Abstract
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 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 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.
Highlights
Since the implementation of the Air Pollution Prevention and Control Action Plan in September 2013, urban air quality in China has improved overall
We developed a new inverse approach of emission source (ES) by combining the sensitivity analysis technique between the ES and the receptor’s concentration and the 3DVAR method
We created the background error covariance (BEC) matrix for ES based on uncertainty analysis and the National Meteorological Center (NMC) statistical method
Summary
Since the implementation of the Air Pollution Prevention and Control Action Plan in September 2013, urban air quality in China has improved overall. The GEOS-Chem model is often used to simulate large-scale physical and chemical processes and is rarely utilized in urban air quality forecasts because its spatial resolution is too coarse This method has high computational costs due to the gradient calculation of the objective function. The top-down 3DVAR inversion methods developed in this study can include the impacts of ES uncertainties by the background error covariance (BEC) matrix of the ES based on multiple sets of ESs. We developed a new inverse modeling approach for the ES that combines the DDM-3D sensitivity analysis method with the 3DVAR assimilation technique and applied it to a case study during a typical heavy haze episode.
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