区域大气汞排放清单建立与污染特征模拟

发布时间：2017-12-27 11:12

本文关键词：区域大气汞排放清单建立与污染特征模拟　出处：《南京大学》2017年硕士论文　论文类型：学位论文

【摘要】：为了更好的理解不同尺度大气汞人为源排放清单间的差异,深入了解排放估算方法以及方法的局限性,从而提高汞排放估算的精确度,本研究采用"自下而上"的方法建立了江苏省2010年0.05°×0.05°的高精度区域排放清单,并与全国和全球尺度清单进行详尽对比。同时为了加深对大气汞污染水平、特征和影响因素的认识,我们在南大仙林校区开展了为期一年的大气汞分形态在线观测,并利用CMAQ-Hg对苏南地区进行区域尺度污染特征模拟。本研究根据详尽的点源信息和实地测量数据估算出江苏省2010年大气汞排放量为39105Kg,其中Hg0,Hg2+,HgP比例为51%、47%以及2%;电厂、水泥、钢铁和其他工业燃煤是主要的排放源,占总排放量的90%。区域清单总汞排放估算结果比各类国家和全球排放清单 NJU、THU、BNU、AMAP/UNEP 以及 EDGARv4.tox2 分别高出 28%、7%、19%、22%以及70%。对于主要的排放源电厂、水泥、钢铁冶炼以及其他工业燃煤,不同尺度清单间的差异主要来源于煤炭含汞量、污染控制设备除汞效率以及活动水平等参数的不同。随着清单尺度的降低,Hg2+的比例逐渐升高,这主要是由于区域清单的水泥生产和钢铁冶炼以及垃圾焚烧三个排放部门采用了国内实测数据。大型点源信息的不同会导致不同清单的网格排放量差别较大,这种情况主要集中在点源密集的江苏南部和西北部。根据蒙特卡洛模型的模拟,区域清单的不确定性相比清单NJU有所减小,这主要是由于区域清单采用了详细的点源活动水平以及煤炭和原料含汞量等数据。2014年8月至2015年7月南京大学仙林校区元素态汞(GEM)、氧化态汞(RGM)以及颗粒态汞(PBM)的平均污染浓度(相对标准偏差)分别为3.85(45%)ng/m3、127.4(188%)pg/m3 和 43.0(274%)pg/m3。GEM、RGM 以及 PBM 春夏秋冬四个季节的污染浓度分别为:3.84、3.92、4.0、3.63 ng/m3,52.9、47.9、45.55、26.4 pg/m3 以及129.1、91.3、138.5、148.3 pg/m3。GEM、RGM 和 PBM 最主要的浓度区间分别为 3-4 ng/m3,10-20 pg/m3以及10-50 pg/m3,出现频率分别为31%、27%和36%。春秋冬三个季节GEM浓度在凌晨逐渐上升,这主要是由于高层大气中残留的高浓度汞通过气流垂直运动迁移到近地面。由于逆温现象,PBM浓度在凌晨4:00之后逐渐升高,白天降低。人为源排放和GEM的氧化导致RGM夏秋冬三个季节均呈现白天高夜晚低的昼夜变化模式。GEM浓度与CO、PM2.5以及S02具有同源性,浓度呈正相关。RGM夏季与03以及温度呈正相关,冬季与S02正相关性高于其他季节,预示着夏季RGM较多地来源于GEM的氧化而冬季受人为源排放影响更大。观测点受到区域传输以及局地排放的共同影响。江苏省南部以及上海地区是最重要的区域传输贡献地区。观测点周边的水泥厂对RGM高污染贡献较为明显。CMAQ-Hg小尺度模拟结果能较好的反应观测点GEM和PBM整体污染水平,但小时模拟浓度与观测值相关性较弱,模拟结果有待进一步改善。由于不同尺度清单的排放估算和空间分布不同,模拟得到的区域大气汞污染浓度水平和空间分布模式也存在相应的差异。
[Abstract]:In order to better understand the differences between different scales of atmospheric mercury emissions inventory of the in-depth understanding of limitations of the emission estimation method and method, so as to improve the accuracy of estimation of mercury emission, this study adopts the "bottom-up" method to establish the high precision of regional emission inventory in Jiangsu Province in 2010 0.05 * 0.05 DEG, and a detailed comparison with the national and global scale list. At the same time in order to deepen our understanding of the atmospheric mercury pollution levels, characteristics and influencing factors of knowledge, we carried out a one-year atmospheric mercury in the South Xianlin Campus morphology observed online and in South of Jiangsu area were simulated by using CMAQ-Hg scale characteristics of regional pollution. Based on detailed point source information and field measurement data, we estimated that the atmospheric mercury emissions in Jiangsu province in 2010 were 39105Kg, of which Hg0, Hg2+ and HgP ratios were 51%, 47% and 2%. Power plants, cement, steel and other industrial coal were the main sources of emissions, accounting for 90% of the total emissions. The estimated total mercury emissions from the regional inventory are 28%, 7%, 19%, 22% and 70% higher than the national and global emission inventories NJU, THU, BNU, AMAP/UNEP and EDGARv4.tox2, respectively. For the main emission sources, such as power plants, cement, steel smelting and other industrial coal combustion, the differences between the different scale lists mainly come from the mercury content of coal, the pollution