本文關(guān)鍵詞：區(qū)域大氣汞排放清單建立與污染特征模擬　出處：《南京大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 大氣汞 高精度 排放清單 在線觀測(cè) CMAQ-Hg

【摘要】：為了更好的理解不同尺度大氣汞人為源排放清單間的差異,深入了解排放估算方法以及方法的局限性,從而提高汞排放估算的精確度,本研究采用"自下而上"的方法建立了江蘇省2010年0.05°×0.05°的高精度區(qū)域排放清單,并與全國(guó)和全球尺度清單進(jìn)行詳盡對(duì)比。同時(shí)為了加深對(duì)大氣汞污染水平、特征和影響因素的認(rèn)識(shí),我們?cè)谀洗笙闪中^(qū)開(kāi)展了為期一年的大氣汞分形態(tài)在線觀測(cè),并利用CMAQ-Hg對(duì)蘇南地區(qū)進(jìn)行區(qū)域尺度污染特征模擬。本研究根據(jù)詳盡的點(diǎn)源信息和實(shí)地測(cè)量數(shù)據(jù)估算出江蘇省2010年大氣汞排放量為39105Kg,其中Hg0,Hg2+,HgP比例為51%、47%以及2%;電廠、水泥、鋼鐵和其他工業(yè)燃煤是主要的排放源,占總排放量的90%。區(qū)域清單總汞排放估算結(jié)果比各類(lèi)國(guó)家和全球排放清單 NJU、THU、BNU、AMAP/UNEP 以及 EDGARv4.tox2 分別高出 28%、7%、19%、22%以及70%。對(duì)于主要的排放源電廠、水泥、鋼鐵冶煉以及其他工業(yè)燃煤,不同尺度清單間的差異主要來(lái)源于煤炭含汞量、污染控制設(shè)備除汞效率以及活動(dòng)水平等參數(shù)的不同。隨著清單尺度的降低,Hg2+的比例逐漸升高,這主要是由于區(qū)域清單的水泥生產(chǎn)和鋼鐵冶煉以及垃圾焚燒三個(gè)排放部門(mén)采用了國(guó)內(nèi)實(shí)測(cè)數(shù)據(jù)。大型點(diǎn)源信息的不同會(huì)導(dǎo)致不同清單的網(wǎng)格排放量差別較大,這種情況主要集中在點(diǎn)源密集的江蘇南部和西北部。根據(jù)蒙特卡洛模型的模擬,區(qū)域清單的不確定性相比清單NJU有所減小,這主要是由于區(qū)域清單采用了詳細(xì)的點(diǎn)源活動(dòng)水平以及煤炭和原料含汞量等數(shù)據(jù)。2014年8月至2015年7月南京大學(xué)仙林校區(qū)元素態(tài)汞(GEM)、氧化態(tài)汞(RGM)以及顆粒態(tài)汞(PBM)的平均污染濃度(相對(duì)標(biāo)準(zhǔn)偏差)分別為3.85(45%)ng/m3、127.4(188%)pg/m3 和 43.0(274%)pg/m3。GEM、RGM 以及 PBM 春夏秋冬四個(gè)季節(jié)的污染濃度分別為: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 最主要的濃度區(qū)間分別為 3-4 ng/m3,10-20 pg/m3以及10-50 pg/m3,出現(xiàn)頻率分別為31%、27%和36%。春秋冬三個(gè)季節(jié)GEM濃度在凌晨逐漸上升,這主要是由于高層大氣中殘留的高濃度汞通過(guò)氣流垂直運(yùn)動(dòng)遷移到近地面。由于逆溫現(xiàn)象,PBM濃度在凌晨4:00之后逐漸升高,白天降低。人為源排放和GEM的氧化導(dǎo)致RGM夏秋冬三個(gè)季節(jié)均呈現(xiàn)白天高夜晚低的晝夜變化模式。GEM濃度與CO、PM2.5以及S02具有同源性,濃度呈正相關(guān)。RGM夏季與03以及溫度呈正相關(guān),冬季與S02正相關(guān)性高于其他季節(jié),預(yù)示著夏季RGM較多地來(lái)源于GEM的氧化而冬季受人為源排放影響更大。觀測(cè)點(diǎn)受到區(qū)域傳輸以及局地排放的共同影響。江蘇省南部以及上海地區(qū)是最重要的區(qū)域傳輸貢獻(xiàn)地區(qū)。觀測(cè)點(diǎn)周邊的水泥廠對(duì)RGM高污染貢獻(xiàn)較為明顯。CMAQ-Hg小尺度模擬結(jié)果能較好的反應(yīng)觀測(cè)點(diǎn)GEM和PBM整體污染水平,但小時(shí)模擬濃度與觀測(cè)值相關(guān)性較弱,模擬結(jié)果有待進(jìn)一步改善。由于不同尺度清單的排放估算和空間分布不同,模擬得到的區(qū)域大氣汞污染濃度水平和空間分布模式也存在相應(yīng)的差異。
[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.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：X51

【參考文獻(xiàn)】

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本文編號(hào)：1341464