空气质量模式“源同化”模型及排放源影响效应研究
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摘要
污染源排放清单是空气质量模式的重要输入数据,排放源的不确定性是影响当前空气质量模式研究结果准确性的主要因素之一,亦对污染源的调控产生较大影响。近年来排放源模式、源反演模型等间接源排放估算方法成为定量计算污染源排放量及其时空变化规律的主要研究方向之一,它们可以为空气质量模式提供高时空分辨率、多排放参数的污染源排放清单。采用简单易行、具有较高可信度和应用价值的“Nudging”源同化修正模型反演具有季节变化特征、高分辨率、动态的多尺度排放源清单,成为提高空气质量预报水平的重要途径之一。
本文研究的主要目的是采用多尺度源同化反演模型在中国各区域、各季节和不同天气条件下进行模拟试验研究,以探讨空气质量模式“源同化”模型的模拟效果及其理论认识;本文还研究气象场模拟技术的改进、不同空间分辨率的观测资料以及OMI高分辨率柱浓度卫星资料的应用对空气质量预报水平提高的显著性问题;本文着重讨论奥运时段华北不同地区污染源的不同排放控制情景对北京市空气质量的影响以及各种物理、化学过程的贡献率,试图寻找周边污染源的关键控制区,以提供奥运会期间北京地区空气质量保障与调控的科学依据。以下是本文的主要研究工作和结论总结。
(1)本文定量分析了“Nudging”污染源同化反演方法在中国不同地区、不同季节、不同天气过程的适用性及其对空气质量预报改进的长期客观效果检验。通过大样本模拟试验,揭示出中国区域排放源的月际和季节变化特征;证实引入源同化反演模型后,CMAQ模式在中国不同地区、不同季节和不同天气条件下预报水平均有较显著的改进。获取了全国47个城市区域具有季节变化特征的动态排放源,其预报效果明显优于2000年David Streets排放源;采用上述源同化反演方法,不仅显著改善了SO_2、NO_2浓度的预报趋势,而且可明显减少预报误差;SO_2排放源强增加导致源影响过程贡献的SO_2浓度增大是源同化方法提高SO_2浓度预报水平的主要物理机制,排放源的改善导致化学反应过程贡献的NO_2浓度增大是源同化方法提高NO_2浓度预报水平的主要化学机制;SO_2、NO_2排放源的改善对PM10、O3逐时预报以及O3最大值预报具有间接影响,可提高PM10、O3逐时及O3最大值浓度预报水平,趋势预报效果较好。
(2)比较了两种气象模式(MM5/WRF)提供的气象场对华北地区SO_2、NO_2源同化修正效果及其浓度预报水平的影响差异;采用CMAQ模式中的IPR过程分析模块着重分析了WRF模式中边界层高度、相对湿度模拟的改进对SO_2、NO_2源同化效果的影响。分析结果表明,边界层高度和相对湿度模拟的改进对于SO_2、NO_2浓度趋势模拟效果有一定的影响;相对气象模式模拟效果的改进而言,排放源的改善对于提高SO_2、NO_2浓度的趋势及浓度模拟水平的作用更显著。
(3)采用不同空间分辨率的SO_2、NO_2实测资料模拟分析了中国地区SO_2、NO_2不同观测信息密度对SO_2、NO_2源同化反演及空气质量预报的影响,重点分析了奥运时段华北地区SO_2、NO_2浓度加密观测对改善SO_2、NO_2排放源和空气质量预报的重要作用。分析结果表明,采用较高分辨率的实测资料进行SO_2、NO_2源同化修正时,可明显减小SO_2、NO_2浓度的预报误差,华北地区较高分辨率的观测信息对于改善源同化修正效果及奥运时段华北地区SO_2、NO_2浓度的趋势预报效果十分重要,尤其是对SO_2浓度的预报尤为重要。
(4)本文采用OMI SO_2、NO_2卫星遥感产品着重讨论北京周边地区SO_2、NO_2排放源的影响效应以及该产品在改善NOX源同化修正效果及提高NO_2浓度预报水平中的初步应用。提出了卫星遥感-地面大气污染观测综合分析及其卫星遥感变分场同化源技术途径。根据卫星遥感与地面观测相结合思路,利用地面实测污染资料变分订正卫星遥感OMI高分辨率柱浓度资料,并采用卫星遥感变分场源同化模型获取了华北地区高分辨率的动态NOX排放源,个例模拟试验研究表明该技术方案具有提高污染物预报水平的可行性。分析结果表明,无论冬季还是夏季,华北地区SO_2、NO_2实测浓度与OMI SO_2、NO_2卫星遥感柱浓度的高低值区分布较一致,SO_2、NO_2柱浓度卫星资料可适用于华北地区卫星遥感-地面观测综合变分分析;经变分订正的OMI SO_2、NO_2柱浓度的空间分布特征可看出,北京的西南、东南地区污染源对北京地区的SO_2、NO_2浓度的影响较大;采用经变分订正、分辨率较高的OMI NO_2卫星遥感资料同化修正排放源时,WRF-CMAQ模式对华北地区冬、夏季NO_2浓度水平预报和趋势预报可取得较显著的改善效果;采用经变分订正的、高分辨率的OMI NO_2卫星遥感资料进行源同化时,可模拟得到与实测浓度分布较一致的、细致的、高分辨率的NO_2浓度信息,弥补了采用地面有限实测资料模拟效果的不足,卫星观测资料对于城市区域尺度的NO_2浓度模拟尤为重要。OMI NO_2高分辨率卫星遥感资料对于奥运期间华北和北京地区的NOX排放源调控以及提高场馆尺度NO_2浓度预报水平有一定的实际应用和参考价值。
(5)在夏季奥运时段华北地区同化修正源的改善以及改进后的WRF模式技术的基础上,着重分析了北京区域气候背景、风向频率分布等气象条件以及奥运时段华北不同地区污染源的不同排放控制情景对北京市SO_2、NO_2、PM10、O3浓度的影响和各种物理、化学过程的贡献率。找到了周边污染源关键控制区,为奥运期间北京地区空气质量保障与调控决策提供科学依据。分析结果表明:1)周边污染源对北京地区空气质量影响的主要通道是北京的西南和东南部地区。北京西南部的污染源对北京地区SO_2、NO_2、PM10浓度的影响最大,东南部的污染源影响次之,西北部的污染源对北京地区SO_2和PM10有一定程度的影响。北京西南部对SO_2浓度影响程度最大,PM10次之,NO_2最小。周边污染源对北京地区O3浓度的影响较小;2)北京周边污染源应急调控方案需考虑当时的风场结构特征,选取“上风方”周边污染源为主要调控目标,其中重点调控北京周边城市西南与东南区域,其次为西北区域;另外在弱风或静风条件下需重点调控北京本地污染源。3)控制北京西南地区的SO_2、NO_2污染源排放时,北京及周边地区SO_2、NO_2、PM10三种污染物浓度的减少幅度形成由南向北的带状分布,而且带状分布地区PM10浓度减少比例存在日变化特征。4)夏季多数三级以上污染日北京地区SO_2、NO_2、PM10浓度减少幅度与北京主体“上风方”(西南、东南部)SO_2、NO_2污染源减排比例呈同步变化关系,且北京西南部三种污染物的减排效果明显优于东南部的调控效果,因此北京西南地区的污染源控制对于改善北京地区的空气质量更加重要。
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 also has a great effect on regulation of air pollution sources. Indirect estimation methods of emission sources such as inverse model of sources is one of important research direction on the quantitative calculation of discharge capacity of pollutant and the law of temporal and spatial variation for emission sources. And the pollution emission inventory of higher resolution and multiple emission parameters is provided to air quality models using the inverse models of sources. Inverse of high resolution and multi-scale emission inventory which has seasonal variation characteristics derived from the simple, reliable, accurate and adaptive nudging scheme is one of important approach to enhance the level of air quality forecasts.
The main aim of the paper is 1) to analyze simulation effect of the nudging-based inverse model and theoretical progress by simulated test in different region, season and weather conditions in China; 2) to evaluate the long-term objectivity of improving of the level of air quality forecasts by the above inverse model; 3) to study the impact of improved simulation of meteorological element field, application of observation data with different spatial resolutions and improved retrieval data of tropospheric SO_2 and NO_2 from OMI/Aura satellite(OMI SO_2 and OMI NO_2) with high resolution on correction effect of inverse model and improving of the level of air quality forecasts; 4) to evaluate the impact of different controlling situation of emission sources in different regions in north of China to air quality in Beijing, to compute contribution rate of different physical and chemical processes in the above controlling situation, to find the key controlling region of surrounding emission sources and to provide the scientific basis for service guarantee of air quality forecasts in Beijing and regulation of surrounding emission sources during the 2008 Olympic Games.
The following are the main conclusions and results:
(1) In this paper, the applicability of multi-scale and corrected emission inventory in different region, season and weather conditions in China using the nudging-based inverse modeling approach and the long-term objectivity of improving of the level of air quality forecasts by using the corrected emission inventory were evaluated quantitatively. The result of long-term simulation test showed that 1) the inverse model of SO_2 and NOX sources using adaptive nudging scheme is suitable for different region, season and weather conditions in China; 2) predictions of SO_2 and NO_2 concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data which is from Streets 2000; 3) the accurate relatively and multi-scale emission inventory which has seasonal variation characteristics is obtained by using the nudging-based inverse modeling approach; 4) the error field in the emission data can be reduced effectively through an inverse modeling procedure using observed air pollution levels, and using the improved emissions led to significant improvement in the forecasts of SO_2 and NO_2 concentrations in all seasons. 5) using the improved emissions of SO_2 and NOX indirectly effect hourly forecasts of PM10 and O3 and maximum forecasts of O3 in a day and the forecasting effect of PM10 and O3 is improved through the nudging-based inverse modeling approach.
