Laboratoire des Sciences du Climat et de l'Environnement, CEA, CNRS, UVSQ/IPSL, Université Paris-Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette Cedex, France.
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
Environ Sci Technol. 2020 Aug 18;54(16):10237-10245. doi: 10.1021/acs.est.0c00343. Epub 2020 Aug 6.
Global fossil fuel carbon dioxide (FFCO) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO emissions estimation through the atmospheric CO inversion process. Whole-city FFCO emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO emissions, underconstrained despite the inclusion of coemitted species information.
全球化石燃料二氧化碳（FFCO）排放将在很大程度上取决于城市地区排放的轨迹。传统的量化城市 FFCO 排放的方法通常依赖于自我报告的经济/能源活动数据，通过标准排放因子转化为排放量。然而，这些传统方法的不确定性给有效缓解策略的实施、独立监测长期趋势和评估政策结果带来了障碍。在这里，我们展示了通过大气 CO 反演过程将温室气体传感器的密集网络与科学驱动的建筑物和街道尺度 FFCO 排放估算相结合的适用性。整个城市的 FFCO 排放量每年相差 3%以内。印第安纳波利斯市目前的自我报告清单排放量比我们的最佳估计值低 35%，各活动部门的差异显著。然而，尽管包含了共排放物种的信息，部门 FFCO 排放的空间分布仍然存在差异，而且受到限制。
