利用集合均方根卡尔曼滤波反演重庆地区SO_2源排放
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摘要
传统的"自下而上"清单方法估算的排放清单,其数据的准确性和时效性存在较大局限.基于集合均方根卡尔曼滤波的源清单反演方法,结合WRF-CMAQ(天气研究和预报模式-公共多尺度空气质量模型)被用于对以清华大学编制的2010年MEIC(中国多尺度排放清单模型)排放清单为基础制作的重庆地区SO_2排放源进行反演试验以解决准确性和时效性问题,试验时间段为2014年10月15—31日,重庆主城17个环境空气质量国控监测点ρ(SO_2)小时观测资料用于反演及检验.结果表明:该方法能够反演重庆地区SO_2源排放量,随着反演次数增加,基于反演排放源预报的ρ(SO_2)预报误差持续减小,反演4次后预报误差达到比较低的稳定的水平,其均方根误差均低于20μg/m~3.5次反演后SO_2源排放量用于2014年10月24—29日每天起始预报,其预报的站点、时间平均的均方根误差从100~400μg/m~3降至30μg/m~3以下.反演中应用局地化尺度减少集合取样误差影响,54与81 km两个局地化尺度反演结果对预报改善效果相当,表明主要影响重庆主城ρ(SO_2)的源排放位于主城及周边地区,也说明内源排放对重庆主城ρ(SO_2)起主要影响.反演后面源排放量主城区降幅约为30 kg/(d·km~2),周边地区减少10~20 kg/(d·km~2),主城区部分SO_2点源排放量降幅约为25 kg/(d·km~2),说明2010年MEIC排放清单高估了试验时段重庆地区的SO_2排放.
Emissions inventories based on the‘bottom-up'inventory method have many problems with respect to accuracy and time.To solve this problem,an inversion of the SO_2 emission over Chongqing from October 15 thto 31 st,2014 was performed with WRF-CMAQ coupled with an ensemble square root Kalman filter.The initial SO_2 emissions estimates come from the 2010 Multi-resolution Emission Inventory for China developed by Tsinghua University.Hourly observations of SO_2 concentrations at 17 state air quality monitoring stations over the downtown area of Chongqing were used for inversion and verification.The results show that the ensemble square root Kalman filter is suitable for inverting SO_2 emissions over Chongqing.The forecast error decreased gradually as the number of inversion emissions increased; additionally,the root mean square errors of the forecast concentration of SO_2 were all less than 20 μg/m~3 after four inversions.Localization was employed to account for ensemble sampling error.The experiments using a localization distance of 54 and 81 km had similar results,demonstrating that the concentrations of SO_2 in Chongqing are primarily affected by the SO_2 emission inventories surrounding the area and that the inner sources are primarily responsible for the concentration of SO_2 in the downtown area of Chongqing.The average root mean square error of several station forecasts,initialized at each day from October 24 thto 29 th,2014,decreased from100-400 μg/m~3 to concentrations less than 30 μg/m~3.The estimated SO_2 emissions of the area decreased by approximately 30 kg/(d·km~2)and 10-20 kg/(d·km~2) in the downtown and surrounding areas of Chongqing,respectively; meanwhile,point emissions decreased byapproximately 25 kg/(d·km~2) in part of the downtown area.The regional SO_2 emissions inventories from 2010 MEIC were overestimated over Chongqing during the experiment.
Emissions inventories based on the‘bottom-up'inventory method have many problems with respect to accuracy and time.To solve this problem,an inversion of the SO_2 emission over Chongqing from October 15 thto 31 st,2014 was performed with WRF-CMAQ coupled with an ensemble square root Kalman filter.The initial SO_2 emissions estimates come from the 2010 Multi-resolution Emission Inventory for China developed by Tsinghua University.Hourly observations of SO_2 concentrations at 17 state air quality monitoring stations over the downtown area of Chongqing were used for inversion and verification.The results show that the ensemble square root Kalman filter is suitable for inverting SO_2 emissions over Chongqing.The forecast error decreased gradually as the number of inversion emissions increased; additionally,the root mean square errors of the forecast concentration of SO_2 were all less than 20 μg/m~3 after four inversions.Localization was employed to account for ensemble sampling error.The experiments using a localization distance of 54 and 81 km had similar results,demonstrating that the concentrations of SO_2 in Chongqing are primarily affected by the SO_2 emission inventories surrounding the area and that the inner sources are primarily responsible for the concentration of SO_2 in the downtown area of Chongqing.The average root mean square error of several station forecasts,initialized at each day from October 24 thto 29 th,2014,decreased from100-400 μg/m~3 to concentrations less than 30 μg/m~3.The estimated SO_2 emissions of the area decreased by approximately 30 kg/(d·km~2)and 10-20 kg/(d·km~2) in the downtown and surrounding areas of Chongqing,respectively; meanwhile,point emissions decreased byapproximately 25 kg/(d·km~2) in part of the downtown area.The regional SO_2 emissions inventories from 2010 MEIC were overestimated over Chongqing during the experiment.
