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基于不同排放清单的长江三角洲人为一氧化碳排放模拟

[Simulation of Anthropogenic CO Emissions in the Yangtze River Delta Based on Different Emission Inventories].

作者信息

Ma Xin-Yi, Huang Wen-Jing, Hu Ning, Xiao Wei, Hu Cheng, Zhang Mi, Cao Chang, Zhao Jia-Yu

机构信息

Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China.

出版信息

Huan Jing Ke Xue. 2023 Apr 8;44(4):2009-2021. doi: 10.13227/j.hjkx.202205281.

Abstract

Nowadays, great uncertainty still exists on the urban- and regional-scale anthropogenic CO 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 CO 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 CO emission datasets andtaking themas input data respectively, this study utilized the WRF-STILT atmospheric transport model to simulate atmospheric CO concentration in the Yangtze River Delta region from December 2017 to February 2018. The simulated atmospheric CO concentrations were further improved by referencing atmospheric CO 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 CO emission flux in the Yangtze River Delta regionwas finally accomplished. The results indicated that:①in winter, in comparison to the atmospheric CO concentration simulated based on EDGAR v6.0, the atmospheric CO concentration simulated based on the modified inventory was more consistent with observed values. ②The simulated atmospheric CO concentration was higher than observation at night and lower than observation during the daytime. The CO 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 CO 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 CO emission flux in the Yangtze River Delta from December 2017 to February 2018 was around (0.184±0.006) mg·(m·s)and (0.183±0.007) mg·(m·s) 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 CO emissions.

摘要

目前,基于排放清单的城市和区域尺度人为一氧化碳排放估算仍存在很大不确定性。为实现中国的碳达峰和碳中和目标,迫切需要准确估算区域尺度尤其是大型城市群的人为一氧化碳排放。本研究以两个清单(EDGAR v6.0清单以及将EDGAR v6.0与GCG v1.0相结合的修正清单)作为先验人为一氧化碳排放数据集,并分别将它们作为输入数据,利用WRF-STILT大气传输模型模拟了2017年12月至2018年2月长江三角洲地区的大气一氧化碳浓度。通过参考安徽省全椒县一座高塔的大气一氧化碳浓度观测数据并使用贝叶斯反演方法获得的缩放因子,对模拟的大气一氧化碳浓度进行了进一步改进。最终完成了长江三角洲地区人为一氧化碳排放通量的估算。结果表明:①冬季,与基于EDGAR v6.0模拟的大气一氧化碳浓度相比,基于修正清单模拟的大气一氧化碳浓度与观测值更一致。②模拟的大气一氧化碳浓度夜间高于观测值,白天低于观测值。排放清单中的一氧化碳排放数据不能完全反映人为排放的日变化,夜间模拟的低大气边界层高度导致观测站附近排放高度较高的点源贡献高估是主要原因。③对观测站浓度有显著贡献的EDGAR网格点的排放偏差对大气一氧化碳浓度的模拟性能有很大影响,这表明EDGAR排放空间分布的不确定性是影响模拟精度的主要因素。④基于EDGAR和修正清单,2017年12月至2018年2月长江三角洲地区的后验人为一氧化碳排放通量分别约为(0.184±0.006)mg·(m·s)和(0.183±0.007)mg·(m·s)。建议选择具有更高时间和空间分辨率以及更准确空间排放分布的清单作为先验排放,以获得更准确的区域人为一氧化碳排放估算。

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