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复杂下垫面下空气污染数值模拟研究
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摘要
本文在对兰州市西固区2005年1月20日—2月2日大气边界层实验观测资料和空气质量监测资料分析的基础上,利用中尺度气象模式WRF (Weather Research and Forecasting)和空气质量模式Models3系统,对实验期间兰州市西固区冬季边界层低空气象要素特征和空气污染分布进行了模拟;通过与同期观测的气象资料和空气质量监测资料进行对比,了解WRF模式中不同边界层参数化方案对兰州市复杂下垫面边界层低层气象场特征的模拟能力,进而探讨WRF模式不同PBL (Planetary Boundary Layer)参数化方案模拟的近地层气象场对空气质量模拟效果的差异;论文也验证了CMAQ (Community Multiscale Air Quality)模式在复杂地形高分辨率下对污染物输送扩散特征的模拟能力,研究结论对于改进复杂下垫面空气质量数值模拟效果,进而提高空气污染数值预报水平有一定的参考价值;利用模拟结果进一步研究了兰州市冬季污染物的时空分布特征,讨论了大气中各种物理化学过程对SO2和NO2地面浓度的贡献;通过设计敏感性试验,研究了兰州市高架源排放对兰州城区空气质量的影响程度,为治理兰州地区大气污染、改善兰州地区空气质量提供科学依据。论文主要结论如下:
     (1)WRF中YSU、MYJ和ACM2三种边界层参数化方案模拟的兰州地区冬季温度场空间分布特征相似,但MYJ方案模拟的夜间温度低于YSU和ACM2方案,日间则高于YSU和ACM2方案。这主要是由于局地闭合的MYJ方案湍流交换能力较弱,从而导致地面温度变化幅度低于非局地闭合方案。日间太阳短波辐射增强,湍流混合加剧的结果使得三个方案模拟的地面温度的水平梯度小于夜间。
     (2)不同边界层参数化方案对地面温度的模拟比较分析表明,对于冬季稳定边界层,WRF模式局地闭合的MYJ方案可以更好地模拟低层温度的时间变化特征。
     (3)与实测位温廓线对比可知,WRF模式三种边界层参数化方案模拟的夜间位温廓线均较好,ACM2方案模拟的日间低层位温廓线优于MYJ和YSU方案,而MYJ方案对边界层中上部位温廓线的模拟更接近于实测。
     (4)WRF模式不同PBL参数化方案与CMAQ模式模拟的SO2和NO2日均浓度均能反映出污染物时空分布特征,说明高分辨率的CMAQ模式能够研究复杂下垫面条件下的空气污染问题。
     (5)不同方案模拟结果表明:ACM2方案与CMAQ模式相结合模拟的污染物浓度与监测值相关性最好,模拟的SO2和NO2日均浓度与监测值浓度的相关系数分别为0.61和0.57;这是由于局地与非局地闭合的ACM2方案描述的湍流扩散特征更接近于实际大气,且气象模式与空气质量模式采用相同的湍流交换系数计算方法,因而模拟效果相对较好。
     (6)粗粒子排放源的不确定性导致模式对PM1o的浓度模拟效果相对不理想,但模拟的PM25浓度与TSP浓度具有一定的相关性(ACM2方案相关系数达0.398),说明CMAQ模式的化学传输模块可以反映污染物气相化学过程;CMAQ模式具有模拟细粒子在大气中的形成、转化及清除机制的能力,而对粗粒子在大气中形成转化机制的处理能力尚需进一步验证。
     (7)对模拟的兰州市城区的SO2、N02和PM2.5等主要污染物水平和垂直分布特征分析表明:受排放源的空间分布和气象场共同影响,污染物浓度的高值区分别在城关区的铁路局附近和西固区的兰化宾馆附近;污染物的浓度随高度线性减小,日间湍流混合能力增强,使得日间污染物浓度随高度递减率小于夜间。
     (8)高架源排放敏感性试验表明:试验期间高架源(有效源高H≥100m)排放的SO2和NO2分别占污染物排放总量的59%和72%;由高架源排放造成的SO2和NO2地面浓度日均值对兰州市主城区的贡献率分别为46%和49%;可见高架源排放对兰州市空气质量影响较大,控制高架排放量是治理兰州市大气污染的有效途径之一
     (9) CMAQ过程分析模块分析表明:对于SO2地面浓度,污染源排放的影响最大,其次是垂直扩散过程的影响;而对于NO2地面浓度,化学反应过程的影响最大,其次是污染源排放,这也是各种物理化学过程对NO2地面浓度的影响区别于SO2的主要特征。
The mesoscale weather model WRF and CMAQ model were used to simulate the characteristics of meteorological field and air polluted distributions in Xigu district of Lanzhou city in winter from the 27th of January to the 2nd of February in 2005, which are based on the analysis of atmospheric boundary-later observed and air quality monitoring. The capacities of WRF and CMAQ models over complex terrain were testified by comparing simulated results and observed data, the effects of meteorological fields from three PBL (Planetary Boundary Layer) schemes (YSU, MYJ and ACM2) on the simulated results of CMAQ model were discussed; The differences of three PBL schemes (YSU, MYJ and ACM2) on the retrieve of meteorological characteristics in lower layer were analyzed, the effects of meteorological fields from three PBL schemes on the simulated results of CMAQ model were also discussed. The results can improve the accuracy of numerical simulation and air pollution forecasting in Lanzhou city. The spatial and temporal distributions of air pollutants in Lanzhou city were also investigated by analyzing the contribution of the various physical-chemical processes on the concentration of SO2 and NO2; some sensitive numerical experiments were used to study the effects of higher emission sources on air quality in Lanzhou city. This would provide some scientific foundations for controlling air pollution and improve air quality in Lanzhou city.