(2) Comparison of the impact of simulation of meteorological element field using MM5 and WRF models to correction effect of inverse model and improving of the level of air quality forecasts was given. Emphasis was placed on the discussion of impact of improved simulation of Planet Boundary Layer (PBL) height and relative humidity using WRF model on correction effect of inverse model of SO_2 and NOX sources. The results indicated that improved simulation of PBL height and relative humidity has certain influence on improvement in the tend prediction of SO_2 and NO_2 concentrations, and can reduce obviously the forecasting error. In comparison with the impact of improved simulation of meteorological element field, correction effect of emission inventory on improvement in the tend prediction of SO_2 and NO_2 concentrations and reduction of forecasting error is more effective.
(3) The effect of observation information density of SO_2 and NO_2 concentrations on correction effect of inverse model and improving of the level of air quality forecasts was simulated by using the observed data of SO_2 and NO_2 concentrations with different resolution. The important effect of dense observational data of SO_2 and NO_2 concentrations in north of China on correction effect of inverse model and improving of the level of air quality forecasts was analyzed emphatically. The result showed that the forecasting error of SO_2 and NO_2 concentrations can be reduced effectively through the inverse modeling procedure using dense observational data with higher resolution. The dense observational data with higher resolution in north of China is extremely significant for correction effect of inverse model and improvement in tend prediction of SO_2 and NO_2 concentrations, especially for the forecasts of SO_2 concentration.
(4) The impact of surrounding emission sources to the level of SO_2 and NO_2 concentrations in Beijing by using the improved retrieval data of OMI SO_2 and OMI NO_2 with high resolution was evaluated. And application of OMI SO_2 and OMI NO_2 data to correction effect of inverse model and improving of the level of air quality forecasts was discussed emphatically. The results revealed that 1) the spatial distribution characteristics of OMI SO_2 and OMI NO_2 column densities is in accordance with that of observational data of SO_2 and NO_2 concentrations in winter or summer. And OMI SO_2 and OMI NO_2 column densities are applicable to variational processing by means of observational data of SO_2 and NO_2 concentrations on the ground in north of China; 2) from the spatial distribution characteristics of OMI SO_2 and OMI NO_2 column densities which corrected by variational method, the conclusion was obtained that surrounding emission sources in southwest and southeast area have a great effect on the level of SO_2 and NO_2 concentrations in Beijing; 3) the tend prediction and the level of NO_2 concentration are improved obviously using revised emission inventory of NO_2 source by OMI NO_2 data with high resolution, which corrected by variational method; 4) it has a certain reference and actual application value in improvement of emission inventory of NO_2 source in north of China and Beijing and improving of the forecast of NO_2 concentration around Olympic gymnasiums.
(5) The impact of different controlling situation of emission sources in different regions in north of China to air quality in Beijing and contribution rate of different physical and chemical processes in the above controlling situation were evaluated and computed, based on improved simulation of meteorological element field using WRF model and relatively accurate emission of SO_2 and NOX sources with high resolution, which are corrected by using the nudging-based inverse modeling approach. The results of control and sensitivity tests showed that 1) the main transport channels of impact of surrounding emission sources to air quality in Beijing are southwest and southeast area of Beijing. Among two main affected zone, southwest area has the most significant influence, the next is southeast area. And emission sources in northwest region have an influence on the levels of SO_2 and PM10 concentrations in Beijing to some extent. Among three main pollutants, emission sources in southwest area have the most significant influence on the level of SO_2 concentration in Beijing, the next is PM10 concentration, the least is NO_2 concentration. And the impact of surrounding emission sources to O3 concentration in Beijing is relatively small; 2) the regulation schemes of SO_2 and NOX emission sources around Beijing need consider characteristics of wind field structure at that time. The main regulation object is upwind emission sources. The key regulation area is southwest and southeast region around Beijing, the next is northwest region. As breeze and static wind blowing, the local emission sources are the key of regulation; 3) when emission of SO_2 and NOX sources in southwest region is controlled, decreasing amplitude of SO_2, NO_2 and PM10 concentrations in Beijing and adjacent area from south to north form the zonal distribution characteristics. And the decreasing ratio of PM10 concentration in the zonal distribution area has diurnal change. 4) as the grade of air pollution in summer in Beijing is third-level or above, the decreasing ratio of SO_2 and PM10 concentrations changes simultaneously with the decreasing ratio of emission of SO_2 and NOX sources in main upwind area of Beijing (southwest and southeast regions). 5) the regulation effect of surrounding emission sources in southwest area is superior to that in southeast area. Regulation of emission sources in southwest area of Beijing is very important for improving air quality in Beijing during the 2008 Olympic Games.
本文研究的主要目的是采用多尺度源同化反演模型在中国各区域、各季节和不同天气条件下进行模拟试验研究,以探讨空气质量模式“源同化”模型的模拟效果及其理论认识;本文还研究气象场模拟技术的改进、不同空间分辨率的观测资料以及OMI高分辨率柱浓度卫星资料的应用对空气质量预报水平提高的显著性问题;本文着重讨论奥运时段华北不同地区污染源的不同排放控制情景对北京市空气质量的影响以及各种物理、化学过程的贡献率,试图寻找周边污染源的关键控制区,以提供奥运会期间北京地区空气质量保障与调控的科学依据。以下是本文的主要研究工作和结论总结。
(1)本文定量分析了“Nudging”污染源同化反演方法在中国不同地区、不同季节、不同天气过程的适用性及其对空气质量预报改进的长期客观效果检验。通过大样本模拟试验,揭示出中国区域排放源的月际和季节变化特征;证实引入源同化反演模型后,CMAQ模式在中国不同地区、不同季节和不同天气条件下预报水平均有较显著的改进。获取了全国47个城市区域具有季节变化特征的动态排放源,其预报效果明显优于2000年David Streets排放源;采用上述源同化反演方法,不仅显著改善了SO_2、NO_2浓度的预报趋势,而且可明显减少预报误差;SO_2排放源强增加导致源影响过程贡献的SO_2浓度增大是源同化方法提高SO_2浓度预报水平的主要物理机制,排放源的改善导致化学反应过程贡献的NO_2浓度增大是源同化方法提高NO_2浓度预报水平的主要化学机制;SO_2、NO_2排放源的改善对PM10、O3逐时预报以及O3最大值预报具有间接影响,可提高PM10、O3逐时及O3最大值浓度预报水平,趋势预报效果较好。
(2)比较了两种气象模式(MM5/WRF)提供的气象场对华北地区SO_2、NO_2源同化修正效果及其浓度预报水平的影响差异;采用CMAQ模式中的IPR过程分析模块着重分析了WRF模式中边界层高度、相对湿度模拟的改进对SO_2、NO_2源同化效果的影响。分析结果表明,边界层高度和相对湿度模拟的改进对于SO_2、NO_2浓度趋势模拟效果有一定的影响;相对气象模式模拟效果的改进而言,排放源的改善对于提高SO_2、NO_2浓度的趋势及浓度模拟水平的作用更显著。
(3)采用不同空间分辨率的SO_2、NO_2实测资料模拟分析了中国地区SO_2、NO_2不同观测信息密度对SO_2、NO_2源同化反演及空气质量预报的影响,重点分析了奥运时段华北地区SO_2、NO_2浓度加密观测对改善SO_2、NO_2排放源和空气质量预报的重要作用。分析结果表明,采用较高分辨率的实测资料进行SO_2、NO_2源同化修正时,可明显减小SO_2、NO_2浓度的预报误差,华北地区较高分辨率的观测信息对于改善源同化修正效果及奥运时段华北地区SO_2、NO_2浓度的趋势预报效果十分重要,尤其是对SO_2浓度的预报尤为重要。
(4)本文采用OMI SO_2、NO_2卫星遥感产品着重讨论北京周边地区SO_2、NO_2排放源的影响效应以及该产品在改善NOX源同化修正效果及提高NO_2浓度预报水平中的初步应用。提出了卫星遥感-地面大气污染观测综合分析及其卫星遥感变分场同化源技术途径。根据卫星遥感与地面观测相结合思路,利用地面实测污染资料变分订正卫星遥感OMI高分辨率柱浓度资料,并采用卫星遥感变分场源同化模型获取了华北地区高分辨率的动态NOX排放源,个例模拟试验研究表明该技术方案具有提高污染物预报水平的可行性。分析结果表明,无论冬季还是夏季,华北地区SO_2、NO_2实测浓度与OMI SO_2、NO_2卫星遥感柱浓度的高低值区分布较一致,SO_2、NO_2柱浓度卫星资料可适用于华北地区卫星遥感-地面观测综合变分分析;经变分订正的OMI SO_2、NO_2柱浓度的空间分布特征可看出,北京的西南、东南地区污染源对北京地区的SO_2、NO_2浓度的影响较大;采用经变分订正、分辨率较高的OMI NO_2卫星遥感资料同化修正排放源时,WRF-CMAQ模式对华北地区冬、夏季NO_2浓度水平预报和趋势预报可取得较显著的改善效果;采用经变分订正的、高分辨率的OMI NO_2卫星遥感资料进行源同化时,可模拟得到与实测浓度分布较一致的、细致的、高分辨率的NO_2浓度信息,弥补了采用地面有限实测资料模拟效果的不足,卫星观测资料对于城市区域尺度的NO_2浓度模拟尤为重要。OMI NO_2高分辨率卫星遥感资料对于奥运期间华北和北京地区的NOX排放源调控以及提高场馆尺度NO_2浓度预报水平有一定的实际应用和参考价值。
(5)在夏季奥运时段华北地区同化修正源的改善以及改进后的WRF模式技术的基础上,着重分析了北京区域气候背景、风向频率分布等气象条件以及奥运时段华北不同地区污染源的不同排放控制情景对北京市SO_2、NO_2、PM10、O3浓度的影响和各种物理、化学过程的贡献率。找到了周边污染源关键控制区,为奥运期间北京地区空气质量保障与调控决策提供科学依据。分析结果表明:1)周边污染源对北京地区空气质量影响的主要通道是北京的西南和东南部地区。北京西南部的污染源对北京地区SO_2、NO_2、PM10浓度的影响最大,东南部的污染源影响次之,西北部的污染源对北京地区SO_2和PM10有一定程度的影响。北京西南部对SO_2浓度影响程度最大,PM10次之,NO_2最小。周边污染源对北京地区O3浓度的影响较小;2)北京周边污染源应急调控方案需考虑当时的风场结构特征,选取“上风方”周边污染源为主要调控目标,其中重点调控北京周边城市西南与东南区域,其次为西北区域;另外在弱风或静风条件下需重点调控北京本地污染源。3)控制北京西南地区的SO_2、NO_2污染源排放时,北京及周边地区SO_2、NO_2、PM10三种污染物浓度的减少幅度形成由南向北的带状分布,而且带状分布地区PM10浓度减少比例存在日变化特征。4)夏季多数三级以上污染日北京地区SO_2、NO_2、PM10浓度减少幅度与北京主体“上风方”(西南、东南部)SO_2、NO_2污染源减排比例呈同步变化关系,且北京西南部三种污染物的减排效果明显优于东南部的调控效果,因此北京西南地区的污染源控制对于改善北京地区的空气质量更加重要。
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 also has a great effect on regulation of air pollution sources. Indirect estimation methods of emission sources such as inverse model of sources is one of important research direction on the quantitative calculation of discharge capacity of pollutant and the law of temporal and spatial variation for emission sources. And the pollution emission inventory of higher resolution and multiple emission parameters is provided to air quality models using the inverse models of sources. Inverse of high resolution and multi-scale emission inventory which has seasonal variation characteristics derived from the simple, reliable, accurate and adaptive nudging scheme is one of important approach to enhance the level of air quality forecasts.