引文
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[4]BINKOWSKI F S,ROSELLE S J.Models-3 community multiscale air quality(CMAQ)model aerosol component 1.model description[J].Journal of Geophysical Research Atmospheres,2003,108(D6):335-346.
[5]李荔,刘倩,李冰,等.南京青奥会期间管控措施空气质量改善效果评估[J].环境科学研究,2016,29(2):175-182.LI Li,LIU Qian,LI Bing,et al.Assessment of air quality benefits from control measures during Nanjing Youth Olympic Games[J].Research of Environmental Sciences,2016,29(2):175-182.
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[8]张珏,孟凡,何友江,等.长江三角洲地区大气二英类污染物输送-沉降模拟研究[J].环境科学研究,2011,24(12):1393-1402.ZHANG Jue,MENG Fan,HE Youjiang,et al.Simulation of transport-deposition of dibenzo-p-dioxins and dibenzofurans in the atmosphere in the Yangtze River Delta region[J].Research of Environmental Sciences,2011,24(12):1393-1402.
[9]边敏娟,钱骏,刘志红,等.四川中东部地区2009年大气硫沉降模拟[J].环境科学研究,2012,25(10):1115-1119.BIAN Minjuan,QIAN Jun,LIU Zhihong,et al.Modeling study on atmospheric sulfur deposition in the eastern and central regions of Sichuan area in 2009[J].Research of Environmental Sciences,2012,25(10):1115-1119.
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[14]BOUSQUET P,CIAIS P,PEYLIN P,et al.Inverse modeling of annual atmospheric CO2sources and sinks 1.method and control inversion[J].Journal of Geophysical Research Atmospheres,1999,104(D21):26161-26178.
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[18]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[J].大气科学,2006,30(5):871-882.ZHU Jiang,WANG Ping.Ensemble Kalman smoother and ensemble Kalman filter approaches to the joint air quality state and emission estimation problem[J].Chinese Journal of Atmospheric Sciences,2006,30(5):871-882.
[19]MIYAZAKI K,ESKES H J,SUDO K.Global NOxemission estimates derived from an assimilation of OMI tropospheric NO2columns[J].Atmospheric Chemistry&Physics,2012,11(5):31523-31583.
[20]TANG X,ZHU J,WANG Z F,et al.Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter[J].Atmospheric Environment,2013,81(4):676-686.
[21]BISHOP C H,ETHERTON B J,MAJUMDAR S J.Adaptive sampling with the ensemble transform Kalman filter.part I:theoretical aspects[J].Monthly Weather Review,2001,129(3):420-436.
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[23]WHITAKER J S,HAMILL T M.Ensemble data assimilation without perturbed observations[J].Monthly Weather Review,2002,130(7):1913-1924.
[24]HAMILL T M,WHITAKER J S,SNYDER C.Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter[J].Monthly Weather Review,2001,129(11):2776-2790.
[25]ANDERSON J L.Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter[J].Physica D:Nonlinear Phenomena,2007,230(1/2):99-111.
[26]SEKIYAMA T T,TANAKA T Y,SHIMIZU A,et al.Data assimilation of CALIPSO aerosol observations[J].Atmospheric Chemistry and Physics,2009,10(1):39-49.
[27]SOBASH R A,STENSRUD D J.The impact of covariance localization for radar data on En KF analyses of a developing MCS:observing system simulation experiments[J].Monthly Weather Review,2013,141(11):3691-3709.
[28]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[J].环境科学学报,2013,33(3):796-805.TANG Xiao,ZHU Jiang,WANG Zifa,et al.Regional ozone data assimilation experiment based on ensemble Kalman filter[J].Acta Scientiae Circumstantiae,2013,33(3):796-805.
[29]WHITAKER J S,HAMILL T M,WEI X,et al.Ensemble data assimilation with the NCEP global forecast system[J].Monthly Weather Review,2008,136(2):463-482.
[30]SKAMAROCK W C,KLEMP J B,DUDHIA J,et al.A description of the advanced research WRF version 3[EB/OL].Boulder,Colorado,USA:National Center for Atmospheric Research,2008[2008-06].http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.
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