     (1) Comparing the results of the observations and simulations shows that the metrological variables have similar characters in PBL with three PBL schemes in WRF, but the temperature from MYJ scheme was lower than that from the other two schemes during the night, with opposite results during the daytime, which is caused by stronger turbulence due to the short-wave radiation during the daytime.
     (2) Local closed MYJ parameterization presents more reasonable daily changes on surface temperature than the other non-local closed parameterizations (YSU and ACM2) due to the facts that traces and energy were dominated by local turbulence, which results in the weak turbulent transportation in winter stable condition especially in valley city.
     (3)Investigating observed and simulated profiles of the mean temperature shows that three PBL schemes have good characters during the night, while the temperature profiles simulated by ACM2 (combined with local and non-local scheme) PBL scheme in WRF in is better lower layers than that from the other schemes during the daytime. It also presents that MYJ gives more reasonable results in upper layer.
     (4) Spatial and temporal characters of pollutants were well simulated by WRF model combined with CAMQ model with different PBL parameterization, which also testified the capacity of studying pollutant problems by CMAQ model.
     (5) The simulated concentrations of air pollution using the ACM2 PBL scheme was in WRF model combining with CMAQ model were consistent with the observed data The correlation coefficients of simulated and observed SO2 and NO2 daily averaged concentration are 0.61 and 0.57 respectively. It is because that'the turbulent exchanges are well simulated by ACM2 PBL scheme. The same exchange coefficients adopted by air quality model and meteorology model is another reason.
     (6) The simulated PM10 concentration was not satisfied due to the uncertainty of course particles. Simulated PM2.5 concentration may be related to TSP concentration, which indicates that the chemical transport process in CMAQ model can illustrate the formation, change and clarity of fine particles, which must be tested for the coarse particles.
     (7) The horizontal and vertical distributions of SO2, NO2 and PM2.5 in Lanzhou city show that they are affected by the spatial distribution of emission sources and meteorological fields. There are two maximum concentrations of pollutions in Lanzhou, which located around Tieluju in Chengguan district and Lanhua hotel in Xigu district. Pollutant concentration decreases linearly with height. The vertical gradients of the concentration are smaller during daytime than that in night because of the stronger turbulent mixing in the daytime.
     (8) The results of sensitive experiments of the emissions of higher emission sources show that:SO2 and NO2 emissions of higher emission sources accounts for 59% and 72% in total emissions; and SO2 and NO2 concentration caused by higher emission sources accounts for 46% and 49% in daily averaged concentration. Controlling the emissions of higher emission sources is the effective method to control air pollution in Lanzhou city.
     (9) Analysis on physical and chemical processes of different pollutions shows that pollution emission is the main factor over effecting SO2 surface concentration, and the vertical diffusion is the secondly one, while chemical processes have significant effects on NO2 concentration.
引文
[1]洪钟祥,胡非.大气污染预测的理论和方法研究进展[J].气候与环境研究,1999,4(3):225—230.
    [2]王自发,庞成明,朱江,等.大气环境数值模拟研究新进展[J].大气科学,2008,32(4):987—995.
    [3]Kindap T. Identifying the Trans-Boundary Transport of Air Pollutants to the City of I stanbul Under Specific Weather Conditions [J]. Water Air-Soil Pollut,2008,189:279-289.
    [4]Arasa R., M.R. Soler, S. Ortega, et al. A performance evaluation of MM5/MNEQA/CMAQ air quality modelling system to forecast ozone concentrations in Catalonia. Journal of Mediterranean Meteorology & Climatology,2010,7:11-23
    [5]许莹.兰州市空气污染现状统计分析[J].环境研究与监测,2004,4:29—32.
    [6]张美根,韩志伟,雷孝恩.城市空气污染预报方法简述[J].气候与环境研究,2001,6(1):1.13—118.
    [7]王勤耕,夏思佳,万祎雪,等.当前城市空气质量预报方法存在的问题及新思路[J].环境科学技术,2009,32(4):189—192.
    [8]房小怡,蒋维楣,吴涧,等.城市空气质量数值预报模式系统及其应用[J].环境科学学报,2004,24(1):111—115.
    [9]佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测,2006,22(2):69—71.
    [10]唐孝炎,张远航,绍敏.大气环境化学[M].北京,高等教育出版社,2006.
    [11]王自发,谢付莹,王喜全,等.嵌套网格空气质量预报模式系统的发展与应用[J].大气科学,2006,30(5):778—790.
    [12]Chang J.S., S. Jin, Y. Li, et al. The SARMAP Air Quality Model part Ⅰ of SAQM Final Report, Air Resources Board. California Environmental Protection Agency,1996, Sacramento, CA.
    [13]Seaman N.L., D.R. Stauffer, and A.M. Lario-Gibbs. A multiscale four dimensional data assimilation applied in the San Joaquin Valley during SARMAP. Part Ⅰ:Modeling design and basic performance characteristics [J]. Applied Met,34:1739-1761.
    [14]Chang T. Y., D. P. Chock, B. I. Nance, et al. A photochemical extent parameter to aid ozone air quality management [J], Atmospheric Environment,1997,31:2787-279 4.
    [15]Hanna S. R., G. E. Moore and M. E. Fernau. Evaluaton of photochemical grid models (UAM-IV, UAM-V, and the ROM/UAM-IV couple) using data from the lake Michigan ozone study (LMOS) [J]. Atmospheric environment,1996,30:3265-3279.
