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• W4241324912 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 <span class=inline-formula>SO₂</span> and <span class=inline-formula>NO_<i>x</i></span> ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of <span class=inline-formula>SO₂</span> and <span class=inline-formula>NO₂</span> during the heavy haze period are remarkably improved, especially for <span class=inline-formula>NO₂</span>. Spatial distributions of <span class=inline-formula>SO₂</span> and <span class=inline-formula>NO₂</span> 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 <span class=inline-formula>SO₂</span>, <span class=inline-formula>NO₂</span>, and <span class=inline-formula>O₃</span> 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." @default.
• W4241324912 created "2022-05-12" @default.
• W4241324912 date "2021-06-28" @default.
• W4241324912 modified "2023-10-02" @default.
• W4241324912 title "Comment on acp-2021-474" @default.
• W4241324912 doi "https://doi.org/10.5194/acp-2021-474-rc1" @default.
• W4241324912 hasPublicationYear "2021" @default.
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