The main aim of the paper is 1) to analyze simulation effect of the nudging-based inverse model and theoretical progress by simulated test in different region, season and weather conditions in China; 2) to evaluate the long-term objectivity of improving of the level of air quality forecasts by the above inverse model; 3) to study the impact of improved simulation of meteorological element field, application of observation data with different spatial resolutions and improved retrieval data of tropospheric SO_2 and NO_2 from OMI/Aura satellite(OMI SO_2 and OMI NO_2) with high resolution on correction effect of inverse model and improving of the level of air quality forecasts; 4) to evaluate the impact of different controlling situation of emission sources in different regions in north of China to air quality in Beijing, to compute contribution rate of different physical and chemical processes in the above controlling situation, to find the key controlling region of surrounding emission sources and to provide the scientific basis for service guarantee of air quality forecasts in Beijing and regulation of surrounding emission sources during the 2008 Olympic Games.
The following are the main conclusions and results:
(1) In this paper, the applicability of multi-scale and corrected emission inventory in different region, season and weather conditions in China using the nudging-based inverse modeling approach and the long-term objectivity of improving of the level of air quality forecasts by using the corrected emission inventory were evaluated quantitatively. The result of long-term simulation test showed that 1) the inverse model of SO_2 and NOX sources using adaptive nudging scheme is suitable for different region, season and weather conditions in China; 2) predictions of SO_2 and NO_2 concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data which is from Streets 2000; 3) the accurate relatively and multi-scale emission inventory which has seasonal variation characteristics is obtained by using the nudging-based inverse modeling approach; 4) the error field in the emission data can be reduced effectively through an inverse modeling procedure using observed air pollution levels, and using the improved emissions led to significant improvement in the forecasts of SO_2 and NO_2 concentrations in all seasons. 5) using the improved emissions of SO_2 and NOX indirectly effect hourly forecasts of PM10 and O3 and maximum forecasts of O3 in a day and the forecasting effect of PM10 and O3 is improved through the nudging-based inverse modeling approach.
(2) Comparison of the impact of simulation of meteorological element field using MM5 and WRF models to correction effect of inverse model and improving of the level of air quality forecasts was given. Emphasis was placed on the discussion of impact of improved simulation of Planet Boundary Layer (PBL) height and relative humidity using WRF model on correction effect of inverse model of SO_2 and NOX sources. The results indicated that improved simulation of PBL height and relative humidity has certain influence on improvement in the tend prediction of SO_2 and NO_2 concentrations, and can reduce obviously the forecasting error. In comparison with the impact of improved simulation of meteorological element field, correction effect of emission inventory on improvement in the tend prediction of SO_2 and NO_2 concentrations and reduction of forecasting error is more effective.
(3) The effect of observation information density of SO_2 and NO_2 concentrations on correction effect of inverse model and improving of the level of air quality forecasts was simulated by using the observed data of SO_2 and NO_2 concentrations with different resolution. The important effect of dense observational data of SO_2 and NO_2 concentrations in north of China on correction effect of inverse model and improving of the level of air quality forecasts was analyzed emphatically. The result showed that the forecasting error of SO_2 and NO_2 concentrations can be reduced effectively through the inverse modeling procedure using dense observational data with higher resolution. The dense observational data with higher resolution in north of China is extremely significant for correction effect of inverse model and improvement in tend prediction of SO_2 and NO_2 concentrations, especially for the forecasts of SO_2 concentration.
(4) The impact of surrounding emission sources to the level of SO_2 and NO_2 concentrations in Beijing by using the improved retrieval data of OMI SO_2 and OMI NO_2 with high resolution was evaluated. And application of OMI SO_2 and OMI NO_2 data to correction effect of inverse model and improving of the level of air quality forecasts was discussed emphatically. The results revealed that 1) the spatial distribution characteristics of OMI SO_2 and OMI NO_2 column densities is in accordance with that of observational data of SO_2 and NO_2 concentrations in winter or summer. And OMI SO_2 and OMI NO_2 column densities are applicable to variational processing by means of observational data of SO_2 and NO_2 concentrations on the ground in north of China; 2) from the spatial distribution characteristics of OMI SO_2 and OMI NO_2 column densities which corrected by variational method, the conclusion was obtained that surrounding emission sources in southwest and southeast area have a great effect on the level of SO_2 and NO_2 concentrations in Beijing; 3) the tend prediction and the level of NO_2 concentration are improved obviously using revised emission inventory of NO_2 source by OMI NO_2 data with high resolution, which corrected by variational method; 4) it has a certain reference and actual application value in improvement of emission inventory of NO_2 source in north of China and Beijing and improving of the forecast of NO_2 concentration around Olympic gymnasiums.
(5) The impact of different controlling situation of emission sources in different regions in north of China to air quality in Beijing and contribution rate of different physical and chemical processes in the above controlling situation were evaluated and computed, based on improved simulation of meteorological element field using WRF model and relatively accurate emission of SO_2 and NOX sources with high resolution, which are corrected by using the nudging-based inverse modeling approach. The results of control and sensitivity tests showed that 1) the main transport channels of impact of surrounding emission sources to air quality in Beijing are southwest and southeast area of Beijing. Among two main affected zone, southwest area has the most significant influence, the next is southeast area. And emission sources in northwest region have an influence on the levels of SO_2 and PM10 concentrations in Beijing to some extent. Among three main pollutants, emission sources in southwest area have the most significant influence on the level of SO_2 concentration in Beijing, the next is PM10 concentration, the least is NO_2 concentration. And the impact of surrounding emission sources to O3 concentration in Beijing is relatively small; 2) the regulation schemes of SO_2 and NOX emission sources around Beijing need consider characteristics of wind field structure at that time. The main regulation object is upwind emission sources. The key regulation area is southwest and southeast region around Beijing, the next is northwest region. As breeze and static wind blowing, the local emission sources are the key of regulation; 3) when emission of SO_2 and NOX sources in southwest region is controlled, decreasing amplitude of SO_2, NO_2 and PM10 concentrations in Beijing and adjacent area from south to north form the zonal distribution characteristics. And the decreasing ratio of PM10 concentration in the zonal distribution area has diurnal change. 4) as the grade of air pollution in summer in Beijing is third-level or above, the decreasing ratio of SO_2 and PM10 concentrations changes simultaneously with the decreasing ratio of emission of SO_2 and NOX sources in main upwind area of Beijing (southwest and southeast regions). 5) the regulation effect of surrounding emission sources in southwest area is superior to that in southeast area. Regulation of emission sources in southwest area of Beijing is very important for improving air quality in Beijing during the 2008 Olympic Games.
引文
[1] 任阵海, 万本太, 苏福庆等,当前我国大气环境质量的几个特征. 环境科学研究, 2004(1):1-6.
[2] 平措. 我国城市大气污染现状及综合防治对策. 环境科学与管理, 2006,31(1): 18～21.
[3] 张继娟, 魏世强. 我国城市大气污染现状与特点. 四川环境, 2006,25(3): 104～112.
