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. We develop a new inversion method which is suitable for linear andnonlinear emission source (ES) modeling, based on the three-dimensionaldecoupled direct (DDM-3D) sensitivity analysis module in the CommunityMultiscale Air Quality (CMAQ) model and the three-dimensional variational(3DVAR) data assimilation technique. We established the explicit observationoperator matrix between the ES and receptor concentrations and thebackground error covariance (BEC) matrix of the ES, which can reflect theimpacts of uncertainties of the ES on assimilation. Then we constructed theinversion model of the ES by combining the sensitivity analysis with 3DVARtechniques. 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 NOx 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 areremarkably improved, especially for NO2. Spatial distributions ofSO2 and NO2 concentrations simulated by the constrained ES weremore accurate compared with an a priori ES in the BTH region. The temporal variationsin regionally averaged SO2, NO2, and O3 modeledconcentrations using an a posteriori inversed ES are consistent with in situ observationsat 45 stations over the BTH region, and simulation errors decreasesignificantly. These results are of great significance for studies on theformation mechanism of heavy haze, the reduction of uncertainties of the ES and itsdynamic updating, and the provision of accurate “virtual” emission inventories forair-quality forecasts and decision-making services for optimization controlof air pollution.

atmospheric chemistry and physics

A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region