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Evaluation on SO2 Emission Inventory Optimizing Applied to RMAPS_Chem V1.0 System
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摘要: 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SO2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SO2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SO2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。Abstract: 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. RMAPS_Chem V1.0 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 WRF_Chem. 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 (MEIC_2012) 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 RMAPS_Chem V1.0 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.
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Key words:
- North China;
- inversing emission inventory;
- SO2 simulation
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图 2 2017年10月月平均地面温度(a)、风速(b)和相对湿度(c)模拟值与观测值对比
(色阶底图为模拟值，实心圆点代表观测值，两者采用相同的色标)
Fig. 2 Comparison of the monthly averaged near-surface temperature(a), wind speed(b) and relative humidity(c) simulated by RMAPS_Chem V1.0 with observations in Oct 2017
(simulated and observed values using the same color bar are indicated by shaded base graphics and shaded circles, respectively )
图 3 2017年10月SO2格点平均月排放量差值(a)EnKF反演清单减去初始清单，(b)优化清单减去初始清单
Fig. 3 Difference value of monthly mean SO2 emission load for each grid in Oct 2017(a)inversion emissions using EnKF approach minus initial emissions, (b)optimized emissions minus initial emissions
图 4 2017年10月1—10日EnKF反演清单(a)、优化清单(b)SO2浓度模拟值与观测值均方根误差
Fig. 4 Root mean square error between observed and simulated SO2 concentration from 1 Oct to 10 Oct in 2017 (a)inversion emissions using EnKF approach, (b)optimized emissions
图 5 2017年10月1—10日EnKF反演清单(a)、优化清单(b)SO2浓度模拟值与观测值归一化平均绝对误差
Fig. 5 Normalized mean absolute error between observed and simulated SO2 concentration from 1 Oct to 10 Oct in 2017 (a)inversion emissions using EnKF approach, (b)optimized emissions
图 6 2017年10月1—10日EnKF反演清单(a)、优化清单(b)D01区域内站点SO2浓度模拟值与观测值偏差概率分布
Fig. 6 Probability distribution of bias between observed and simulated SO2 concentrations from 1 Oct to 10 Oct in 2017 (a)inversion emissions using EnKF approach, (b)optimized emissions
图 7 2017年10月排放清单优化前后各区域SO2日平均浓度模拟值与观测值逐日变化
Fig. 7 Temporal variation of different regional observed and simulated SO2 daily mean concentration with initial and optimized emissions in Oct 2017
图 8 2017年10月排放清单优化前后SO2浓度模拟值与观测值均方根误差(a)初始清单, (b)优化清单
Fig. 8 Root mean square error between observed and simulated monthly mean SO2 concentration with initial(a) and optimized(b) emissions in Oct 2017
图 9 2017年10月排放清单优化前后SO2浓度模拟值与观测值归一化平均绝对误差(a)初始清单, (b)优化清单
Fig. 9 Normalized mean absolute error between observed and simulated monthly mean SO2 concentration with initial(a) and optimized(b) emissions in Oct 2017
图 10 2017年10月华北区域616个环境监测站SO2月平均浓度模拟值与观测值偏差概率分布(a)初始清单, (b)优化清单
Fig. 10 Probability distribution of bias between simulated SO2 concentration using initial(a) and optimized(b) emissions and observations at 616 stations over North China in Oct 2017
表 1 2017年10月各区域及重点城市EnKF反演清单、优化清单较初始清单SO2排放量变化(单位：t)
Table 1 Difference of SO2 emission load between inversion emissions using EnKF approach, optimized emissions and initial emissions in different regions and cities in Oct 2017(unit: t)
区域 EnKF反演清单减去初始清单 优化清单减去初始清单 D01 72.0 -32.3 D02 47.1 -21.6 河北南部 2.0 -8.8 河北北部 0.1 0.2 北京 -0.2 -0.3 石家庄 0.1 -0.4 
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表 2 2017年10月排放清单优化前后各区域模拟值和观测值对比
Table 2 Comparison of mean SO2 concentration for different regions between observed and simulated with initial and optimized emissions in Oct 2017
检验区域 观测浓度/
(μg·m-3)优化前 优化后 模拟浓度/
(μg·m-3)均方根误差/
(μg·m-3)归一化
平均绝对误差模拟浓度/
(μg·m-3)均方根误差/
(μg·m-3)归一化
平均绝对误差D01 16.11 32.06 31.04 2.03 7.93 14.23 0.66 D02 18.72 54.25 52.10 4.49 10.12 15.73 0.79 河北南部 13.15 75.95 69.53 6.86 11.71 7.29 0.55 河北北部 7.33 28.50 28.65 6.51 8.19 7.33 1.47 北京 3.57 60.12 64.43 18.11 13.68 10.93 3.97 石家庄 12.98 96.81 87.70 8.42 13.29 6.84 0.57
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