RMAPS_Chem V1.0系统SO_2排放清单优化效果评估

Evaluation on SO_2 Emission Inventory Optimizing Applied to RMAPS_Chem V1.0 System

RMAPS_Chem V1. 0系统是基于WRF_Chem模式建立的服务于华北区域雾霾等污染预报业务的模式系统,该研究着重针对系统中污染排放清单不确定性带来的SO_2浓度预报偏差较大问题,采用EnKF源反演和误差统计订正相结合的方法对排放清单进行了改进,形成了一套优化后的华北区域SO_2排放清单。通过输入初始清单和优化清单对2017年10月进行模拟,并与华北地区616个地面环境监测站观测值进行对比,结果表明:EnKF源反演结合误差统计订正的排放清单优化方法适用于SO_2排放清单的改进,有效降低了清单系统性偏差,针对主要区域及重点城市的检验显示模拟结果接近观测值;排放清单优化后模拟误差显著降低,如河北南部、山东西部至北京一带模式预报均方根误差与归一化平均绝对误差明显下降,区域内站点模拟误差呈正态分布特征,误差分布范围、最大概率出现范围均明显变窄,且最大误差概率明显上升。

Air pollution emission inventory is an important input data of air quality model. The uncertainty of emission inventory is a primary source of error in air quality forecasts and it also affects the regulation of air pollution sources. RMAPSChem VI. 0 is an operational forecasting system for haze and atmospheric pollution in North China. It is established based on an online coupled regional chemical transport model WRFChem. In order to reduce the large deviation of forecasted SO2 concentration, through the test of model accuracy on weather condition, a conclusion is drawn that the simulated error of SO2 concentration mainly comes from the deviation of emission. An optimized SO2 emission inventory is established, first inversed by ensemble square root Kalman filter(EnKF) approach, and then revised by using statistical error correction method. Comparison indicates that the optimized emission has obvious advantages to improve the prediction accuracy of ground SO2 concentration. Distribution of surface SO2 concentration over North China in October 2017 is simulated using initial emission inventory(MEIC2012) and the optimized emission inventory. Simulated results are compared with observations at 616 stations from China National Environmental Monitoring Center(CNEMC), and the difference between simulated results using two emission inventories is analyzed. Results show that the above emission inventory optimizing method is applicable for the correcting of regional deviation in SO2 emission, which is very effective on improving SO2 forecast accuracy in main regions and urban areas. Simulated results using optimized emissions are closer to the observed value in focus areas of RMAPSChem VI. 0 system. The largest forecast deviation areas concentrate in south region of Hebei, west region of Shandong and Beijing, which is consistent with the distribution of SO2 emissions deviation. Optimizing of the emission inventory brought significant reduction in forecast deviation in these regions, with root mean square error and normalized mean absolute error reduced obviously. The simulation error show normal distribution characteristics. The probability of error distribution range, the maximum range are significantly narrowed, and the biggest error probability value rises significantly, indicating errors are reduced.