    [16]Mathur R., U. Shankar, A. F. Hanna, et al. Multiscale Air Quality Simulation Platform (MAQSIP):Initial applications and performance for tropospheric ozone and particulate matter [J]. Journal of Geophysical Research,2005,110:13308-13331.
    [17]Nester K., H.-H. Panitz and F.Fiedler. Comparison of the DRAIS and EURAD model simulations of air pollution in a mesoscale area [J]. Meteorol Atmos Phys,1995,57:135-158.
    [18]Hass H., M. Memmesheimer, H. Gei. Simulation of the chernobyl radioactive cloud over Europe using the EURAD model [J]. Atmospheric Environment,1990,24:673-692.
    [19]Hass H., A. Ebel, H. Feldmann, et al. Evaluation studies with a regional chemical tra nsport model (EURAD) using air quality data from the EMEP monitoring network [J]. Atmospheric Environment,1993,27:867-887.
    [20]LeDuc S., K. Schere, J. Godowitch, et al. Models3/CMAQ Applications which illustrate capability and functionality. Air Pollution Modeling and Its Application,2004, Part 7,737-738.
    [21]房小怡,蒋维楣,吴涧,等.城市空气质量数值预报模式系统及其应用[J].环境科学学报, 2004,24(1):111—115.
    [22]Mebust M. R., B. K. Eder, F. S. Binkowski, et al. Models3 Community Multiscale Air Quality (CMAQ) model aerosol component [J], Geophys Res,2003,108:4184-4198.
    [23]Eder B. and S. Yu. A performance evaluation of the 2004 release of Models3 CMAQ [J]. Atmos Environ,2006,40:4811-4824.
    [24]Appel K. W., A. B. Gilliand, G. Sarwar, et al. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5:Sensitivities impacting model performance [J]. Atmos Environ,2007,41:9603-9615.
    [25]Appel K. W., P. V. Bhave, A. B. Gilliland et al. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5:Sensitivities impacting model performance; Part Ⅱ-particulate matter. Atmos. Environ.,2008(42):6057-6066.
    [26]Appel K. W., S. J. Roselle, R. C. Gilliam, et al. Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers [J]. Geosci Model Dev.2010,3:169-188.
    [27]Jimenez-Guerreroa P., O. Jorba, J. M. Baldasano, et al. The use of a modelling system as a tool for air quality management:Annual high-resolution simulations and evaluation [J]. Science of the total environment,2008,390:323-340.
    [28]O'Neill S. M. and B. K. Lamb. Intercomparison of the community multiscale air quality model and CALGRID using process analysis [J]. Environmental Science & Technology, 2005,39:5742-5753.
    [29]Park S. K., A. Marmur, S. B. Kim, et al. Evaluation of Fine Particle Number Concentrations in CMAQ [J]. Aerosol Science and Technology,2006,40:985-996.
    [30]Wu S.Y., S. Krishnan, Y. Zhang, et al. Modeling atmospheric transport and fate of ammonia in North Carolina—Part Ⅰ:Evaluation of meteorological and chemical predictions [J]. Atmospheric Environment,2008,42:3419-3436.
    [31]Seigneur C. Current status of air quality models for particulate matter [J]. Journal of Air and Waste Management Association,2001,51:1508-1521.
    [32]Isakov V., J. S. Irwin, J. Ching. Using CMAQ for Exposure Modeling and Characterizing the Subgrid Variability for Exposure Estimates [J]. Journal of Applied Meteorology and Climatology,2007,46:1354-1371.
    [33]Smyth S. C., W. M. Jiang, D. Z. Yin, et al. Evaluation of CMAQ O3 and PM2.5 Performance using Pacific 2001 measurement data [J]. Atmospheric Environment,2006,40:2735-2749.
    [34]Eder B., D. Kang, R. Mathur, et al.An operational evaluation of the Eta-CMAQ air quality forecast model [J]. Atmospheric Environment,2006,40:4894-4905.
    [35]Park S. K., A. Marmur, S. B. Kim, et al. Evaluation of fine particle number concentrations in CMAQ [J]. Aerosol Science and Technology,2006,40:985-996.
    [36]Jimenez P., O. Jorba, R. Parra, et al. Evaluation of MM5-EMICAT2000-CMAQ performance and sensitivity in complex terrain:High-resolution application to the northeastern iberian peninsula [J]. Atmospheric Environment,2006,40(26):5056-5072.
    [37]Park S. K., C. E. Cobb, K. Wade, et al. Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations [J]. Atmospheric Environment,2006,40:S563-S573.
    [38]Phillipsa S. B., P. L. Finkelstein. Comparison of spatial patterns of pollutant distribution with CMAQ predictions [J]. Atmospheric Environment,2006,40:4999-5009.
    [39]Jiang W., S. Smyth. E. Giroux. Differences between CMAQ fine mode particle and PM2.5 concentrations and their impact on model performance evaluation in the lower Fraser valley [J]. Atmospheric Environment,2006,40:4973-4985.
    [40]Sokhi R. S., R. San Jose, N. Kitwiroon, et al. Prediction of ozone levels in London using the MM5-CMAQ modelling system.[J]. Environmental Modelling & Software,2006,21:566-576.
    [41]Gilliland A. B., C. Hogrefe, R. W. Pinder, et al. Dynamic evaluation of regional air quality models:Assessing changes in O3 stemming from changes in emissions and meteorology [J]. Atmospheric Environment,2008,42:5110-5123.
    [42]Zhanga K. M.,A. S. Wexlerb. Modeling urban and regional aerosols—Development of the UCD Aerosol Module and implementation in CMAQ model [J]. Atmospheric Environment 2008,42:3166-3178.
