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.

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From: Atmospheric Chemistry and Physics(Vol. 21, Issue 18)
Publisher: Copernicus GmbH
Document Type: Brief article
Length: 327 words

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Abstract :

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 SO.sub.2 and NO.sub.x ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO.sub.2 and NO.sub.2 during the heavy haze period are remarkably improved, especially for NO.sub.2 . Spatial distributions of SO.sub.2 and NO.sub.2 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 SO.sub.2, NO.sub.2, and O.sub.3 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.

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Gale Document Number: GALE|A675712911