control equipment, mercury removal efficiency and activity level. With the decrease of inventory scale, the proportion of Hg2+ gradually increased. This is mainly due to the three measured data of cement production, steel smelting and refuse incineration in the regional inventory. The difference of large point source information will lead to large difference in grid emissions from different list, which is mainly concentrated in the South and northwest of Jiangsu. According to Monte Carlo simulation, the uncertainty of the regional inventory is reduced compared with the list NJU, mainly due to the detailed point source activity level and the mercury content of coal and raw materials. From August 2014 to July 2015 Xianlin Campus of Nanjing University elemental mercury (GEM), oxidized mercury (RGM) and particulate mercury (PBM) the average pollutant concentration (relative standard deviation) were 3.85 (45%) ng/m3, 127.4 (188%) pg/m3 and 43 (274%) pg/m3. The concentrations of GEM, RGM and PBM in four seasons were 3.84, 3.92, 4, 3.63 ng/m3,52.9, 47.9, 45.55, 26.4 pg/m3, 129.1, 91.3, 138.5, and 138.5 pg/m3 respectively. The main concentration ranges of GEM, RGM and PBM were 3-4 ng/m3,10-20 pg/m3 and 10-50 pg/m3 respectively, and the frequencies were 31%, 27% and 36%, respectively. In the three seasons of spring and autumn and winter, the concentration of GEM gradually increased in the morning. This is mainly due to the high concentration of mercury in the upper atmosphere moving to the near surface through the vertical movement of air. As a result of the temperature inversion, the concentration of PBM increased gradually after 4:00 in the morning and decreased in the daytime. The three seasons of RGM summer, autumn and winter were caused by human emission and the oxidation of GEM, which showed high night and low day night change mode. The concentration of GEM is homologous with CO, PM2.5 and S02, and the concentration is positively correlated. RGM in summer is positively correlated with 03 and temperature. The positive correlation between winter and S02 is higher than that of other seasons. It indicates that RGM in summer is mostly derived from GEM oxidation, while winter is more influenced by anthropogenic emissions. The observation points are affected by the local transmission and the local emission. The southern Jiangsu province and the Shanghai region are the most important regional transmission areas. The cement plant around the observation point has a more obvious contribution to the high pollution of RGM. The results of CMAQ-Hg small scale simulation can better reflect the overall pollution level of GEM and PBM. However, the correlation between hourly simulated concentration and observed values is relatively weak, and the simulation results need further improvement. Due to the difference of emission estimation and spatial distribution of different scale list, there are also differences in the level and spatial distribution pattern of mercury concentration in simulated area.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X51

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