    [43]Appel K. W., A. B. Gilliland, G. Sarwar, et al. Evaluation of the community multiscale air quality (CMAQ) model version 4.5:Sensitivities impacting model performance, Part Ⅰ—Ozone [J]. Atmospheric Environment,2007,41:9603-9615.
    [44]Appel K. W., P. V. Bhave, A.B. Gilliland, et al. Evaluation of the community multiscale air quality (CMAQ) model version 4.5:Sensitivities impacting model performance; Part Ⅱ-particulate matter [J]. Atmospheric Environment,2008,42:6057-6066.
    [45]Hogrefe C., B. Lynn, R. Goldberg, et al. A combined model-observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations [J]. Atmospheric Environment,2009,43:2561-2570.
    [46]Smyth S. C.,W. Jiang, H. Roth, et al. A comparative performance evaluation of the AURAMS and CMAQ air-quality modelling systems [J]. Atmospheric Environment,2009, 43:1059-1070.
    [47]Chuang M. T., J. S. Fu, C. J. Jang, et al. Simulation of long-range transport aerosols from the Asian Continent to Taiwan by a Southward Asian high-pressure system [J]. Science of the total environment,2008,406:168-179.
    [48]Kubilay N., S. Nickovic, C. Moulin, et al. An illustration of the transport and deposition of mineral dust onto the eastern Mediterranean [J]. Atmospheric Environment,2000,34:1293-1303.
    [49]Rodriguez S., X. Querol, A. Alastuey, et al. Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain [J]. Atmospheric Environment,2001,35(14):2433-2447.
    [50]Lelieveld J., H. Berresheim, S. Borrmann, et al. Global air pollution crossroads over the Mediterranean [J]. Science,2002,298:794-799.
    [51]Pe'reza C., P. Jime'neza, O. Jorbaa, et al. Influence of the PBL scheme on high-resolution photochemical simulations in an urban coastal area over the Western Mediterranean Atmospheric Environment[J],2006,(40):5274-5297
    [52]Koo Y-S., S-T. Kim, H-Y Yun, et al.The simulation of aerosol transport over East Asia region [J]. Atmospheric Research,2008,90:264-271.
    [53]Kindap T.. Identifying the Trans-Boundary Transport of Air Pollutants to the City of Istanbul Under Specific Weather Conditions [J]. Water Air Soil Pollute,2008,189:279-289.
    [54]Jimenez-Guerrer P., O. Jorba, J. M. Baldasan, et al. The use of a modelling system as a tool for air quality management:Annual high-resolution simulations and evaluation [J]. Science of the total environment,2008,390:323-340.
    [55]Shrestha K. L., A. Kondo, A. Kaga, et al. High-resolution modeling and evaluation of ozone air quality of Osaka using MM5-CMAQ system [J]. Journal of Environmental Sciences,2009, 21:782-789.
    [56]赵秀勇,程水源,陈东升,等.应用ARPS—CMAQ模拟研究石景山污染对北京的影响[J].环境科学学报,2007,27(12):2074—2079.
    [57]权建农,张晓山,段宁,等.中国西南和华南地区硫沉降数值模拟[J].高原气象,2007,26(2):326—332.
    [58]陈训来,冯业荣,王安宇,等.珠江三角洲城市群灰霾天气主要污染物的数值研究[J].中山大学学报(自然科学版),2007,46(4):103—107.
    [59]张美根.多尺度空气质量模式系统及其验证Ⅱ:东亚地区对流层臭氧及其前体物模拟[J].大气科学,2005,29(6):926—936.
    [60]王扬锋,马雁军.空气质量模式系统Model-3在沈阳市的数值模拟研究[J].环境科学学报,2007,27(3):487—493.
    [61]安兴琴,左洪超,吕世华,等.Models3空气质量模式对兰州市污染物输送的模拟[J].高原气象,2005,24(5):748—756.
    [62]Seaman N. L. Meteorological modeling for air-quality assessments [J]. Atmospheric E nvironment,2000,34:2231-2259.
    [63]Sistla G., N. Zhou, W. Hao, et al. Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies [J]. Atmospheric Environment,2001, 30:2011-2025.
    [64]Sistla G., W. Hao, J.-Y. Ku, et al. An operational evaluation of two regionalscale ozone air quality modeling systems over the eastern United States [J]. Bulletin of the American Meteorological Society,2001,82:945-963.
    [65]Saulo A. C., M. Seluchi, C. Campetella, et al. Error evaluation of NCEP and LAHM regional model daily forecasts over Southern South America [J]. Weather and Forecasting,2001,16: 697-712.
    [66]Vaughan J., B. Lamb, C. Frei, et al. A numerical daily air quality forecast system for The Pacific Northwest [J]. Bulletin of the American Meteorological Society,2004,85:549-561.
    [67]Gego E., C. Hogrefe, G. Kallos, et al. Examination of model predictions at different horizontal grid resolutions [J]. Environmental Fluid Mechanics,2005,5:63-85.
    [68]Pe'reza C., P. Jime'neza, O. Jorbaa, et al. Influence of the PBL scheme on high-resolution photochemical simulations in an urban coastal area over the Western Mediterranean [J], Atmospheric Environment,2006,40:5274-5297
    [69]Miao J.F., D. Chen. K. Wyser, et al. Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast:Influence of PBL and LSM parameterizations [J]. Meteorol Atmos Phys,2008,99:77-103.
    [70]Bossioli E., M. Tombrou, A. Dandou, et al. The Role of Planetary Boundary-Layer Parameterizations in the Air Quality of an Urban Area with Complex Topography Boundary-Layer [J]. Meteorol,2009,131:53-72.
    [71]Pielke R A.中尺度气象模拟[M].张香珍,杨长新译.北京,气象出版社,1990.402—451.