[4] 韩明霞,过孝民. 我国城市的大气污染及其对居民的健康影响. 城市规划, 2006,30(6): 84～87
[5] 郝冉. 大气污染的危害及防治措施. 工业安全与环保, 2005,31(6): 27～28
[6] 阚海东.上海市城区大气颗粒物污染对居民健康危害的经济学评价. 中国卫生经济, 2004,23(2): 8～11
[7] 唐孝炎,张远航,邵敏. 大气环境化学(第二版). 高等教育出版社, 2006, 447～449
[8] 王明星. 大气化学(第二版). 气象出版社, 1999, 31～87
[9] 郝吉明,马广大. 大气污染控制工程. 北京:高等教育出版社,2002
[10] 姚增权. 国外空气质量模式研究现状及展望. 电力环境保护,1999,15(3):27-31
[11] 雷孝恩,张美根等. 大气污染数值预报基础和模式. 气象出版社, 1998, 34～59
[12] 刘烽,吴增茂. 城市大气质量模式研究进展. 环境科学进展,1999,7(2)22～30
[13] Bret M. A multiple source approach to acute human health risk assessments. Waste Management,1998,18:377～384
[14] Matthew L.et al. Testing the USA EPA’S ISCST-Version 3 model on dioxins:a comparison of predicted and observed air and soil concentrations. Atmospheric Environment,2000,34: 3995～4010
[15] Jonathan I.L.et al. Using CALPUFF to evaluate the impacts of power plant emissions in Illinois:model sensitivity and implications. Atmospheric Environment,2002,36: 1063～1075
[16] Ying Zhou.et al. Estimating population exposure to power plant emissions using CALPUFF:a case study in Beijing,China. Atmospheric Environment,2003,37: 815～826
[17] 吴烨. 用 ISCST3 模式模拟北京市多源扩散. [本科学位论文]. 清华大学环境系, 1998
[18] 李冰,陈长和,余金香. 利用 EKMA 曲线制定光化学烟雾污染区域总量控制方案. 高原气象,1998,17(2): 111～119
[19] 胡泳涛,张远航,谢绍东,等. 利用轨迹模式模拟近地层臭氧日变化. 环境科学学报,1999,19(4): 346～351
[20] 王志国,阎怀中,李泰岳,等. 应用模式模拟济南市机动车污染的空间分布. 环境科学研究, 2000, 13(4): 26～30
[21] J. M.Hao, et al. A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing. Atmospheric Environment,2000,34: 453～465
[22] 袁敬. 北京市大气污染控制对策模拟研究. [硕士学位论文]. 清华大学环境系, 2001
[23] 王锷一,王志国,雷孝恩,等. 济南市大气燃煤污染诊断. 环境科学研究, 2002, 15(4): 16～19
[24] Leonard K.P. et al. The current state and future direction of Eulerian Models in simulating the tropospheric chemistry and transport of trace species: a review. Atmospheric Environment,1995,29(2): 189～222.
[25] Puttanna S.et al. Measured and predicted airshed concentrations of methyl bromide in an agricultural valley and applications to exposure assessments. Atmospheric Environment,2000,34: 3511～3523
[26] Morris R.E., T.C. Meyers. User’s Guide for the Urban Airshed Model,VolumeⅠ: User’s Manual for UAM(CB-IV),U.S. Environmental Protection Agency,1990,10～40
[27] Lamb R G. A Regional Scale Model of Photochemical Air Pollution, Part Ⅰ: Theoretical Formulation. U.S. Environmental Protection Agency,1983,10～40
[28] Lamb R G. A Regional Scale Model of Photochemical Air Pollution, Part Ⅱ: Input Processor Network Design. U.S. Environmental Protection Agency,1983,10～40
[29] Chang J. S., P. B. Middleton,et al. A three-dimensional Eulerian acid deposition model: Physical concepts and formulation. J.Geo. Res,1987,92:14681～14700
[30] Venkatram A.K., P.K Karamchandani,The ADOM Ⅱ Scavenging Module,1998,ENSR Consulting and Engineering Report.
[31] Steven R.G.et al.Evaluation of photochemical grid models(UAM-Ⅳ,UAM-Ⅴ,and the ROM/ UAM-Ⅳ couple) using data from the Lake Michigan Ozone Study(LMOS). Atmospheric Environment,1996,30(19): 3259～3279
[32] Armistead R., Robin D. NARSTO critical review of photochemical models and modeling. Atmospheric Environment,2000,34: 2283～2324
[33] 骆启仁,廖正军,雷孝恩. 重庆市大气污染控制规划. 重庆环境科学,1996,18(6): 8～14
[34] 张美根,韩志伟,雷孝恩. 天津市空气污染数值预报实验中的模式系统. 气候与环境研究,1999,4(3): 237～243
[35] 杨晰,李兴生. 近地面 O3 变化化学反应机理的数值研究. 大气科学,1999,23(4): 427～437
[36] X. Yang, et al. Numerical study of surface ozone in China during summer time. Journal of Geophysical Research, 1999, 104(D23): 30341～30349
[37] 韩志伟,杜世勇,雷孝恩,等. 城市空气污染数值预报模式系统及其应用. 中国环境科学,2002,22(3): 202～ 206
[38] 孟凡,徐峻,何毅,等. 贵阳市二氧化硫和酸沉降污染来源及控制战略. 环境科学研究,2002,15(1): 24～27
[39] 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. 高原气象,2002,21(1): 1～7
[40] Byun D.W.,A. Hanna. Models-3 air quality model prototype science and computational concept development. 1993,Transactions of Air waste Management Association Specialty Conference on Regional Photochemical Measurement and Modeling Studies,197～212
[41] Robin L.D., Byun D.W., Novak J. H., et al. The next generation of integrated air quality modeling: EPA’s models-3. Atmospheric Environment,1996,30(2): 1925～1938
[42] Byun D. W., J. K. S Ching,et al. Development and Implementation of the EPA’s Models-3 Initial Operating Version: Community Multi-scale Air Quality Model,Air Pollution Modeling and its Application,1998, Plenum Publishing Coorp,357～368
[43] Byun D. W., J. Yong,et a. Description of the Models-3 Community Multi-scale Air QualityModel,Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix,1998,264～268
[44] Byun, D.W., Ching, J.K.S. Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. EPA/600/R-99/030, 1999. USEPA National Exposure Research Laboratory, Research Triangle Park, NC.
[45] Byun, D.W., Kim, S. B., Kim, S.T., et al.. Science assessment of CAMx and CMAQ: I. model input and algorithm comparisons. Report to Texas Commission on Environmental Quality, October, 2004, 86pp.
[46] Byun, D.W., Kim, S.T., Czader, B., et al. Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston–Galveston ozone nonattainment area. Journal of Environmental Management, 2005, 75/4 (June), 285～301.
[47] Byun, D.W., Schere, K.L. Review of the governing equations, computational algorithms, and other components of the Models-3 community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 2006, 59 (2): 51～77
[48] Bullock O R, Brehme K A. Atmospheric mercury simulation using the CMAQ model: formulation description and analysis of wet deposition results. Atmospheric Environment,2002, 36(13): 2135～2146
[49] Arnold J R, Dennis R L, Tonnesen G S. Diagnostic evaluation of numerical air quality models with specialized ambient observation: testing the Community Multiscale Air Quality modeling system(CMAQ) at selected SOS 95 ground sites. Atmospheric Environment,2003, 37(9-10): 1185～1198
[50] Zhang M, Uno I, Carmichael G R, et al. Large-scale structure of trace gas and aerosol distribution over the western Pacific Ocean during the Transport and Chemical Evolution Over the Pacific(TRACE-P) experiment. J. Geophys. Res., 2003,108(D21): Art. No.8820
[51] Jimenez P, Baldasano J M. Ozone response to precursor controls in very complex terrains: Use of photochemical indicators to assess O3-NOX-VOC sensitivity in the northeastern Iberian Peninsula. J. Geophys. Res., 2004,109(D20): Art. No.D20309
[52] Hogrefe C, Biswas J, Lynn B, et al. Simulating regional-scale ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment,2004, 38(17): 2627～2638
[53] Zhang M, Uno I, Yoshida Y, et al. Transport and transformation of sulfur compounds over East Asia during the TRACE-P and ACE-Asia campaigns. Atmospheric Environment,2004, 38(40): 6947～6959
[54] Jang C. Simulations of Multiple Pollutants over the Pacific Regions Using the Models-3/CMAQ system. Third annual CMAS Models-3 conference, 2004, Chapel Hill, NC.
[55] Meigen Zhang. Modeling of organic carbon aerosol distributions over east asia in the springtime. China Particuology, 2004, 2(5): 192～195
[56] Phillip K. Gbor, Deyong Wen, Fan Meng, et al. Improved model for mercury emission, transport and deposition. Atmospheric Environment,2006, 40: 973～983
[57] XU Jun, ZHANG Yuanhang and Wang Wei. Numerical Study on the Impacts of Heterogeneous Reactions on Ozone Formation in the Beijing Urban Area. Advance in Atmospheric Science, 2006, 23(4): 605～614
[58] 张美根,韩志伟. TRACE-P 期间硫酸盐、硝酸盐和铵盐气溶胶的模拟研究. 高原气象,2003,22(1): 1～6
[59] 樊琦,蒙伟光,王雪梅,等. 美国环保局第三代空气质量预报和评估系统. 重庆环境科学,2003,25(11): 134～137
[60] 张美根. 多尺度空气质量模式系统及其验证 Ⅰ. 模式系统介绍与气象要素模拟. 大气科学,2005 a,29(5): 805～813
[61] 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. 大气科学,2005 b,29(6): 926～936
[62] 刘煜,李维亮,周秀骥.夏季华北地区二次气溶胶的模拟研究. 中国科学 D 辑,2005,35(增Ⅰ):156～166
[63] 安兴琴,左洪超,吕世华,等. Models-3 空气质量模式对兰州市污染物输送的模拟. 高原气象,2005,24(5): 748～756
[64] 张美根,徐永福,张仁健,等. 东亚地区春季黑碳气溶胶排放及其浓度分布. 地球物理学报,2005,48(1): 46～51
[65] 宜树华. 北京及其周边地区空气污染的动力和化学过程分析. [硕士学位论文]. 中国气象科学研究院,2002
[66] 许建明,徐祥德,刘煜,等. CMAQ-MOS 区域空气质量统计修正模型预报途径研究. 中国科学D辑,2005,35(增Ⅰ):131～144
[67] 许建明. 城市大气环境数值技术的集成、改进和应用研究:[博士学位论文]. 南京:南京信息工程大学,2006
[68] 朱帅. 长江三角洲臭氧和气溶胶数值模拟研究. [硕士学位论文]. 中国气象科学研究院,2006
[69] Keiya Yumimotoa, Itsushi Uno. Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation. Atmospheric Environment, 2006, 40: 6836～6845
[70] 白乃彬. 中国大陆 C02、S02:和 Nox l°Хl°网格排放估计,中国地区大气臭氧变化及其对气候环境的影响,气象出版社,1995,145～150
[71] Streets, D G, Gupta S, Waldhoff S T, et al. Black carbon emission in China. Atmospheric Environment, 2001, 35: 4281～4296
[72] Streets, D G, Bond ,T C, Carmichael G R, et al. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000,J.Geophys.Res., 2003,108(D21): 8809～8820
[73] Ma J, Aardenne, J. Impact of different emission invenioried on simulated tropospheric ozone over China: a regional chemical transport model evaluation. Atlnospheric Chemisty and Physics, 2004: 4:877～887
[74] 张强, Zbigniew klimont, David G. Streets,等. 中国人为源颗粒物排放模型及 2001 年排放清单估算. 自然科学进展,2006,16(2):223～231
[75] Jacob D J, Crawford J H, Kelb M M, et al. Transport and chemical evolution over the Pacific(TRACE-P) emission: Design, execution, and first results. J. Geophys. Res., 2003, 108(D20): 8781
[76] Huebert B J, Bates T, Russell P B, et al. An overview of ACE-Asia: Strategies for quantifying the relationship between Asian aerosols and their climatic impacts. J. Geophys. Res., 2003, 108(D23): 8633
[77] Bond, T C, Streets D G, Yaber K F, et al. A technology-based global inventory of black and organic carbon emission from combustion. J. Geophys. Res., 2004, 109(D14): 203
[78] 胡泳涛,张远航, 谢绍东,等. 区域高时空分辨率 VOC 天然源排放清单的建立. 环境科学,2001 ,22 (6) :1～6.
