摘要工业活动密集和化石燃料的大量使用造成了南京市较高的大气污染物排放和大气细颗粒物（PM2.5）环境浓度，导致灰霾污染的频发和能见度的下降。含碳气溶胶，主要包括黑碳（Black Carbon，BC）和有机碳（Organic Carbon, OC），是PM2.5中的重要化学组分，对人体健康、能见度和气候均产生显著的影响。论文基于现有的环境统计和污染源普查资料，对南京市主要排放源的工艺特征和排放水平开展详细调研，采用“自下而上”的排放因子法，建立了 2014 年南京市含碳气溶胶排放清单。计算结果表明：BC 和 OC 的总排放量为3682吨和4271 吨。工业部门对BC 的贡献超过1/3，其中89 %来自于钢铁行业；砖瓦生产以小型企业为主，排放估计的不确定较大，也是BC 不可忽略的重要来源之一。民用部门排放占 OC 总排放量的 37 %，其次是工业部门和生物质开放燃烧。利用ArcGIS 对排放清单进行地理空间分配发现，江宁区和六合区工业发达、能源消耗量大，排放强度较高。基于所建立的排放清单，采用WRF-CMAQ模式，对2014 年1月1日—1 月 31 日南京市含碳气溶胶大气环境浓度进行数值模拟，并将模拟结果与南京大学仙林校区在线观测数据进行对比。结果表明，模拟值与观测值的变化趋势较为一致，但仍存在一定的偏差。 模式低估的原因主要包括：城市排放清单中扬尘源的缺失和气象模式（WRF）对模拟风速的高估。模式对个别时间段也存在明显的高估，可能的原因是边界层模拟偏低。42432
毕业论文关键词  含碳气溶胶  排放清单   WRF-CMAQ     
Title   The  Emission  Inventory  Development and Pollution Characteristics Simulation of Urban Carbonaceous Aerosols             
Abstract Intensive industrial activity and the use of fossil fuels have caused high  atmospheric pollution emissions and concentration of fine particulate matter (PM2.5) in Nanjing, leading to frequent ash haze weather and downgrading visibility. Carbonaceous aerosols which include black carbon (BC) and organic carbon (OC) are chemical compounds of major importance  in PM2.5. They have a significant impact to health, visibility and climate. In this study, we carry out detailed research on the characteristic and the emission level of main emission sources in Nanjing based on the existing environment and pollution census statistics. We estimate the carbonaceous aerosol emissions in Nanjing in 2014 using  emission factors  and bottom-up methodology.  Carbonaceous aerosol emissions are determined to be as follows: 3682 t BC and 4271 t OC. Among them, the industrial sector accounts for more than 1/3. 89 % of total industrial emissions are from the iron and steel industry.  Brick production in  small  plants  is one of the most important  contributors of carbonaceous aerosol emissions, which are thought to be highly polluting, because the low level of technology inhibits the use of efficient control devices. As for OC, residential sector represents the largest share of the total (37 %), other large sources are industrial sector and biomass burning. Emissions are distributed using various spatial proxies to relatively small and uniform gridded forms using ArcGIS technology. A similar feature stands out: the relatively strong signature of BC and OC from Jiangning and Liuhe districts, where are industrialized and consume huge energy. Based on this high-resolution emission inventory, combined with WRF and three layers of nested grid method, CMAQ is applied to simulate the pollutant concentration in Nanjing during January in 2014. Monitoring data in the Xianlin Campus of Nanjing University is selected to compare with the simulated data to assess the emission inventory. The model generally reproduces  the daily variations of MBC  and MOC, although  there  is  still  a deviation  sometimes. The model underestimates MBC  and MOC mainly on account of the lack of dust source in the emission inventory and the overestimation of wind speed in WRF. The model also overestimates them for a specific time due to the underestimation of the boundary layer.    城市含碳气溶胶排放清单建立与污染特征模拟:http://www.youerw.com/huaxue/lunwen_42863.html