    [72]王式功,杨德保,黄建国.兰州市八种主要空气污染物浓度分布类型及其相互关系[J].兰州大学学报(自然科学版),1996,32(1):121—125.
    [73]杨德保,王式功,黄建国,等.兰州冬季大气污染与天气形势的统计分析.复杂地形上大气边界层和大气扩散的研究[M].北京,气象出版社,1993,159—165.
    [74]杨德保,王式功,黄建国.兰州市区大气污染与气象条件的关系[J].兰州大学学报(自然科学版),1994,30(1):132—136.
    [75]王式功,杨德保,尚可政,等.兰州市城区冬半年低空风特征及其与空气污染的关系[J].兰州大学学报(自然科学版),1997,33(3):97—105.
    [76]王式功,杨德保,李腊平,等.兰州城区冬半年冷锋活动及其对空气污染的影响[J].高原气象,1998,17(2):142—149.
    [77]尚可政,王式功,杨德保,等.兰州冬季空气污染与地面气象要素的关系[J].甘肃科学学报,1999,11:1—5.
    [78]张强,胡隐樵.浅谈兰州市城区大气环境污染与治理的若干问题[J].高原气象,1998,17(2):203—210.
    [79]胡隐樵,张强.兰州山谷大气污染的物理机制与防治对策[J].中国环境科学,1999,19(2):119—122.
    [80]Zhang L, C H Chen, and J Murlis. Study on Winter Air Pollution Control in Lanzhou, China [J]. Water Air & Soil Pollution.2001,127:351-372.
    [81]张强,吕世华,张光庶.山谷城市大气边界层结构及输送能力[J],高原气象,2003,22(4):346—353.
    [82]拓瑞芳,陈长和,张中锋.复杂地形上气象条件对城市空气污影响的数值模拟[J].兰州大学学报(自然科学版),1994,30(4):143—151.
    [83]姜金华,彭新东.复杂地形城市冬季大气污染的数值模拟研究[J].高原气象,2002,21(1):1—7.
    [84]安兴琴,安俊岭,吕世华,等.复杂地形城市SO2扩散特征的模拟研究[J],城市环境与城市生态,2005,18(3):23—26.
    [85]李江林,陈玉春,吕世华,等.利用RAMS模式对山谷城市冬季局地风场的数值模拟[J],高原气象,2009,28(6):40—49.
    [86]缪国军,张镭,舒红.利用WRF对兰州冬季大气边界层的数值模拟[J].气象科学,2007,27(2):169—175.
    [87]王海龙,张镭,陈长和,等.兰州市东部地区冬季低空风场和温度场分析[J].兰州大学学报(自然科学版),1999,35(14):17—123.
    [1]王自发,谢付莹,王喜全,等.嵌套网格空气质量预报模式系统的发展与应用[J].大气科学,2006,30(5):778—790.
    [2]Monti P., H. J. S. Fernando, M. Princevac, et al. Observations of flow and turbulence in the nocturnal boundary layer over a slope[J]. Journal of the Atmospheric Sciences,2002,59: 2513-2534.
    [3]Max K., Zhang A., S. Anthony, et al. Modeling urban and regional aerosols—Development of the UCD Aerosol Module and implementation in CMAQ model. Atmospheric Environment[J],2008,42:3166-3178.
    [4]O'Neill S. M.,& B. K. Lamb, Intercomparison of the community multi scale air quality model and CALGRID using process analysis[J]. Environmental Science & Technology,2005, 39:5742-5753.
    [5]Jang C. J., N. B. Chang. Development and applications of U.S. EPA's regulatory air quality modeling systems[J]. Journal of Chinese Institute of Environment,2000,10:19-34.
    [6]Byun D. W., A. Hanna. Models3 air quality model prototype science and computational concept development[C]. Transactions of Air waste Management Association Specialty Conference on Regional Photochemical Measurement and Modeling Studies,1993,197-212.
    [7]Robin L. D., D. W. Byun, J H Novak, et al. The next generation of integrated air quality modeling:EPA's Models3[J].Atmospheric Environment,1996,30(2):1925-1938.
    [8]Byun D. W., J. K.S. Chin g, et al. Development and implementation of the EPA's models3 initial operating version:Community Multi-scale Air Quality Model, air pollution modeling and its application [M], Plenum Publishing Coorp,1998,357-368.
    [9]Byun D. W.; J. Yong, et al.Description of the models3 community multi-scale air quality model[C], Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix,1998:264-268.
    [10]Byun D. W., J. K. S. Ching, Science algorithms of the EPA Models3 community multiscale Air Quality (CMAQ) modeling system. U. S. Environmental Protection Agency Rep.1999, EPA-600/R-99/030,727 pp. [Available from Office of Research and Development, EPA, Washington, DC 20460.
    [11]Carter W. P. L.,2000. Implementation of the SAPRC-99 chemical mechanism into the Models3 framework'[R]. Report to the United States Environmental Protection Agency, January 29.
    [12]Binkowski F. S., S. J. Roselle, Models3 Community Multi scale Air Quality (CMAQ) model aerosol componentI Model description [J]. Geophys Res,2003,108:4183, doi: 10.1029/2001JD001409.
    [13]Byun D. and K. L. Schere. Review of the governing equations, computational algorithms, and other components of the Models3 Community Multi scale Air Quality (CMAQ) modeling system [J]. Appl Mech Rev,2006,59:51-77.
    [14]Boylan J.W., M. T. Odman, J. G. Wilkinson, et al, Development of a comprehensive, multiscale'one-atmosphere' modeling system:application to the Southern Appalachian Mountains [J]. Atmospheric Environment,2002,36:3721-3734.
