We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO₂ and NO_x ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO₂ and NO₂ during the heavy haze period are remarkably improved, especially for NO₂. Spatial distributions of SO₂ and NO₂ concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO₂, NO₂, and O₃ modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

中文翻译：

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基于DDM-3D和3DVAR技术的排放源反演新方法：在京津冀地区空气质量预报中的应用

我们基于社区多尺度空气质量 (CMAQ) 模型中的三维解耦直接 (DDM-3D) 灵敏度分析模块和三维变分 (3DVAR) 数据同化技术。我们建立了ES和受体浓度之间的显式观测算子矩阵和ES的背景误差协方差（BEC）矩阵，可以反映ES的不确定性对同化的影响。然后我们将敏感性分析与3DVAR技术相结合，构建了ES的反演模型。我们使用反演模型对 2016 年 12 月 27 日至 30 日期间在京津冀 (BTH) 地区的重雾霾案例研究进行了模拟实验。SO ₂和NO _x ESs 的浓度以及 ES 的 BEC 矩阵是合理的。使用后验逆 ES，重霾期间SO ₂和NO _{2 的}低估得到显着改善，尤其是对于NO ₂。与 BTH 区域的先验 ES 相比，受约束 ES 模拟的SO ₂和NO ₂浓度的空间分布 更准确。区域平均SO ₂、NO ₂和O ₃的时间变化使用后验反演 ES 模拟的浓度与 BTH 地区 45 个站点的原位观测结果一致，模拟误差显着降低。这些结果对于研究重雾霾形成机制、降低ES不确定性及其动态更新、为空气质量预测提供准确的“虚拟”排放清单和优化决策服务具有重要意义。控制空气污染。