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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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[1] 程念亮, 张大伟, 李云婷, 等.2000~2014年北京市SO2时空分布及一次污染过程分析.环境科学, 2015, 36(11):3961-3971. http://d.old.wanfangdata.com.cn/Periodical/hjkx201511004 [2] 王丽涛, 潘雪梅, 郑佳, 等.河北及周边地区霾污染特征的模拟研究.环境科学学报, 2012, 32(4):925-931. http://d.old.wanfangdata.com.cn/Periodical/hjkxxb201204022 [3] Huang R J, Zhang Y L, Bozzetti C, et al.High secondary aerosol contribution to particulate pollution during haze events in China.Nature, 2014, 514(7521):218-222. doi: 10.1038/nature13774 [4] 徐昶, 沈建东, 何曦, 等.杭州无车日大气细颗粒物化学组成形成机制及光学特性.中国环境科学, 2013, 33(3):392-401. doi: 10.3969/j.issn.1000-6923.2013.03.002 [5] Zhao Y, Nielsen C, Lei Y, et al.Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China.Atmos Chem Phys, 2011, 11:2295-2308. doi: 10.5194/acp-11-2295-2011 [6] Lee C, Martin R V, Van Donkelaa R A, et al.SO2 emissions and lifetimes:Estimates from inverse modeling using in situ and global, space-based (SCIAMACHY and OMI) observations.J Geophys Res, 2011, DOI: 10.10292010JD014758. [7] 蔡旭晖, 邵敏, 苏芳.甲烷排放源逆向轨迹反演模式研究.环境科学, 2002, 23(5):19-24. doi: 10.3321/j.issn:0250-3301.2002.05.004 [8] 苏芳, 邵敏, 蔡旭辉, 等.利用逆向轨迹反演模式估算北京地区甲烷源强.环境科学学报, 2002, 22:586-591. doi: 10.3321/j.issn:0253-2468.2002.05.009 [9] Tang X, Zhu J, Wang Z F, et al.Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter.Atmos Environ, 2013, 81(4):676-686. http://www.sciencedirect.com/science/article/pii/S1352231013006717 [10] 程兴宏, 徐祥德, 安兴琴, 等.2013年1月华北地区重霾污染过程SO2和NOx的CMAQ源同化模拟研究.环境科学学报, 2016, 36(2):638-648. http://d.old.wanfangdata.com.cn/Periodical/hjkxxb201602035 [11] 孟凯, 程兴宏, 徐祥德, 等.基于CMAQ源同化反演方法的京津冀局地污染源动态变化特征模拟研究.环境科学学报, 2017, 37(1):52-60. http://d.old.wanfangdata.com.cn/Periodical/hjkxxb201701006 [12] 赵秀娟, 徐敬, 张自银, 等.北京区域环境气象数值预报系统及PM2.5预报检验.应用气象学报, 2016, 27(2):160-172. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160204&flag=1 [13] Hong C P, Zhang Q, He K B, et al.Variations of China's emission estimates:Response to uncertainties in energy statistics.Atmos Chem Phys, 2017, 17:1227-1239. doi: 10.5194/acp-17-1227-2017 [14] Wang L, Zhang Y, Wang K, et al.Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China:Sensitivity study, comparative evaluation, and policy implications.Atmos Environ, 2016, 124:337-350, DOI: 10.1016/j.atmosenv.2014.12.052. [15] Georg A G, Steven E P, Rainer S, et al.Fully coupled "online" chemistry within the WRF model.Atmos Environ, 2005, 39:6957-6975. doi: 10.1016/j.atmosenv.2005.04.027 [16] Jiang F, Wang T J, Wang T T, et al.Numerical modeling of a continuous photochemical pollution episode in Hong Kong using WRF-chem.Atmos Environ, 2008, 42:8717-8727. doi: 10.1016/j.atmosenv.2008.08.034 [17] Geng F, Tie X, Guenther A, et al.Effect of isoprene emissions from major forests on ozone formation in the city of Shanghai, China.Atmos Chem Phys, 2011, 11:10449-10459. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_68a0edd1f418df8ed3173c6516382928 [18] 徐敬, 张小玲, 蔡旭晖, 等.基于敏感源分析的动态大气污染排放方案模拟.应用气象学报, 2016, 27(6):654-665. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160602&flag=1 [19] Wang L, Zhang Y, Wang K, et al.Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China:Sensitivity study, comparative evaluation, and policy implications.Atmos Environ, 2014, 124:337-350. http://www.sciencedirect.com/science/article/pii/S1352231014010000 [20] 徐敬, 马志强, 赵秀娟, 等.边界层方案对华北低层O3垂直分布模拟的影响.应用气象学报, 2015, 26(5):567-577. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150506&flag=1 [21] 闵晶晶.BJ-RUC系统模式地面气象要素预报效果评估.应用气象学报, 2014, 25(3):265-273. doi: 10.3969/j.issn.1001-7313.2014.03.002 [22] 朱江, 汪萍.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用.大气科学, 2006, 30(5):871-882. doi: 10.3878/j.issn.1006-9895.2006.05.16 [23] 唐晓, 朱江, 王自发, 等.基于集合卡尔曼滤波的区域臭氧资料同化试验.环境科学学报, 2013, 3(3):796-805. http://d.old.wanfangdata.com.cn/Periodical/hjkxxb201303020 [24] 梁晓, 郑小谷, 戴永久, 等.EnKF中误差协方差优化方法及在资料同化中应用.应用气象学报, 2014, 25(4):397-405. doi: 10.3969/j.issn.1001-7313.2014.04.002 [25] Peng Z, Liu Z, Chen D, et al.Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filte.Atmospheric Chemistry and Physics, 2017, 17:4837-4855, DOI: 10.5194/acp-17-4837-2017. [26] Zhi G R, Zhang Y Y, Sun J Z, et al.Village energy survey reveals missing rural raw coal in northernChina.Significance in Science and Policy, 2017, 23:705-712. https://reference.medscape.com/medline/abstract/28196720 [27] Xu X, Xie L, Cheng X, et al.Application of an adaptive nudging scheme in air quality forecasting in China.Journal of Applied Meteorology and Climatology, 2008, 47(8):2105-2114. doi: 10.1175/2008JAMC1737.1 [28] Cheng X H, Xu X D, Ding G A.An emission source inversion model based on satellite data and its application in air quality forecasts.Science China(Earth Sciences), 2010, 53(5):752-762. doi: 10.1007/s11430-010-0044-9 [29] 蔡旭晖, 丑景垚, 宋宇, 等.北京市大气静稳型重污染的印痕分析.北京大学学报(自然科学版), 2008, 44(1):135-141. doi: 10.3321/j.issn:0479-8023.2008.01.024 [30] 靳军莉, 颜鹏, 马忐强, 等.北京及周边地区2013年1-3月PM2.5变化特征.应用气象学报, 2014, 25(6):690-700. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20140605&flag=1 [31] Tuccella P, Curci G, Visconti G, et al.Modeling of gas and aerosol with WRF/Chem over Europe:Evaluation and sensitivity study.J Geophys Res, 2012, 117(D03303), DOI: 10.1029/2011JD016302. [32] Liao L, Liao H.Role of the radiative effect of black carbon in simulated PM2.5 concentrations during a haze event in China.Atmosphere and Oceanic Science Letters, 2014, 7(5):434-440. doi: 10.1080/16742834.2014.11447203 [33] 李阳, 徐晓斌, 林伟立, 等.基于观测的污染气体区域排放特征.应用气象学报, 2012, 23(1):10-19. doi: 10.3969/j.issn.1001-7313.2012.01.002 [34] 徐晓斌, 刘希文, 林伟立, 等.输送对区域本底站痕量气体浓度的影响.应用气象学报, 2009, 20(6):656-664. doi: 10.3969/j.issn.1001-7313.2009.06.002 -
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