    [15]Jang J. C., H E Jeffries, D. Byun, et al, Sensitivity of ozone to model grid resolution-I: Application of high resolution regional acid deposition model[J]. Atmospheric Environment, 1995,29(21):3085-3100.
    [16]Jang J. C. C., H. E. Jeffries, S.Tonnesen. Sensitivity of ozone to model grid resolution-II: Detailed process analysis for ozone chemistry[J]. Atmospheric Environment,1995,29(21): 3101-3114.
    [17]Arunachalam S., A. Holland, M. Abraczinskas, et al, A quantitative assessment of the influence of grid resolution on predictions of future-year air quality in North Carolina, USA[J]. Atmospheric Environment,2006.40(26):5010-5026.
    [18]Jimenez P., O. Jorba, R. Parra, et al.Evaluation of MM5-EMICAT2000-CMAQ performance and sensitivity in complex terrain:High-resolution application to the northeastern iberian peninsula[J]. Atmospheric Environment,2006,40(26):5056-5072.
    [19]Nelson L., Seaman. Meteorological modeling for air-quality assessment[J]. Atmospheric Environment,2000, (34):2231-2259.
    [20]程兴宏,徐祥德,丁国安,等.MM5/WRF气象场模拟差异对CMAQ空气质量预报效果的影响[J].环境科学研究.2009,22(12):1411—1419.
    [21]Appel K. W., S. J. Roselle, R. C. Gilliam, et al. Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers [J]. Geosci Model Dev Discuss,2009,2:1081-1114.
    [22]Carolina Environmental Programs. Sparse Matrix Operator Kernel Emission (SMOKE) Modeling System [Z]. University of Carolina, Carolina Environmental Programs, Research Triangle Park, N.C.,2003.
    [1]赵鸣,苗曼倩,王彦昌.边界层气象学教程[M].北京,气象出版社,1991.
    [2]张强,胡隐樵.大气边界层物理学的研究进展和面临的科学问题[J].地球科学进展,2001,16(4):526—532.
    [3]胡非,洪钟祥,雷孝恩.大气边界层和大气环境研究进展.大气科学.2003,27(4):712-728.
    [4]张强.大气边界层气象学研究综述[J].干旱气象.2003,21(3):74—78.
    [5]刘红年,刘罡,蒋维楣.关于非均匀下垫面大气边界层研究的讨论[J].高原气象.2004,23(3):412—413.
    [6]陈长和,黄建国,程麟生,等.复杂地形上大气边界层和大气扩散的研究[J].气象出版社.北京,1993.
    [7]安兴琴,左洪超,吕世华,等Models3空气质量模式对兰州市污染物输送的模拟[J].高原气象,2005,24(5):748—756.
    [8]杜萍,陈长和,钱泽雨.兰州冬季城乡边界层高度的比较分析[J].兰州大学学报(自然科学版),2001,37(2):152—154.
    [9]胡波,张武,张镭,等.兰州市西固区冬季大气气溶胶粒子的散射特征[J].高原气象,2003,22(4):354—360.
    [1]Pleim J E. A combined local and nonlocal closure model for the atmospheric boundary layer. Part II:Application and evaluation in a mesoscale meteorological model [J]. Appl Meteor Climat,2007,46:1396-1409.
    [2]Zhang K S, H T Mao, K Civerolo, et al. Numerical Investigation of Boundary-Layer Evolution and Nocturnal Low-Level Jets:Local versus Non-Local PBL Schemes [J]. Environmental Fluid Mechanics,2001,1:171-208.
    [3]王晨稀.MM5模式中不同对流参数化方案对降水预报效果影响的对比试验[J].气象科学,2004,24(2):168—176.
    [4]蔡芗宁,寿绍文,钟青.边界层参数化方案对暴雨数值模拟的影响[J].南京气象学院学报.2006,29(3):365—370.
    [5]Braun S A, and W K Tao. Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations [J]. Mon Wea Rev,2000,128:3941-3961.
    [6]Bright D R, S L Mullen. The sensitivity of the numerical simulation of the southwest monsoon boundary layer to the choice of PBL turbulence parameterization in MM5 [J]. Wea Forecasting,2002,17:99-114.
    [7]Srinivas C V, R Venkatesan, A Bagavath Singh. Sensitivity of mesoscale simulations of land-sea breeze to boundary layer turbulence parameterization [J]. Atmos Environ,2007,41: 2534-2548.
    [8]Wyser, D Chen, H Ritchie. Impacts of boundary layer turbulence and land surface process parameterizations on. simulated sea breeze characteristics [J]. Ann Geophys,2009,27: 2303-2320.
    [9]Carlos Pe'reza, Pedro Jime'neza, Oriol Jorbaa, et al. Influence of the PBL scheme on high-resolution photochemical simulations in an urban coastal area over the Western Mediterranean. Atmospheric Environment [J].2006,40:5274-5297.
    [10]Miao J F, D Chen, K Wyser, et al. Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast:Influence of PBL and LSM parameterizations. Meteorol [J]. Atmos Phys,2008,99:77-103.
    [11]Bossioli E, M Tombrou, A Dandou, et al. The Role of Planetary Boundary-Layer Parameterizations in the Air Quality of an Urban Area with Complex Topography Boundary-Layer [J]. Meteorol,2009,131:53-72.
    [12]Zhang D L, W Z Zheng. Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations [J]. Appl Meteor,2004,43:157-169.
    [13]Pleim J E. Combined Local and Non-local ClosureModel for the Atmospheric Boundary Layer. Part 2:Application and Evaluation in a Mesoscale Model [J]. Appl Meteor Climat 2007,46:1396-1409.