[79] 李伟,傅立新,郝吉明,等. 中国道路机动车 10 种污染物的排放量. 城市环境与城市生态, 2003,16(2):36-38.
[80] H. Elbern, H. Schmidt, O. Talagrand, et al. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environmental Modelling & Software, 2000 (15): 539～548
[81] Fisher, M., Lary, D.J.. Lagrangian four-dimensional variational data assimilation of chemical species. Quart. J. R. Meteorol. Soc , 1995, 121: 1681～1704
[82] Elbern, H., Schmidt, H., Ebel, A.. Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res. , 1997, 102 (D13): 15967～15985
[83] Elbern, H., Schmidt, H. A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling. J. Geophys. Res , 1999, 104: 18583～18598
[84] Yienger,J, Levy, H. Empirical model of global soil-biogenic NOx emissions. J. Geophys. Res. , 1995, 100: 11447～11464
[85] Reich, S, Gomez, D, et al. Artificial neural network for the identification of unknown air Pollution sources. Atmospheric Environment, 1999, 33(15): 3045～3052
[86] Michael, M., John, H. Inverse air pollution modeling of urban-scale carbon monoxide emission,Atmospheric Environment, 1995, 29(4): 497～516
[87] Belgamaschi, P, R Hein,et al. Inverse modeling of CO mixing ratios. J. Geophys. Res. ,1999,105: 1909～1928
[88] Bousquet,P, Ciais,P, et al.. Inverse modeling of annual atmospheric CO_2 sources and sinks.Ⅰ. Method and contro1 inversion,J. Geophys. Res. , 1999, 104: 26161～2 6178
[89] Hein, R, CrutZen,P, et al. An inverse modeling approach to investigate the global atmospheric methane cycle. Global Biogeochem Cycles, 1997, 11:43～76
[90] Houweling, S, Kaminski, T, et al. Inverse modeling of methane sources and sinks using the adjoint of a global transport model. J. Geophys. Res. , 1999, 104: 26137～2 6160
[91] Guenther, A, Newitt, C, et al. A global model of natural volatile organic compound emissions,J. Geophys. Res. , 1995, 100: 8873～8874
[92] Hartley D, Prinn, R. On the feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical transport model. J. Geophys. Res. , 1993, 98: 5183～5198
[93] Randall,V M, Kelly, C, et al. An improved regrieval of tropospheric nitrogen dioxide from GOME. J. Geophys. Res. , 2002, 107(D20): 4437～4 466
[94] 徐大海,朱蓉. 城市大气污染物源强反演. 大气环境和环境影响评价, 1998, 气象出版社
[95] 陈军明,徐大海,等. 遗传算法在点源扩散浓度反演排放源强中的应用. 气象, 2002,28(9):12～16
[96] 蔡旭辉,韵敏,等.甲烷排放源逆向轨迹反演模式研究. 环境科学,2002,23(5): 25～30
[97] 苏芳,韵敏,等. 利用逆向轨迹反应模式估算北京地区甲烷源强. 环境科学学报, 2002,22(5): 586～591
[98] 李灿,许黎,等. 一种大气 CO_2 源汇反演模式方法的建立及应用. 中国环境科学, 2003, 23(6): 610～613
[99] 刘峰,胡非,等. 用伴随方法求解多个工业污染源优化布局问题. 中国科学, D 辑, 2005, 35(l): 64～71
[100] 刘峰,胡非,等. 用伴随方程研究空气污染的优化控制. 环境科学学报, 2003, 23(4): 472～475
[101] Nelson L. Seaman. Meteorological modeling for air-quality assessments. Atmospheric Environment, 2000 (34): 2231～2259
[102] 王喜全, 虞统, 孙峰等. 北京 PM10 重污染预警预报关键因子研究. 气候与环境研究, 2006, 11(4): 470～476
[103] Martin, R.V., Jacob, D.J., Chance, K.V., et al. Global inventory of nitrogen dioxide emissions constrained by space-based observations of NO_2 columns. Journal of Geophysical Research, 2003,108: 4537.
[104] 程立刚,王艳姣,王耀庭. 遥感技术在大气环境监测中的应用综述. 中国环境监测, 2005, 21(5):17～23
[105] Richter, A., Burrows, J.P. Retrieval of tropospheric NO_2 from GOME measurements. Advances in Space Research, 2002, 29: 1673～1683.
[106] Martin, R.V., Chance, K., Jacob, D.J. An improved retrieval of tropospheric nitrogen dioxide from GOME. Journal of Geophysical Research, 2002, 107 (D20): 4437
[107] Boersma, K.F., Eskes, H.J., Brinksma, E.J. Error analysis for tropospheric NO_2 retrieval from space. Journal of Geophysical Research , 2004,109: D04311
[108] Boersma, K.F., Eskes, H.J., Veefkind, J.P., et al. Near-real time retrieval of tropospheric NO_2 from OMI. Atmospheric Chemistry and Physics , 2007, 7: 2103～2118
[109] Schaub, D., Brunner, D., Boersma, K.F., et al. SCIAMACHY tropospheric NO_2 over Switzerland: estimates of NOx lifetimes and impact of the complex Alpine topography on the retrieval. Atmospheric Chemistry and Physics, 2007, 7, 5971～5987
[110] Schaub, D., Boersma, K.F., Kaiser, J.W., et al. Comparison of GOME tropospheric NO_2 columns with NO_2 profiles deduced from ground-based in situ measurements. Atmospheric Chemistry and Physics, 2006, 6: 3211～3229
[111] Martin, R.V., Parrish, D.D., Ryerson, T.B., et al. Evaluation of GOME satellite measurements of tropospheric NO_2 and HCHO using regional data from aircraft campaigns in the southeastern United States. Journal of Geophysical Research, 2004, 109: D24307.
[112] Khokhar, M.F., Frankenberg, C., Hollwedel, J.,et al. Satellite remote sensing of atmospheric SO_2: volcanic eruptions and anthropogenic emissions, Proceedings of the ENVISAT & ERS Symposium, 6-10 September 2004, Salzburg, Austria, ESA publication SP-572 (CD-ROM).
[113] 江文华,马建中,颜鹏,等. 利用 GOME 卫星资料分析北京大气 NO_2 污染变化. 应用气象学报,2006,17(1): 67～72
[114] Boersma, K.F., Jacob, D.J., Bucsela, E.J. Validation of OMI tropospheric NO_2 observations during INTEX-B and application to constrain NOx emissions over the eastern United States and Mexico. Atmospheric Environment, 2008, doi:10.1016/j.atmosenv , in press.
[115] Boersma, K.F., Jacob, D.J., Eskes, H.J., et al. Intercomparison of SCIAMACHY and OMI tropospheric NO_2 columns: observing the diurnal evolution of chemistry and emissions from space. Journal of Geophysical Research, 2008, doi:10.1029/2007JD008816, in press.
[116] Wang, Y.X., McElroy, M.B., Boersma, K.F., et al. Traffic restrictions associated with the Sino-African summit: reductions of NOx detected from space.Geophysical Research Letters, 2007, 34: L08814
[117] Jeffries, H E, S. Tonnesen. A comparison of two photochemical reaction mechanisms using mass balance and process analysis. Atmospheric Environment, 1994, 28(18): 2991～3003
[118] Jang, J C, H E, Jeffries, S. Tonnesen, et al. Sensitivity of ozone to model grid resolution-Ⅱ. Detailed process analysis for ozone chemistry. Atmospheric Environment, 1995b, 29(21): 3101～3114
[119] 徐峻,张远航. 北京市区夏季 O3 生成过程分析. 环境科学学报, 2006, 26(6): 973～980
[120] 陈义珍,柴发合,段宁,等. “西电东送”火电规划对北京空气质量的影响. 环境科学研究, 2003,16(6): 46～52
[121] 徐祥德,周丽,周秀骥,等. 城市大气环境重污染过程周边源影响域. 中国科学, D 辑, 2004, 34(10): 958～966
[122] 徐祥德,周秀骥,施晓辉,等. 城市群落大气污染影响的空间结构及尺度特征. 中国科学D辑,2005,35(增Ⅰ):53～65
[123] 苏福庆, 高庆先, 张志刚等. 北京边界层外来污染物输送通道. 环境科学研究, 2004,17(1): 26～29
[124] 清华大学. 北京市大气污染综合防治对策研究. 北京:清华大学,2001,12
[125] 张志刚, 高庆先, 韩雪琴等. 中国华北区域城市间污染物输送研究. 环境科学研究, 2004,17(1): 14～20
[126] 孟伟,高庆先,张志刚等. 北京及周边地区大气污染数值模拟研究. 环境科学研究, 2006,19(5): 11～18
[127] 郝吉明,王丽涛,李 林,等. 北京市能源相关大气污染源的贡献率和调控对策分析. 中国科学D辑,2005,35(增Ⅰ):115～122
[128] 颜鹏,黄健,等. 周边地区对北京地面 SO_2 影响的初步研究. 应用气象学报,2002,13(特刊):144～152
[129] 颜鹏,黄健,Roland Draxler,等. 北京地区 SO_2 污染的长期模拟及不同类型排放源的计算与评估. 中国科学D辑,2005,35(增Ⅰ):167～176
[130] 张金善,钟中,黄瑾. 中尺度大气模式 MM5 简介. 海洋预报,2005,22(1): 31～40
[131] 丑纪范. 四维同化的理论和新方法. 数值天气预报中的若干新技术[M]. 气象出版社,1995.
