Stagakis Stavros, Feigenwinter Christian, Vogt Roland, Brunner Dominik, Kalberer Markus
Department of Environmental Sciences, University of Basel, Klingelbergstrasse 27, 4056 Basel, Switzerland.
Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Sci Total Environ. 2023 Dec 10;903:166035. doi: 10.1016/j.scitotenv.2023.166035. Epub 2023 Aug 3.
Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO source/sink mixture, is still evident in the optimised fluxes.
到2050年实现气候中和需要在从地方到区域尺度的气候变化缓解规划和行动方面取得突破性的技术和方法进步。以足够的细节和精度监测城市的一氧化碳排放对于指导可持续城市转型至关重要。当前的一氧化碳排放清单方法依赖于自下而上（BU）的方法，这些方法通常无法提供排放的空间或时间变异性信息，并且存在很大的不确定性。本研究开发了一种新方法，该方法在贝叶斯反演框架内，将来自城市涡度协方差（EC）塔的直接一氧化碳通量观测与来自先进的城市自下而上表面通量模型（本研究的第1部分，斯塔加基斯等人，2023年）的非常高时空分辨率信息进行同化。该方法应用于瑞士巴塞尔市中心（3×3公里区域），利用了相距1.6公里的两个长期城市EC站点。数据同化以20米分辨率和每周时间步长分别为每个城市表面通量分量（即建筑物供暖排放、商业/工业排放、交通排放、人类呼吸排放、生物源净交换）提供优化的网格化一氧化碳通量信息。结果表明，城市EC观测可以持续用于改进高分辨率的自下而上表面一氧化碳通量模型估计，提供每个通量分量实际的季节变异性。在反演过程中，交通排放是五个通量分量中确定度最高的。优化后的年度人为排放量比先前估计值低14.7%，人类呼吸排放量减少了12.1%，而生物源分量从弱汇转变为弱源。EC观测与模型输出之间每周比较的均方根误差（RMSE）持续降低。然而，在优化后的通量中，总通量仍存在轻微低估，特别是在一氧化碳源/汇混合复杂的位置。