    [14]Zhang Y, M K Dubey, S C Olsen, et al. Comparisons of WRF/Chem simulations in Mexico City with ground-based RAMA measurements during the 2006-MILAGRO [J]. Atmos Chem Phys,2009,9:3777-3798.
    [15]Hu X M, J W Nielsen-Gammon, F Q Zhang. Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model [J]. Journal of the Applied Meteorology and Climatology, doi: 10.1175/2010JAMC2432.1
    [16]Miglietta M M, S Zecchetto, F De Biasio. WRF model and ASAR-retrieved 10m wind field comparison in a case study over Eastern Mediterranean Sea [J]. Adv Sci Res,2010,4:83-88
    [17]陈炯,王建捷.北京地区夏季边界层结构日变化的高分辨模拟对比[J].应用气象学报,2006,17(4):403-411.
    [18]Zhang L, C H Chen, J Murlis. Study on Winter Air Pollution Control in Lanzhou, China [J]. Water Air & Soil Pollution,2001,127:351-372.
    [19]张强,吕世华,等.山谷城市大气边界层结构及输送能力[J],高原气象,2003,22(4):346—353.
    [20]Troen I. and L. Mahrt. A simple model of the atmospheric boundary layer sensitivity to surface evaporation [J]. Bound.-Layer Meteor.,1986,37:129-148.
    [21]Holtslag A. A. M., and B. A. Boville,. Local versus nonlocal boundary-layer diffusion in a global climate model [J]. J. Climate,1993,6:1825-1842.
    [22]Hong S.-Y., and H.-L. Pan. Nonlocal boundary layer vertical diffusion in a Medium-Range Forecast model [J]. MonWea Rev,1996,124:2322-2339.
    [23]Noh Y., W. G. Cheon, S.-Y. Hong, and S. Raasch. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor,2003, 107:401-427.
    [24]Hong Song-You, Yign Noh, Jimy Dudhia. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes [J]. American Meteorological Society.2006,134,2318-2341.
    [25]Mellor G.L. and T. Yamada. Development of a turbulence closure model for geophysical fluid problems [J]. Rev Geophys Space Phys,1982,20:851-875.
    [26]Janjic Z. I.,2002:Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note No.437,61 pp pp.
    [27]Beljaars A.C.M. The parameterization of surface fluxes in large-scale models under free convection [J]. Quart J Roy Meteor Soc,1994,121:255-270.
    [28]Pleim J. E. A combined local and nonlocal closure model for the atmospheric boundary layer. Part I:Model description and testing [J]. J Appl Meteor Climatol,2007a,46:1383-1395.
    [29]Pleim J. E. A combined local and nonlocal closure model for the atmospheric boundary layer. Part II:Application and evaluation in a mesoscale meteorological model [J]. J. Appl Meteor Climatol,2007b,46:1396-1409.
    [30]Pleim J. E. and J. S. Chang. A non-local closure model for vertical mixing in the convective boundary layer [J]. Atmos Environ,1992,26A,965-981.
    [31]邱崇践,胡泽勇.兰州附近地区地面风场的数值模拟实验[J].兰州大学学报(自然科学版),1987,23(2):97—100.
    [32]李江林,陈玉春,吕世华,等.利用RAMS模式对山谷城市冬季局地风场的数值模拟[J],高原气象,2009,28(5).
    [33]缪国军,张镭,舒红.利用WRF对冬季大气边界层的数值模拟[J].气象科学,2007,27(2):169—175.
    [34]王海龙,张镭,陈长和,等.兰州市东部地区冬季低空风场和温度场分析[J].兰州大学学报(自然科学版),1999,35(4):117—123.
    [35]安兴琴,陈玉春,吕世华.中尺度模式对冬季兰州市低空风场和温度场的数值模拟[J].高原气象,2002,21(2):186—192.
    [1]Pielke, R.A., Uliasz, M. Use of meteorological models as input to regional and mesoscale air quality models-limitations and strengths [J]. Atmospheric Environment,1998,32: 1455-1466.
    [2]Athanassiadis G.A., Rao, S.T., Ku J.-Y, et al. Boundary layer evolution and its influence on ground-level ozone concentrations [J]. Environmental Fluid Mechanics,2002,2:339-357.
    [3]Pirovano G., I. Coll, M. Bedogni, et al. On the influence of meteorological input on photochemical modeling of a severe episode over a coastal area [J]. Atmospheric Environment,2007,41:6445-6464
    [4]Lee S.-H., Kim Y.-K., Kim H.-S, et al. Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula [J]. Atmospheric Environment,2007,41:4451-4465.
    [5]Miao J-F, Chen D, Wyser K, et al. Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast:influence of PBL and LSM parameterizations [J]. Meteorology and Atmospheric Physics,2008,99:77-103.
    [6]Gilliam R.C., C. Hogrefe, S.T Rao. New methods for evaluating meteorological models used in air quality applications [J]. Atmospheric Environment,2006,40:5073-5086.
    [7]Seaman N.L. Meteorological modeling for air-quality assessments [J]. Atmospheric Environment,2000,34:2231-2259.
    [8]Sistla G., Zhou N., Hao W, et al. Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies [J]. Atmospheric Environment,2001, 30:2011-2025.
    [9]Pirovano G., Coll I., Bedogni M, et al. On the influence of meteorological input on photochemical modelling of a severe episode over a coastal area [J]. Atmospheric .Environment,2007,41:6445-6464.
    [10]Meij A. de, A. Gzella, C. Cuvelier, et al. The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations [J]. Atmos Chem Phys,2009,9: 6611-6632.