[132] 国家环境保护局. 2004 中国环境统计年报. 北京:中国环境科学出版社,2005
[133] 范元中. 空气污染指数计算程序. 监测与评价,1998, 8: 19～20
[134] Welsh P , Wildman A , Shaw B ,et al. Implementing the Weather Research and Forecast (WRF) model with local data Assimilation in a NWS WFO [C] ∥84 th AMS Annual Meeting , Seattle , U.S. A. 2004.
[135] Hiroyuki Kusaka, Andrew Crook, Jimy Dudhia, et al. Comparison of the WRF and MM5 Models for Simulation of Heavy Rainfall along the Baiu Front[J]. Scientific Online Letters on the Atmosphere(SOLA), 2005, 1, 197～200
[136] 孙健, 赵平. 用 WRF 与 MM5 模拟 1998 年三次暴雨过程的对比分析 . 气象学报, 2003 ,61 (6) : 692～701.
[137] 张芳华,马旭林,杨克明. 2003 年 6 月 24～25 日江南特大暴数值模拟和诊断分析. 气象, 2004 ,30 (1) : 28～32.
[138] 刘宁微, 王奉安. WRF 和 MM5 模式对辽宁暴雨模拟的对比分析. 气象 科技, 2006, 34(4): 364～369
[139] 赵洪, 杨学联, 邢建勇等. WRF 与 MM5 对 2007 年 3 月初强冷空气数值预报结果的对比分析[J]. 海洋预报, 2007, 24(2): 1～8
[140] 章毅之, 辜晓青. 气象要素的等值线绘制技术[J]. 江西气象科技,2002,25(1): 30～33.
[141] http://aura.gsfc.nasa.gov/instruments/omi/index.html
[142] Krotkov, N.A., S.A. Carn, A.J. Krueger, et al. Band residual difference algorithm for retrieval of SO_2 from the Aura Ozone Monitoring Instrument(OMI). IEEE Trans.Geosci. Remote Sensing, AURA special issue, 2006, 44(5): 1259～1266
[143] Taubman, B. F., et al. Aircraft vertical profiles of trace gas and aerosol pollution over the mid-Atlantic United States: Statistics and meteorological cluster analysis. Journal of Geophysical Research-Atmospheres, 2006,111.
[144] Boersma, K. F., E. J. Bucsela, E. J. Brinksma, et al. NO_2, OMI-EOS Algorithm Theoretical Basis Document: Trace Gas Algorithms: NO_2, 2001, 4:12～35
[145] Bucsela, E., E. Celarier, M. Wenig, et al. Algorithm for NO_2 vertical column retrieval from the Ozone Monitoring Instrument. IEEE Transactions on Geoscience and Remote Sensing, 2006,44: 1245～1258
[146] Edward A. Celarier, James F. Gleason,Bojan Bojkov, et al. Evaluation of OMI NO_2 Data Quality. 2006
[147] 周秀骥,徐祥德. 2000 年春季沙尘暴动力学特征. 中国科学,D 辑,2002,32(4): 327～334.
[148] 徐祥德,周秀骥,翁永辉,等. 星载 MODIS 资料与地面光度计探测气溶胶变分场. 科学通报,2003,15:1680～1685.
[149] 徐祥德,许建民,王继志,等. 大气遥感再分析场构造技术与原理. 北京: 气象出版社,2003:58～91.
[150] http://www.bjee.org.cn/api/index.php?stat=month
[2] 平措. 我国城市大气污染现状及综合防治对策. 环境科学与管理, 2006,31(1): 18～21.
[3] 张继娟, 魏世强. 我国城市大气污染现状与特点. 四川环境, 2006,25(3): 104～112.
[4] 韩明霞,过孝民. 我国城市的大气污染及其对居民的健康影响. 城市规划, 2006,30(6): 84～87
[5] 郝冉. 大气污染的危害及防治措施. 工业安全与环保, 2005,31(6): 27～28
[6] 阚海东.上海市城区大气颗粒物污染对居民健康危害的经济学评价. 中国卫生经济, 2004,23(2): 8～11
[7] 唐孝炎,张远航,邵敏. 大气环境化学(第二版). 高等教育出版社, 2006, 447～449
[8] 王明星. 大气化学(第二版). 气象出版社, 1999, 31～87
[9] 郝吉明,马广大. 大气污染控制工程. 北京:高等教育出版社,2002
[10] 姚增权. 国外空气质量模式研究现状及展望. 电力环境保护,1999,15(3):27-31
[11] 雷孝恩,张美根等. 大气污染数值预报基础和模式. 气象出版社, 1998, 34～59
[12] 刘烽,吴增茂. 城市大气质量模式研究进展. 环境科学进展,1999,7(2)22～30
[13] Bret M. A multiple source approach to acute human health risk assessments. Waste Management,1998,18:377～384
[14] Matthew L.et al. Testing the USA EPA’S ISCST-Version 3 model on dioxins:a comparison of predicted and observed air and soil concentrations. Atmospheric Environment,2000,34: 3995～4010
[15] Jonathan I.L.et al. Using CALPUFF to evaluate the impacts of power plant emissions in Illinois:model sensitivity and implications. Atmospheric Environment,2002,36: 1063～1075
[16] Ying Zhou.et al. Estimating population exposure to power plant emissions using CALPUFF:a case study in Beijing,China. Atmospheric Environment,2003,37: 815～826
[17] 吴烨. 用 ISCST3 模式模拟北京市多源扩散. [本科学位论文]. 清华大学环境系, 1998
[18] 李冰,陈长和,余金香. 利用 EKMA 曲线制定光化学烟雾污染区域总量控制方案. 高原气象,1998,17(2): 111～119
[19] 胡泳涛,张远航,谢绍东,等. 利用轨迹模式模拟近地层臭氧日变化. 环境科学学报,1999,19(4): 346～351
[20] 王志国,阎怀中,李泰岳,等. 应用模式模拟济南市机动车污染的空间分布. 环境科学研究, 2000, 13(4): 26～30
[21] J. M.Hao, et al. A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing. Atmospheric Environment,2000,34: 453～465
[22] 袁敬. 北京市大气污染控制对策模拟研究. [硕士学位论文]. 清华大学环境系, 2001
[23] 王锷一,王志国,雷孝恩,等. 济南市大气燃煤污染诊断. 环境科学研究, 2002, 15(4): 16～19
[24] Leonard K.P. et al. The current state and future direction of Eulerian Models in simulating the tropospheric chemistry and transport of trace species: a review. Atmospheric Environment,1995,29(2): 189～222.
[25] Puttanna S.et al. Measured and predicted airshed concentrations of methyl bromide in an agricultural valley and applications to exposure assessments. Atmospheric Environment,2000,34: 3511～3523
[26] Morris R.E., T.C. Meyers. User’s Guide for the Urban Airshed Model,VolumeⅠ: User’s Manual for UAM(CB-IV),U.S. Environmental Protection Agency,1990,10～40
[27] Lamb R G. A Regional Scale Model of Photochemical Air Pollution, Part Ⅰ: Theoretical Formulation. U.S. Environmental Protection Agency,1983,10～40
[28] Lamb R G. A Regional Scale Model of Photochemical Air Pollution, Part Ⅱ: Input Processor Network Design. U.S. Environmental Protection Agency,1983,10～40
[29] Chang J. S., P. B. Middleton,et al. A three-dimensional Eulerian acid deposition model: Physical concepts and formulation. J.Geo. Res,1987,92:14681～14700
[30] Venkatram A.K., P.K Karamchandani,The ADOM Ⅱ Scavenging Module,1998,ENSR Consulting and Engineering Report.
[31] Steven R.G.et al.Evaluation of photochemical grid models(UAM-Ⅳ,UAM-Ⅴ,and the ROM/ UAM-Ⅳ couple) using data from the Lake Michigan Ozone Study(LMOS). Atmospheric Environment,1996,30(19): 3259～3279
[32] Armistead R., Robin D. NARSTO critical review of photochemical models and modeling. Atmospheric Environment,2000,34: 2283～2324
[33] 骆启仁,廖正军,雷孝恩. 重庆市大气污染控制规划. 重庆环境科学,1996,18(6): 8～14
[34] 张美根,韩志伟,雷孝恩. 天津市空气污染数值预报实验中的模式系统. 气候与环境研究,1999,4(3): 237～243
[35] 杨晰,李兴生. 近地面 O3 变化化学反应机理的数值研究. 大气科学,1999,23(4): 427～437
[36] X. Yang, et al. Numerical study of surface ozone in China during summer time. Journal of Geophysical Research, 1999, 104(D23): 30341～30349
[37] 韩志伟,杜世勇,雷孝恩,等. 城市空气污染数值预报模式系统及其应用. 中国环境科学,2002,22(3): 202～ 206
[38] 孟凡,徐峻,何毅,等. 贵阳市二氧化硫和酸沉降污染来源及控制战略. 环境科学研究,2002,15(1): 24～27
[39] 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. 高原气象,2002,21(1): 1～7
[40] Byun D.W.,A. Hanna. Models-3 air quality model prototype science and computational concept development. 1993,Transactions of Air waste Management Association Specialty Conference on Regional Photochemical Measurement and Modeling Studies,197～212
[41] Robin L.D., Byun D.W., Novak J. H., et al. The next generation of integrated air quality modeling: EPA’s models-3. Atmospheric Environment,1996,30(2): 1925～1938
[42] Byun D. W., J. K. S Ching,et al. Development and Implementation of the EPA’s Models-3 Initial Operating Version: Community Multi-scale Air Quality Model,Air Pollution Modeling and its Application,1998, Plenum Publishing Coorp,357～368
[43] Byun D. W., J. Yong,et a. Description of the Models-3 Community Multi-scale Air QualityModel,Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix,1998,264～268
[44] Byun, D.W., Ching, J.K.S. Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. EPA/600/R-99/030, 1999. USEPA National Exposure Research Laboratory, Research Triangle Park, NC.
