On adjoint method based atmospheric emission source tracing

The utmost important task for air pollution prevention and control is to identify the source of pollution such that targeted management and governance can be greatly enhanced. In this paper, we have develoepd an air quality forecasting system called “the national air quality high-resolution forecast and pollution control decision support system (NARS)”, which integrates atmospheric environment monitoring and analysis, meteorological numerical forecasting, emission source inversion, air quality forecasting, meteorological and atmospheric chemical data assimilation, emission sources tracing, profit and loss assessment together with dynamic optimal control. The system could be applied to perform the closed-loop prevention and control for atmospheric pollution by monitoring, forecasting, assimilation, traceability, emission source inversion and optimal control, and the system stands a great promise of providing a solution for current air pollution prevention and control strategy. In the NARS, a CAMx based adjoint model was developed, and was further demonstrated to perform dynamic inversion and grid-based quantitative emission source tracing. Our results show that the system can trace quickly and quantitatively both spatial and temporal distributions of emission sources and their contribution rates resulting from severe air pollution for certain target area for the coming 7 days. As an application of the NARS, the emission inversion, meteorological field simulation, air quality forecasting and grid traceability were studied for the time period from September 2016 to March 2017 in Beijing. Compared with the observation of national control station in Beijing-Tianjin-Hebei region (BTH), the results showed that the forecast accuracies of the pollution processes, the pollution level and the pollutant concentration are close to 100%, 88.8% and 84.7%, respectively, and the correlation coefficient between the modeling and observation is 0.81. The results of grid traceability showed that the emissions of PM2.5 pollution in the main urban area of Beijing are mainly from the air pollutant transmission channel in the southwest of. The emission contributions of BTH plus the surrounding areas are respectively 66%, 29% and 5%. The 19% PM2.5 emissions of BTH have resulted in 80% Beijing PM2.5 concentration in heavily polluted and severely polluted days. Among them, 9% Beijing local emissions account for 63% PM2.5, and 10% Hebei emissions accounted for 17%. The 26% BTH emissions have resulted in 80% PM2.5 in Beijing’s main city for slightly or heavily polluted days. Furthermore, Beijing emissions accounting for 9% of BTH emissions have contributed 61%; meanwhile, Hebei emissions accounting for 15% of BTH emissions have contributed the 18% of the pollution; finally, Tianjin 2% accouts for the leaving 1%. The emissions leading to the PM2.5 pollution in Beijing main city mainly distribute in the city zone and the sourthern of Beijing, some districts or counties of Baoding and Shijiazhuang. The top six districts or counties accounting for 48% pollutants are located in Beijing, while the top 20 districts or counties contribute 73%. Here, using the the adjoint model developed, we are able to analyze synchronously the emisison apportionment of the modeling results through combining the dynamic inversion and the monitoring emission inventories. Using both profit-loss assessment and natural cybernetics, the NARS is able to locate the emisison sources for optimal emisison control during a heavy air pollution episode.