    [11]Steven C, Smyth, Dazhong Yin, et al. The Impact of GEM and MM5 Modeled Meteorological Conditions on CMAQ Air Quality Modeling Results in Eastern Canada and the Northeastern United States [J]. Journal of Applied Meteorology and Climatology,2006, 45:1525-1541.
    [12]Han Zhiwei, Meigen Zhang, Junling An. Sensitivity of air quality model prediction to parameterization of vertical eddy diffusivity [J]. Environ Fluid Mech,2009,9:73-89.
    [13]Lee S-H, Y-K Kim, H-S Kim, et al. Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula [J]. Atmospheric Environment,2007,41:4451-4465,
    [14]Ku J-Y, Mao H, Zhang K, et al. Numerical investigation of the effects of boundary-layer evolution on the predictions of ozone and the efficacy of emission control options in the northeastern United States [J]. Environmental Fluid Mechanics,2001,1:209-233.
    [15]Pe'rez, C., Jime'nez P, Jorba O, et al. Influence of the PBL scheme on high-resolution photochemical simulations in an urban coastal area over the Western Mediterranean [J], Atmospheric Environment,2006,40:5274-5297.
    [16]Borge R, V Alexandrov, J-J del Vas, et al. A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula [J]. Atmospheric Environment, 2008,42:8560-8574.
    [17]Holtslag A.A.M. Atmospheric boundary layers:modeling and parameterization. In: Encyclopedia of Atmospheric Sciences. Academic Press,2002, pp.253-261.
    [18]Stull R.B. An Introduction to Boundary Layer Meteorology. Kluwer Academic, Boston,2003, p.670.
    [19]Lin J-T, M-B. McElroy. Impacts of boundary layer mixing on pollutant vertical profiles in the lower troposphere:Implications to satellite remote sensing [J]. Atmospheric Environment, 2010,44:1726-1739.
    [20]Lin J-T, D Youn, X-Z Liang, et al. Global model simulation of summertime U.S. ozone diurnal cycle and its sensitivity to PBL mixing, spatial resolution, and emissions. Atmospheric Environment [J]. Atmospheric Environment,2008,42:8470-8483.
    [21]Lee P, Y-h Tang, D-w Kang. Impact of consistent boundary layer mixing approaches between NAM and CMAQ [J]. Environ Fluid Mech,2009,9:23-42.
    [22]王式功,杨德保,李腊平,等.兰州城区冬半年冷锋活动及其对空气污染的影响.高原气象,1998,17(2):142-149.
    [23]王式功,杨德保,尚可政,祁斌.城市空气污染预报研究[C].兰州,兰州大学出版社,2002,157-164.
    [24]刘吉,陈长和.兰州城市冬季大气气溶胶特征的综合观测研究.兰州大学学报(自然科学版).2003,39(4):104-108.
    [25]王鑫,奚晓霞,郭治龙,等.2002年兰州市春季大气气溶胶特征分析.兰州大学学报(自然科学版).2006,42(3):44-47.
    [26]奚晓霞,郭治龙,姚卡玲,等.兰州皋兰山顶春季大气气溶胶的监测与分析.兰州大学学报(自然科学版).2003,39(5):101—104.
    [1]王式功,杨德保,陈长和.兰州市不同季节大气污染物时空变化规律的对比分析[J].兰州大学学报(自然科学版),1994,30(3):150—155.
    [2]王希波,马安青,安兴琴.兰州市主要大气污染物浓度季节变化时空特征分析[J].中国环境监测,2007,23(4):61—64.
    [3]安兴琴,马安青,王惠林.基于GIS的兰州市大气污染空间分析[J].干旱区地理.2006,29(4):576—581.
    [4]Wang S.G., X.Y. Feng, X.Q. Zeng et al. A study on variations of concentrations of particulate matter with different sizes in Lanzhou, China [J]. Atmospheric Environment,2009, 43:2823-2828.
    [5]Zhang L, C.H. Chen and J Murlis. Stduy on winter air pollution control in Lanzhou, China [J]. Water, Air,& Soil Pollution,2001,127:351-372.
    [6]An X.Q., H.C. Zuo, L.J. Chen. Atmospheric Environmental Capacity of SO2 in Winterover Lanzhou in China:A Case Study [J]. Advances in atmospheric sciences.2007,24(4).
    [7]Chu P.C., Chen Y, Lu S, et al. Particulate air pollution in Lanzhou China [J]. Environ Int 2008,34(5):698-713.
    [8]Chu P.C., Y.C. Chen, S.H. Lu. Atmospheric effects on winter SO2 pollution in Lanzhou, China [J]. Atmospheric Research,2008,89:365-373.
    [9]拓瑞芳,陈长和,张中锋.复杂地形上气象条件对城市空气污染影响的数值模拟[J].兰州大学学报(自然科学版),1994,30(4):143—151.
    [10]姜金华,彭新东.复杂地形城市冬季大气污染的数值模拟研究[J].高原气象,2002,21(1):1—7.
    [11]安兴琴,安俊岭,吕世华,等.复杂地形城市SO2扩散特征的模拟研究[J].城市环境与城市生态,2005,18(3):23—26.
    [12]安兴琴,左洪超,吕世华,等Models3空气质量模式对兰州市污染物输送的模拟[J].高原气象,2005,24(5),748—756.
    [13]姜金华,彭新东.复杂地形上城市冬季大气污染的数值模拟研究[J].高原气象,2002,21(1):1—7.
    [1]Ma L.J., T.J. Zhang, Li Q.X.. Evaluation of ERA-40, NCEP-1, and NCEP-2 reanalysis air temperatures with ground-based measurements in China. Journal of geophysical research,2008, 113,D15115,doi:10.1029/2007JD009549.

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