| 基于不同排放清单的长三角人为CO2排放模拟 |
| 摘要点击 1441 全文点击 1885 投稿时间：2022-05-25 修订日期：2022-07-19 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 人为CO2排放 排放清单 WRF-STILT模型 大气CO2摩尔分数 长三角地区 |
| 英文关键词 anthropogenic CO2 emissions emission inventory WRF-STILT model atmospheric CO2 concentration Yangtze River Delta |
| 作者 | 单位 | E-mail | | 马心怡 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044 | maxinyi27@163.com | | 黄文晶 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044 | | | 胡凝 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044
南京信息工程大学江苏省农业气象重点实验室, 南京 210044 | huning@nuist.edu.cn | | 肖薇 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044
南京信息工程大学江苏省农业气象重点实验室, 南京 210044 | | | 胡诚 | 南京林业大学生物与环境学院, 南京 210018 | | | 张弥 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044 | | | 曹畅 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044 | | | 赵佳玉 | 南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心, 南京 210044 | |
|
| 中文摘要 |
| 目前基于排放清单估算的区域和城市尺度上的人为CO2排放不确定性较大.为了我国实现碳达峰和碳中和的目标，亟需对我国的区域尺度，特别是大城市群的人为CO2排放进行准确估算.分别利用两种先验人为CO2排放数据（EDGAR v6.0清单和EDGAR v6.0联合GCG v1.0的改进清单）作为输入数据，采用WRF-STILT大气传输模型模拟长三角地区2017年12月至2018年2月大气CO2摩尔分数，再以安徽全椒高塔观测的大气CO2摩尔分数作为参考值，通过贝叶斯反演方法得到的比例因子改进了模拟结果，并实现了长三角人为CO2排放通量的估算.结果表明：①在冬季，相对于基于EDGAR v6.0模拟的大气CO2摩尔分数值而言，基于改进清单模拟的大气CO2摩尔分数与观测值更为一致；②模拟的大气CO2摩尔分数在夜间高于观测值，白天则相反，主要因为排放清单的CO2排放数据不能表征人为排放的日变化特征，以及夜间大气边界层高度偏低导致模拟高估了观测站点附近排放高度较高点源的贡献；③EDGAR中对观测站点浓度贡献较大网格点的排放误差将会很大程度上影响浓度模拟效果，表明EDGAR在排放的空间分配上的不确定性是影响模型模拟能力的主要原因；④基于EDGAR和改进清单估算的2017年12月至2018年2月长三角后验人为CO2排放通量约为（0.184±0.006） mg ·（m2 ·s）-1和（0.183±0.007） mg ·（m2 ·s）-1.研究认为应选择时间与空间分辨率更高、排放分配更准确的清单作为先验排放数据，才能对区域的人为CO2排放有更准确的估算. |
| 英文摘要 |
| Nowadays, great uncertainty still exists on the urban- and regional-scale anthropogenic CO2 emission estimation based on emission inventories. In order to achieve the carbon peaking and neutrality targets for China, it is urgent to accurately estimate anthropogenic CO2 emissions at regional scales, especially in large urban agglomerations. Using two inventories (EDGAR v6.0 inventory and a modified inventory combining EDGAR v6.0 with GCG v1.0) as prior anthropogenic CO2 emission datasets andtaking themas input data respectively, this study utilized the WRF-STILT atmospheric transport model to simulate atmospheric CO2 concentration in the Yangtze River Delta region from December 2017 to February 2018. The simulated atmospheric CO2 concentrations were further improved by referencing atmospheric CO2 concentration observation at a tall tower in Quanjiao County of Anhui Province and using the scaling factors obtained from the Bayesian inversion method. An estimation of anthropogenic CO2 emission flux in the Yangtze River Delta regionwas finally accomplished. The results indicated that:①in winter, in comparison to the atmospheric CO2 concentration simulated based on EDGAR v6.0, the atmospheric CO2 concentration simulated based on the modified inventory was more consistent with observed values. ②The simulated atmospheric CO2 concentration was higher than observation at night and lower than observation during the daytime. The CO2 emission data of emission inventories could not fully reflect the diurnal variation in anthropogenic emissions, andtheoverestimation, caused by the simulated low-atmospheric boundary layer height at night, of the contribution from point sources with higher emission height near the observation station were the main reasons. ③The simulation performance on atmospheric CO2 concentration was greatly affected by the emission bias of the EDGAR grid points that significantly contributed to concentrations of the observation station, and this indicated that the uncertainty in the spatial distribution in EDGAR emission was the main factor influencing the simulation accuracy. ④The posterior anthropogenic CO2 emission flux in the Yangtze River Delta from December 2017 to February 2018 was around (0.184±0.006) mg·(m2·s)-1and (0.183±0.007) mg·(m2·s)-1 based on EDGAR and the modified inventory, respectively. It is suggested that the inventories with higher temporal and spatial resolutions and more accurate spatial emission distribution should be selected as the prior emissions to obtain a more accurate estimation of the regional anthropogenic CO2 emissions. |
|
|
|
