基于卫星遥感的区域高分辨率氮氧化物排放量反演模型研究

Nitrogen oxides (NOx≡NO+NO2) play an important role in the formation of the air pollution complex and gray haze, and they are major chemical species of air pollutant emission inventories. "Bottom-up" inventories of NOx emissions, based on limited knowledge of emission factors and exrapolation, are subject to substantial uncertainties, "top-down" information derived from space-based observations of NO2 columns can reduce significantly the uncertainties in NOx emissions. However, current inverse modelling of NOx emissions can't performe well on the high resolution grids, because of the uncertainties related to satellite retrieval of NO2 columns, the neglection of horizontal transport between grids, and the complexity of the inverse problems. We will improve the retrieval of NO2 columns, which are the basis of an inversion of NOx emissions, and foucus on exploring the coupling between horizontal transportation and chemical process of NOx, therefore, simplify the complex non-linear relationship between NOx emissions and NO2 observations, in order to avoid time-consuming inversion and update the NOx emissions timely. The main research contents include the following: (1)Performing the China high-resolution retrieval of NO2 vertical column density from OMI so as to reduces the uncertainties related to operational standard NO2 column retrievals; (2)Establishing the non-linear relationship between NOx emissions and NO2 observations, which is performed through the construction of an horizontal transport model coupled with chemical process of NOx; (3)Implementing the optimization inversion of NOx emissions based on Kalman filter algorithm. This study is expected to provide a new technique for NOx emissions monitoring and reduction evaluation on a mesoscopic scale. We believe this work is also likely to provide scientific supports for achieving China's control target for NOx emissions.

氮氧化物NOx对区域大气复合污染具有重要贡献，是大气污染物排放清单调查的重要内容。"自下而上"排放清单具有较大不确定性，卫星遥感技术可以对其进行校验和改善。然而受限于卫星数据源、污染物网格传输及反演计算复杂度等问题，现有排放量卫星反演模型不能适用于区域高分辨率网格空间。本项目将改善卫星NO2柱浓度反演精度，简化"排放-浓度"非线性关系模型，实现NOx排放量快速估算，满足区域高分辨率NOx排放清单校验优化和动态更新的应用需求。研究内容包括：（1）针对NO2业务化产品在区域尺度应用的缺陷，开展中国地区高分辨率卫星NO2柱浓度数据反演；（2）分析污染物水平传输与化学转化过程，建立耦合化学过程的NOx水平传输模型；（3）研究排放模型及观测模型误差传递特征，利用卡尔曼滤波算法实现排放量最优化估计。本项目有望为区域NOx排放量监控及减排评估提供新的技术手段，为我国完成NOx排放总量控制目标提供科学支持。

氮氧化物NOx对区域大气复合污染具有重要贡献，是大气污染物排放清单调查的重要内容。"自下而上"排放清单具有较大不确定性，卫星遥感技术可以对其进行校验和改善。然而受限于卫星数据源、污染物网格传输及反演计算复杂度等问题，现有排放量卫星反演模型不能适用于区域高分辨率网格空间。本项目通过改善卫星NO2柱浓度反演精度，简化"排放-浓度"非线性关系模型，实现NOx排放量快速估算。研究内容包括：（1）针对NO2业务化产品在区域尺度应用的缺陷，开展中国地区高分辨率卫星NO2柱浓度数据反演；（2）分析污染物水平传输与化学转化过程，建立耦合化学过程的NOx水平传输模型，并利用卡尔曼滤波算法实现排放量最优化估计；（3）基于卫星观测结果分析区域NOx排放变化趋势并对空气污染控制措施减排效果进行评估；(4) 针对目前日益严峻的O3污染问题，通过分析两种重要前体物NOx和VOCs的排放变化，从卫星观测的角度初步分析了我国典型区域O3污染现状及成因。本研究构建了中国地区高分辨率卫星NO2柱浓度数据集，实现了近地面NO2浓度的估算，完成了基于卫星NO2柱浓度估算NOx排放量的模型开发。本研究有望为区域NOx排放量监控及减排评估提供新的技术手段，为我国完成NOx排放总量控制目标及O3污染防治提供科学支持。