Coal-fired power plant is the major emission source of air pollutants in China, and has been indentified as the primary target for air pollution control. The management of air quality is largely depending on how exactly we can understand and quantify the emission budgets from coal-fired power plants and their environmental effects. However, a complete database for the coal-fired power plants has not been built up to support scientific study and policy analysis in China. Due to the lack of accurate activity data and emission factors and the inconsistency between different datasets, the emission estimates based on the traditional "bottom-up" approach could be with large uncertainty, which is also difficult to evaluate. Meanwhile, there is no effective in-situ measurements to support an accurate evaluation of the efficiency and operation of desulfurization and denitrification devices equipped on the coal-fired power plants in recent years in China. Satellite remote sensors developed during the past two decades provided us a new view to better indentify and quantify surface air pollutant emissions from large point sources. In this study, we will first build up a complete database for Chinese coal-fired power plants, which covers a long time period (since 1990) with detailed information such as geographical locations, boilor size, coal consumption, and the emission control technology for each generator unit. Based on this database, we will develop a unit-based coal-fired power plant emission inventory for China for the recent two decades. This will be largely benefit to the modelling simulation analysis. With the high-resolution a priori NO2 profiles generated by GEOS-Chem and WRF-Chem models and other parameters from OMI products, we will optimize the current OMI satellite NO2 and SO2 retrievals to a high spatial resolution. The bias from unperfect spectrum treatment in the current NASA OMI SO2 product will be corrected with latitudinal background SO2 columns over clean Pacific areas. Combining the satellite measurements and simulation results, we will evaluate the unit-based power plant NOx and SO2 emission estimates and their historical trends. The efficiency of the desulfurization and denitrification devices will be examined by comparing the variations of OMI NO2 and SO2 retrievals over individual power plants. The high-resolution satellite retrievals will be used to explore the new approach to inverse emissions from single large point source with numerical fitting analysis, and finally improve the "bottom-up" emission estimates. We will quantitatively evaluate the contributions of the coal-fired power plants to the regional NO2 and SO2 burdens. The regional concentrations of O3 and particulate matters could also be susceptible to the changes of power plant NOx and SO2 emissions, which will also be completely examined in this study.
燃煤电厂是中国大气污染物的重要贡献来源,也是大气污染物排放总量控制的主要对象。准确掌握燃煤电厂的大气污染物排放及其环境影响是解决中国大气污染问题的重要基础。目前中国尚未建立一个时空序列完整的燃煤电厂基础信息数据库供科学研究和政策分析使用;受观测数据所限,对于"自下而上"排放清单中单个机组的排放量估算缺少有效的评估和改进手段;对于燃煤电厂脱硫、脱硝装置的控制效果和运行状况也缺少有效的评估方法。近年来卫星遥感观测数据资料为定量分析地面点源的大气污染物排放提供了新的手段。本研究将利用优化的高分辨率二氧化氮和二氧化硫OMI卫星遥感数据,结合中国燃煤电厂排放清单的开发编制和区域尺度空气质量模拟等分析手段,全面评估中国燃煤电厂大气污染物排放清单的精度、排放变化趋势、烟气脱硫、脱硝等排放控制措施的效果等,开发卫星遥感反演地面点源排放的新方法,并系统性分析燃煤电厂排放对中国区域性复合污染的贡献。
燃煤电厂是中国人为源二氧化硫(SO2)和氮氧化物(NOx)的主要排放源之一,对其排放的控制是中国完成SO2和NOx总量控制目标的关键。随着“十二五”期间中国SO2和NOx减排工作进入全面攻坚阶段,对污染源的排放监管和减排评估工作成为环境管理层面的迫切需求。本研究通过编制“自下而上”的燃煤电厂排放清单和卫星遥感技术定量分析了中国燃煤电厂的SO2和NOx排放量及变化趋势,为燃煤电厂的排放监管和减排评估提供了新的技术手段。.首先,在收集整理约8000个机组信息的基础上,通过多源数据融合方法,建立了中国燃煤电厂高时空分辨率排放数据库,计算了1990–2012年逐个机组的大气污染物排放。与之前研究相比,本研究建立的电厂排放清单在排放量和时空分布方面均具有更高精度。其次,通过背景残差校正、云压校正和AMF计算等手段改进了卫星遥感SO2和NO2柱浓度反演算法中的关键参数,在此基础上建立了改进后的反演算法。与现有卫星业务算法产品相比,改进算法获得的SO2和NO2柱浓度数据质量有明显改善,可用于电厂排放变化的定量分析。继而,利用背景浓度拟合和非对称性拟合域选取等手段,建立了适用于中国复杂背景浓度条件下卫星遥感反演燃煤电厂排放的新方法。对中国大陆地区的燃煤电厂进行了SO2和NOx排放量及排放变化的拟合反演。通过分析卫星观测厂附近SO2和NO2浓度的变化趋势以及脱硫设备实际运行前后卫星反演排放的变化,实现了利用卫星遥感技术对燃煤电厂脱硫设备运行效果的独立评估,为目前我国重点排放源的排放监管和评估工作提供了新的技术支撑手段。综合卫星观测和模型模拟结果分析了中国地区2005-2012年期间SO2和NO2浓度的变化趋势,发现中国东部地区“十一五”前期SO2和NO2浓度的迅速增长与新建电厂排放关系密切;“十一五”后期SO2浓度下降明显,证实了燃煤电厂脱硫对SO2总量减排的贡献。本研究利用卫星遥感定量分析中国燃煤电厂硫、氮排放的工作可以为中国的空气质量管理和减排决策等提供长期的科学支持。
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数据更新时间:2023-05-31
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