| 基于在线监测的江苏省大型固定燃煤源排放清单及其时空分布特征 |
| 摘要点击 2901 全文点击 1527 投稿时间：2014-07-01 修订日期：2015-03-18 |
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| 中文关键词 大型固定燃煤源 大气污染物 排放清单 月变化 空间分布 |
| 英文关键词 coal-fired stationary sources atmospheric pollutant emission inventory monthly variation spatial distribution |
| 作者 | 单位 | E-mail | | 张英杰 | 南京信息工程大学气象灾害预报预警与评估协同创新中心,中国气象局气溶胶与云降水重点开放实验室,南京 210044 | zyjnuist@gmail.com | | 孔少飞 | 南京信息工程大学气象灾害预报预警与评估协同创新中心,中国气象局气溶胶与云降水重点开放实验室,南京 210044 | kongshaofei@126.com | | 汤莉莉 | 江苏省环境监测中心,南京 210029 | | | 赵天良 | 南京信息工程大学气象灾害预报预警与评估协同创新中心,中国气象局气溶胶与云降水重点开放实验室,南京 210044 | | | 韩永翔 | 南京信息工程大学气象灾害预报预警与评估协同创新中心,中国气象局气溶胶与云降水重点开放实验室,南京 210044 | | | 于红霞 | 南京大学环境学院,南京 210093 | |
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| 中文摘要 |
| 大气污染物排放清单是了解各地区大气污染物排放及其时空分布,精确模拟该地区环境空气质量的最基础资料.现有大气污染物排放清单的粗时空分辨率,极大地限制了空气质量数值预报的准确性.本研究以江苏省大型固定燃煤源为例,以2012年为基准年,收集江苏省电力企业在线监控系统数据及江苏省大气核查核算表数据,结合相关文献的排放因子,分析了江苏省大型固定燃煤源主要污染物的总排放量和月变化特征.分析结果表明: 1 SO2、NOx、TSP、PM10、PM2.5、CO、EC、OC、NMVOC、NH3等大气污染物的排放总量分别达到 106.0、278.3、40.9、32.7、21.7、582.0、3.6、2.5、17.3、2.2 kt.2 呈现2~3、7~8、12月排放量高,9~10月排放量低的月变化特征,可能原因是2~3月处于春节阶段,为保证节日供应,在此期间居民取暖、用电等都有可能增加; 7~8月高温天气用电量增加,12月北方城市冬季燃煤取暖导致的煤炭消耗量增加.另外,由于部分污染物排放因子取自国内外相关文献,是本研究清单不确定性的主要因素.今后的工作可以在排放因子实测更新以及将排放清单纳入空气质量预报模式等方面进行更为深入的研究. |
| 英文摘要 |
| Emission inventory of air pollutants is the key to understand the spatial and temporal distribution of atmospheric pollutants and to accurately simulate the ambient air quality. The currently established emission inventories are still limited on spatial and temporal resolution which greatly influences the numerical prediction accuracy of air quality. With coal-fired stationary sources considered, this study analyzed the total emissions and monthly variation of main pollutants from them in 2012 as the basic year, by collecting the on-line monitoring data for power plants and atmospheric verifiable accounting tables of Jiangsu Province. Emission factors in documents are summarized and adopted. Results indicated that the emission amounts of SO2, NOx, TSP, PM10, PM2.5, CO, EC, OC, NMVOC and NH3 were 106.0, 278.3, 40.9, 32.7, 21.7, 582.0, 3.6, 2.5, 17.3 and 2.2 kt, respectively. They presented monthly variation with high emission amounts in February, March, July, August and December and low emissions in September and October. The reason may be that more coal are consumed which leads to the increase of pollutants emitted, to satisfy the needs of heat and electricity power supply in cold and hot periods. Local emission factors are needed for emission inventory studies and the monthly variation should be considered when emission inventories are used in air quality simulation. |
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