[45] Byun, D.W., Kim, S. B., Kim, S.T., et al.. Science assessment of CAMx and CMAQ: I. model input and algorithm comparisons. Report to Texas Commission on Environmental Quality, October, 2004, 86pp.
[46] Byun, D.W., Kim, S.T., Czader, B., et al. Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston–Galveston ozone nonattainment area. Journal of Environmental Management, 2005, 75/4 (June), 285～301.
[47] Byun, D.W., Schere, K.L. Review of the governing equations, computational algorithms, and other components of the Models-3 community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 2006, 59 (2): 51～77
[48] Bullock O R, Brehme K A. Atmospheric mercury simulation using the CMAQ model: formulation description and analysis of wet deposition results. Atmospheric Environment,2002, 36(13): 2135～2146
[49] Arnold J R, Dennis R L, Tonnesen G S. Diagnostic evaluation of numerical air quality models with specialized ambient observation: testing the Community Multiscale Air Quality modeling system(CMAQ) at selected SOS 95 ground sites. Atmospheric Environment,2003, 37(9-10): 1185～1198
[50] Zhang M, Uno I, Carmichael G R, et al. Large-scale structure of trace gas and aerosol distribution over the western Pacific Ocean during the Transport and Chemical Evolution Over the Pacific(TRACE-P) experiment. J. Geophys. Res., 2003,108(D21): Art. No.8820
[51] Jimenez P, Baldasano J M. Ozone response to precursor controls in very complex terrains: Use of photochemical indicators to assess O3-NOX-VOC sensitivity in the northeastern Iberian Peninsula. J. Geophys. Res., 2004,109(D20): Art. No.D20309
[52] Hogrefe C, Biswas J, Lynn B, et al. Simulating regional-scale ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment,2004, 38(17): 2627～2638
[53] Zhang M, Uno I, Yoshida Y, et al. Transport and transformation of sulfur compounds over East Asia during the TRACE-P and ACE-Asia campaigns. Atmospheric Environment,2004, 38(40): 6947～6959
[54] Jang C. Simulations of Multiple Pollutants over the Pacific Regions Using the Models-3/CMAQ system. Third annual CMAS Models-3 conference, 2004, Chapel Hill, NC.
[55] Meigen Zhang. Modeling of organic carbon aerosol distributions over east asia in the springtime. China Particuology, 2004, 2(5): 192～195
[56] Phillip K. Gbor, Deyong Wen, Fan Meng, et al. Improved model for mercury emission, transport and deposition. Atmospheric Environment,2006, 40: 973～983
[57] XU Jun, ZHANG Yuanhang and Wang Wei. Numerical Study on the Impacts of Heterogeneous Reactions on Ozone Formation in the Beijing Urban Area. Advance in Atmospheric Science, 2006, 23(4): 605～614
[58] 张美根,韩志伟. TRACE-P 期间硫酸盐、硝酸盐和铵盐气溶胶的模拟研究. 高原气象,2003,22(1): 1～6
[59] 樊琦,蒙伟光,王雪梅,等. 美国环保局第三代空气质量预报和评估系统. 重庆环境科学,2003,25(11): 134～137
[60] 张美根. 多尺度空气质量模式系统及其验证 Ⅰ. 模式系统介绍与气象要素模拟. 大气科学,2005 a,29(5): 805～813
[61] 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. 大气科学,2005 b,29(6): 926～936
[62] 刘煜,李维亮,周秀骥.夏季华北地区二次气溶胶的模拟研究. 中国科学 D 辑,2005,35(增Ⅰ):156～166
[63] 安兴琴,左洪超,吕世华,等. Models-3 空气质量模式对兰州市污染物输送的模拟. 高原气象,2005,24(5): 748～756
[64] 张美根,徐永福,张仁健,等. 东亚地区春季黑碳气溶胶排放及其浓度分布. 地球物理学报,2005,48(1): 46～51
[65] 宜树华. 北京及其周边地区空气污染的动力和化学过程分析. [硕士学位论文]. 中国气象科学研究院,2002
[66] 许建明,徐祥德,刘煜,等. CMAQ-MOS 区域空气质量统计修正模型预报途径研究. 中国科学D辑,2005,35(增Ⅰ):131～144
[67] 许建明. 城市大气环境数值技术的集成、改进和应用研究:[博士学位论文]. 南京:南京信息工程大学,2006
[68] 朱帅. 长江三角洲臭氧和气溶胶数值模拟研究. [硕士学位论文]. 中国气象科学研究院,2006
[69] Keiya Yumimotoa, Itsushi Uno. Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation. Atmospheric Environment, 2006, 40: 6836～6845
[70] 白乃彬. 中国大陆 C02、S02:和 Nox l°Хl°网格排放估计,中国地区大气臭氧变化及其对气候环境的影响,气象出版社,1995,145～150
[71] Streets, D G, Gupta S, Waldhoff S T, et al. Black carbon emission in China. Atmospheric Environment, 2001, 35: 4281～4296
[72] Streets, D G, Bond ,T C, Carmichael G R, et al. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000,J.Geophys.Res., 2003,108(D21): 8809～8820
[73] Ma J, Aardenne, J. Impact of different emission invenioried on simulated tropospheric ozone over China: a regional chemical transport model evaluation. Atlnospheric Chemisty and Physics, 2004: 4:877～887
[74] 张强, Zbigniew klimont, David G. Streets,等. 中国人为源颗粒物排放模型及 2001 年排放清单估算. 自然科学进展,2006,16(2):223～231
[75] Jacob D J, Crawford J H, Kelb M M, et al. Transport and chemical evolution over the Pacific(TRACE-P) emission: Design, execution, and first results. J. Geophys. Res., 2003, 108(D20): 8781
[76] Huebert B J, Bates T, Russell P B, et al. An overview of ACE-Asia: Strategies for quantifying the relationship between Asian aerosols and their climatic impacts. J. Geophys. Res., 2003, 108(D23): 8633
[77] Bond, T C, Streets D G, Yaber K F, et al. A technology-based global inventory of black and organic carbon emission from combustion. J. Geophys. Res., 2004, 109(D14): 203
[78] 胡泳涛,张远航, 谢绍东,等. 区域高时空分辨率 VOC 天然源排放清单的建立. 环境科学,2001 ,22 (6) :1～6.
[79] 李伟,傅立新,郝吉明,等. 中国道路机动车 10 种污染物的排放量. 城市环境与城市生态, 2003,16(2):36-38.
[80] H. Elbern, H. Schmidt, O. Talagrand, et al. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environmental Modelling & Software, 2000 (15): 539～548
[81] Fisher, M., Lary, D.J.. Lagrangian four-dimensional variational data assimilation of chemical species. Quart. J. R. Meteorol. Soc , 1995, 121: 1681～1704
[82] Elbern, H., Schmidt, H., Ebel, A.. Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res. , 1997, 102 (D13): 15967～15985
[83] Elbern, H., Schmidt, H. A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling. J. Geophys. Res , 1999, 104: 18583～18598
[84] Yienger,J, Levy, H. Empirical model of global soil-biogenic NOx emissions. J. Geophys. Res. , 1995, 100: 11447～11464
[85] Reich, S, Gomez, D, et al. Artificial neural network for the identification of unknown air Pollution sources. Atmospheric Environment, 1999, 33(15): 3045～3052
[86] Michael, M., John, H. Inverse air pollution modeling of urban-scale carbon monoxide emission,Atmospheric Environment, 1995, 29(4): 497～516
[87] Belgamaschi, P, R Hein,et al. Inverse modeling of CO mixing ratios. J. Geophys. Res. ,1999,105: 1909～1928
[88] Bousquet,P, Ciais,P, et al.. Inverse modeling of annual atmospheric CO_2 sources and sinks.Ⅰ. Method and contro1 inversion,J. Geophys. Res. , 1999, 104: 26161～2 6178
[89] Hein, R, CrutZen,P, et al. An inverse modeling approach to investigate the global atmospheric methane cycle. Global Biogeochem Cycles, 1997, 11:43～76
[90] Houweling, S, Kaminski, T, et al. Inverse modeling of methane sources and sinks using the adjoint of a global transport model. J. Geophys. Res. , 1999, 104: 26137～2 6160
[91] Guenther, A, Newitt, C, et al. A global model of natural volatile organic compound emissions,J. Geophys. Res. , 1995, 100: 8873～8874
[92] Hartley D, Prinn, R. On the feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical transport model. J. Geophys. Res. , 1993, 98: 5183～5198
[93] Randall,V M, Kelly, C, et al. An improved regrieval of tropospheric nitrogen dioxide from GOME. J. Geophys. Res. , 2002, 107(D20): 4437～4 466
[94] 徐大海,朱蓉. 城市大气污染物源强反演. 大气环境和环境影响评价, 1998, 气象出版社
[95] 陈军明,徐大海,等. 遗传算法在点源扩散浓度反演排放源强中的应用. 气象, 2002,28(9):12～16
[96] 蔡旭辉,韵敏,等.甲烷排放源逆向轨迹反演模式研究. 环境科学,2002,23(5): 25～30
[97] 苏芳,韵敏,等. 利用逆向轨迹反应模式估算北京地区甲烷源强. 环境科学学报, 2002,22(5): 586～591
[98] 李灿,许黎,等. 一种大气 CO_2 源汇反演模式方法的建立及应用. 中国环境科学, 2003, 23(6): 610～613
[99] 刘峰,胡非,等. 用伴随方法求解多个工业污染源优化布局问题. 中国科学, D 辑, 2005, 35(l): 64～71
[100] 刘峰,胡非,等. 用伴随方程研究空气污染的优化控制. 环境科学学报, 2003, 23(4): 472～475
[101] Nelson L. Seaman. Meteorological modeling for air-quality assessments. Atmospheric Environment, 2000 (34): 2231～2259
[102] 王喜全, 虞统, 孙峰等. 北京 PM10 重污染预警预报关键因子研究. 气候与环境研究, 2006, 11(4): 470～476
[103] Martin, R.V., Jacob, D.J., Chance, K.V., et al. Global inventory of nitrogen dioxide emissions constrained by space-based observations of NO_2 columns. Journal of Geophysical Research, 2003,108: 4537.
