RMAPS_Chem V1.0系统SO_2排放清单优化效果评估
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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. RMAPS_Chem 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 WRF_Chem. In order to reduce the large deviation of forecasted SO_2 concentration, through the test of model accuracy on weather condition, a conclusion is drawn that the simulated error of SO_2 concentration mainly comes from the deviation of emission. An optimized SO_2 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 SO_2 concentration. Distribution of surface SO_2 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 SO_2 emission, which is very effective on improving SO_2 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 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 SO_2 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.
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 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 WRF_Chem. In order to reduce the large deviation of forecasted SO_2 concentration, through the test of model accuracy on weather condition, a conclusion is drawn that the simulated error of SO_2 concentration mainly comes from the deviation of emission. An optimized SO_2 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 SO_2 concentration. Distribution of surface SO_2 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 SO_2 emission, which is very effective on improving SO_2 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 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 SO_2 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.
引文
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[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.
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[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. Aimos Environ,2016,124:337-350, DOI:10. 1016/j. atmosenv. 2014. 12. 052.
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[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.
[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.
[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.
[20]徐敬,马志强,赵秀娟,等.边界层方案对华北低层03垂直分布模拟的影响.应用气象学报,2015,26(5):567-577.
[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.
[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.
[23]唐晓,朱江,王白发,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.
[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.
[25] Peng Z,Liu Z,Chen D,et al. Improving PM_(2.5)forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filte. Atmospheric Chemistry and, 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.
[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.
[28] Cheng X II,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.
[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.
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[32] Liao L,Liao II. Role of the radiative effect of black carbon in simulated PM_(2.5)concentrations during a haze event in China.Atmosphere and Oceanic Science Letters,2014,7(5):434-440.
[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.
[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.
(1)Chen D,Liu Z Q,Peng Z,et al. Three-year(2015—2017)winter-time air pollution in China:PartⅡ. SO_2 emission changes by using WRF/Chem-EnKF coupled data assimilation system. Submitted to Atmospheric Chemistry and Physics,2018.
[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.
[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.
[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.
[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.
[6] Lee C, Martin R V, Van Donkelaa R A,et al. SO_2 emissions and lifetimes:Estimates from inverse modeling using in situ and global, space-based(SCIAMACIIY and OMI)observations. J Geophys Res, 2011, DOI:10. 1029 2010JD014758.
[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.
[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.
[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.
[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SO_2和NOx的CMAQ源同化模拟研究.环境科学学报,2016,36(2):638-648.
[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.
[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PM_(2.5)预报检验.应用气象学报,2016,27(2):160-172.
[13] Hong C P,Zhang Q,IIe K B,et al. Variations of China's emission estimates:Response to uncertainties in energy statistics. Aimos Chem Phys,2017,17:1227-1239.
[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. Aimos 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.
[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. Aimos Environ, 2008,42:8717-8727.
[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.
[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.
[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.
[20]徐敬,马志强,赵秀娟,等.边界层方案对华北低层03垂直分布模拟的影响.应用气象学报,2015,26(5):567-577.
[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.
[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.
[23]唐晓,朱江,王白发,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.
[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.
[25] Peng Z,Liu Z,Chen D,et al. Improving PM_(2.5)forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filte. Atmospheric Chemistry and, 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.
[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.
[28] Cheng X II,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.
[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.
[30]靳军莉,颜鹏,马忐强,等.北京及周边地区2013年1一3月PM_(2.5)变化特征.应用气象学报,2014,25(6):690-700.
[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 II. Role of the radiative effect of black carbon in simulated PM_(2.5)concentrations during a haze event in China.Atmosphere and Oceanic Science Letters,2014,7(5):434-440.
[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.
[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.
(1)Chen D,Liu Z Q,Peng Z,et al. Three-year(2015—2017)winter-time air pollution in China:PartⅡ. SO_2 emission changes by using WRF/Chem-EnKF coupled data assimilation system. Submitted to Atmospheric Chemistry and Physics,2018.