时空动态模型；最后在京津冀及珠三角等地区进行验证与精度评价。该模型的建立可以降低颗粒物卫星遥感反演在区域尺度上的不确定性，解决目前颗粒物卫星遥感反演方法在湿度订正上的瓶颈，为提高的颗粒物遥感反演精度奠定基础，具有重要的科学意义和应用价值。

本研究在北京地区进行了颗粒物组分及其吸湿增长观测实验。在实验结果数据基础上，利用神经网络算法建立了考虑颗粒物多种组分的颗粒物综合吸湿增长模型。并借助于大气化学模式系统模拟了中国区域的颗粒物组分格网。建立了与葵花8卫星像元相匹配的、逐网格变化的颗粒物吸湿增长模型。研究结果用在了我国大气攻关专项中2+26城市的颗粒物监测中，为近地面颗粒物浓度分布反演中的湿度订正环节提供了更精细的订正参数，从而提高了区域尺度卫星遥感反演颗粒物精度。达到预期设定的研究目标。