应用地面和卫星观测资料反演中国地区氨气排放源

Rapid growth of ammonia (NH3) anthropogenic emissions in China has led to significant increases in surface aerosol pollution and atmospheric nitrogen deposition. Understanding the ammonia emission in China, including spatial and seasonal patterns, is important to assess its environmental impacts and regulation strategies. Current estimates of Chinese ammonia emission are all based on bottom-up approaches that appear to have large uncertainties and errors. This project proposes to use top-down approaches with observations from surface networks and satellite, combined with a chemical transport model to better quantify the ammonia emission in China. The work will apply Kalman filter with surface observations of ammonium wet deposition fluxes to constrain the total ammonia emission. It will apply the adjoint model with satellite observations of ammonia concentrations to inverse estimate its emissions with detailed information on the spatial distribution. The inversion results will be evaluated independently with surface measurements of ammonia and ammonium concentrations. The project will also examine the potential biases in satellite observations of ammonia concentrations. The project is expected to significantly improve our understanding of ammonia emission in China and its environmental impacts.

近年来我国快速增长的氨气排放对地面气溶胶微粒物污染和大气氮沉降等环境问题有着显著影响。准确认识氨气排放及其时空分布对评估其环境危害和控制排放有着重要的意义。当前我国氨气排放估计都是基于自下而上的方法统计其排放清单从而存在很大的误差和不确定性。本项目将应用地面和卫星观测资料结合化学传输模式通过自上而下的方法反演中国地区氨气排放。本项目拟应用卡尔曼滤波方法结合地面铵盐湿沉降数据反演氨气排放总量，拟应用伴随模式结合卫星观测的氨气浓度数据反演氨气排放的时空变化。反演结果将与地面观测的氨气和铵盐浓度数据相比较以验证反演的准确度并指出现有排放估计的不足。同时本项目将应用地面观测的氨气浓度数据评估卫星资料的潜在系统偏差。本项目将有效地提高我国氨气排放估计的准确度进而认识氨气排放导致的环境影响。

本项目应用地面和卫星观测资料结合反向模式的研究方法反演优化中国地区的氨气排放，以实现提高我国氨气排放评估的准确度并认识氨气排放对大气环境的影响的研究目标。本项目采用了大气化学模型GEOS-Chem及其伴随模式，并改进了该模型的空间分辨率和气溶胶模拟。应用伴随模式和卫星仪器TES的氨气柱浓度观测资料反演获得了中国地区的氨气排放量及其空间分布。反演优化的中国地区氨气排放总量为13.2百万吨，高于大多现有的氨气排放清单，且存在很强的季节变化；夏季氨气的排放是冬季排放的3倍以上。本项目统计了我国典型的农作物及其种植面积和化肥施用资料，改进了化肥施用量的计算及其导致的氨气排放评估，以此解释反演结果和现有氨气排放清单的偏差。本项目应用伴随模式进一步量化了我国氨气排放对华北地区细颗粒物污染和沿海地区大气氮沉降的重要程度和贡献大小。