[104] 程立刚,王艳姣,王耀庭. 遥感技术在大气环境监测中的应用综述. 中国环境监测, 2005, 21(5):17～23
[105] Richter, A., Burrows, J.P. Retrieval of tropospheric NO_2 from GOME measurements. Advances in Space Research, 2002, 29: 1673～1683.
[106] Martin, R.V., Chance, K., Jacob, D.J. An improved retrieval of tropospheric nitrogen dioxide from GOME. Journal of Geophysical Research, 2002, 107 (D20): 4437
[107] Boersma, K.F., Eskes, H.J., Brinksma, E.J. Error analysis for tropospheric NO_2 retrieval from space. Journal of Geophysical Research , 2004,109: D04311
[108] Boersma, K.F., Eskes, H.J., Veefkind, J.P., et al. Near-real time retrieval of tropospheric NO_2 from OMI. Atmospheric Chemistry and Physics , 2007, 7: 2103～2118
[109] Schaub, D., Brunner, D., Boersma, K.F., et al. SCIAMACHY tropospheric NO_2 over Switzerland: estimates of NOx lifetimes and impact of the complex Alpine topography on the retrieval. Atmospheric Chemistry and Physics, 2007, 7, 5971～5987
[110] Schaub, D., Boersma, K.F., Kaiser, J.W., et al. Comparison of GOME tropospheric NO_2 columns with NO_2 profiles deduced from ground-based in situ measurements. Atmospheric Chemistry and Physics, 2006, 6: 3211～3229
[111] Martin, R.V., Parrish, D.D., Ryerson, T.B., et al. Evaluation of GOME satellite measurements of tropospheric NO_2 and HCHO using regional data from aircraft campaigns in the southeastern United States. Journal of Geophysical Research, 2004, 109: D24307.
[112] Khokhar, M.F., Frankenberg, C., Hollwedel, J.,et al. Satellite remote sensing of atmospheric SO_2: volcanic eruptions and anthropogenic emissions, Proceedings of the ENVISAT & ERS Symposium, 6-10 September 2004, Salzburg, Austria, ESA publication SP-572 (CD-ROM).
[113] 江文华,马建中,颜鹏,等. 利用 GOME 卫星资料分析北京大气 NO_2 污染变化. 应用气象学报,2006,17(1): 67～72
[114] Boersma, K.F., Jacob, D.J., Bucsela, E.J. Validation of OMI tropospheric NO_2 observations during INTEX-B and application to constrain NOx emissions over the eastern United States and Mexico. Atmospheric Environment, 2008, doi:10.1016/j.atmosenv , in press.
[115] Boersma, K.F., Jacob, D.J., Eskes, H.J., et al. Intercomparison of SCIAMACHY and OMI tropospheric NO_2 columns: observing the diurnal evolution of chemistry and emissions from space. Journal of Geophysical Research, 2008, doi:10.1029/2007JD008816, in press.
[116] Wang, Y.X., McElroy, M.B., Boersma, K.F., et al. Traffic restrictions associated with the Sino-African summit: reductions of NOx detected from space.Geophysical Research Letters, 2007, 34: L08814
[117] Jeffries, H E, S. Tonnesen. A comparison of two photochemical reaction mechanisms using mass balance and process analysis. Atmospheric Environment, 1994, 28(18): 2991～3003
[118] Jang, J C, H E, Jeffries, S. Tonnesen, et al. Sensitivity of ozone to model grid resolution-Ⅱ. Detailed process analysis for ozone chemistry. Atmospheric Environment, 1995b, 29(21): 3101～3114
[119] 徐峻,张远航. 北京市区夏季 O3 生成过程分析. 环境科学学报, 2006, 26(6): 973～980
[120] 陈义珍,柴发合,段宁,等. “西电东送”火电规划对北京空气质量的影响. 环境科学研究, 2003,16(6): 46～52
[121] 徐祥德,周丽,周秀骥,等. 城市大气环境重污染过程周边源影响域. 中国科学, D 辑, 2004, 34(10): 958～966
[122] 徐祥德,周秀骥,施晓辉,等. 城市群落大气污染影响的空间结构及尺度特征. 中国科学D辑,2005,35(增Ⅰ):53～65
[123] 苏福庆, 高庆先, 张志刚等. 北京边界层外来污染物输送通道. 环境科学研究, 2004,17(1): 26～29
[124] 清华大学. 北京市大气污染综合防治对策研究. 北京:清华大学,2001,12
[125] 张志刚, 高庆先, 韩雪琴等. 中国华北区域城市间污染物输送研究. 环境科学研究, 2004,17(1): 14～20
[126] 孟伟,高庆先,张志刚等. 北京及周边地区大气污染数值模拟研究. 环境科学研究, 2006,19(5): 11～18
[127] 郝吉明,王丽涛,李 林,等. 北京市能源相关大气污染源的贡献率和调控对策分析. 中国科学D辑,2005,35(增Ⅰ):115～122
[128] 颜鹏,黄健,等. 周边地区对北京地面 SO_2 影响的初步研究. 应用气象学报,2002,13(特刊):144～152
[129] 颜鹏,黄健,Roland Draxler,等. 北京地区 SO_2 污染的长期模拟及不同类型排放源的计算与评估. 中国科学D辑,2005,35(增Ⅰ):167～176
[130] 张金善,钟中,黄瑾. 中尺度大气模式 MM5 简介. 海洋预报,2005,22(1): 31～40
[131] 丑纪范. 四维同化的理论和新方法. 数值天气预报中的若干新技术[M]. 气象出版社,1995.
[132] 国家环境保护局. 2004 中国环境统计年报. 北京:中国环境科学出版社,2005
[133] 范元中. 空气污染指数计算程序. 监测与评价,1998, 8: 19～20
[134] Welsh P , Wildman A , Shaw B ,et al. Implementing the Weather Research and Forecast (WRF) model with local data Assimilation in a NWS WFO [C] ∥84 th AMS Annual Meeting , Seattle , U.S. A. 2004.
[135] Hiroyuki Kusaka, Andrew Crook, Jimy Dudhia, et al. Comparison of the WRF and MM5 Models for Simulation of Heavy Rainfall along the Baiu Front[J]. Scientific Online Letters on the Atmosphere(SOLA), 2005, 1, 197～200
[136] 孙健, 赵平. 用 WRF 与 MM5 模拟 1998 年三次暴雨过程的对比分析 . 气象学报, 2003 ,61 (6) : 692～701.
[137] 张芳华,马旭林,杨克明. 2003 年 6 月 24～25 日江南特大暴数值模拟和诊断分析. 气象, 2004 ,30 (1) : 28～32.
[138] 刘宁微, 王奉安. WRF 和 MM5 模式对辽宁暴雨模拟的对比分析. 气象 科技, 2006, 34(4): 364～369
[139] 赵洪, 杨学联, 邢建勇等. WRF 与 MM5 对 2007 年 3 月初强冷空气数值预报结果的对比分析[J]. 海洋预报, 2007, 24(2): 1～8
[140] 章毅之, 辜晓青. 气象要素的等值线绘制技术[J]. 江西气象科技,2002,25(1): 30～33.
[141] http://aura.gsfc.nasa.gov/instruments/omi/index.html
[142] Krotkov, N.A., S.A. Carn, A.J. Krueger, et al. Band residual difference algorithm for retrieval of SO_2 from the Aura Ozone Monitoring Instrument(OMI). IEEE Trans.Geosci. Remote Sensing, AURA special issue, 2006, 44(5): 1259～1266
[143] Taubman, B. F., et al. Aircraft vertical profiles of trace gas and aerosol pollution over the mid-Atlantic United States: Statistics and meteorological cluster analysis. Journal of Geophysical Research-Atmospheres, 2006,111.
[144] Boersma, K. F., E. J. Bucsela, E. J. Brinksma, et al. NO_2, OMI-EOS Algorithm Theoretical Basis Document: Trace Gas Algorithms: NO_2, 2001, 4:12～35
[145] Bucsela, E., E. Celarier, M. Wenig, et al. Algorithm for NO_2 vertical column retrieval from the Ozone Monitoring Instrument. IEEE Transactions on Geoscience and Remote Sensing, 2006,44: 1245～1258
[146] Edward A. Celarier, James F. Gleason,Bojan Bojkov, et al. Evaluation of OMI NO_2 Data Quality. 2006
[147] 周秀骥,徐祥德. 2000 年春季沙尘暴动力学特征. 中国科学,D 辑,2002,32(4): 327～334.
[148] 徐祥德,周秀骥,翁永辉,等. 星载 MODIS 资料与地面光度计探测气溶胶变分场. 科学通报,2003,15:1680～1685.
[149] 徐祥德,许建民,王继志,等. 大气遥感再分析场构造技术与原理. 北京: 气象出版社,2003:58～91.
[150] http://www.bjee.org.cn/api/index.php?stat=month