In order to mitigate climate change, it is crucial to understand urban greenhouse gas (GHG) emissions precisely, as more than two-thirds of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly.We present the Munich Urban Carbon Column network (MUCCnet), the world's first urban sensor network, which has been permanently measuring GHGs, based on the principle of differential column measurements (DCMs), since summer 2019. These column measurements and column concentration differences are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city, making them a favorable input for an inversion framework and, therefore, a well-suited candidate for the quantification of GHG emissions.However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring column-averaged CO₂, CH₄ and CO concentrations with a solar-tracking Fourier transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This also includes software that starts and stops the measurements autonomously and can be used independently from the enclosure system.Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO₂ since 2015, as well as concentration gradients between sites upwind and downwind of the city. Thanks to the automation, we were also able to continue taking measurements during the COVID-19 lockdown in spring 2020. By correlating the CO₂ column concentration gradients to the traffic amount, we demonstrate that our network is capable of detecting variations in urban emissions.The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this approach for long-term greenhouse gas monitoring in urban areas.

中文翻译：

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MUCCnet：慕尼黑城市碳柱网络

为了缓解气候变化，准确了解城市温室气体（GHG）排放至关重要，因为全球超过三分之二的人为温室气体排放均来自城市。如今，城市排放估算主要基于具有高度不确定性的自下而上的计算方法。可靠且长期的自上而下的测量方法可以显着降低这些排放清单的不确定性。我们提出了慕尼黑城市碳柱网络（MUCCnet），这是世界上第一个基于永久性测量温室气体的城市传感器网络。自2019年夏季以来，采用差分色谱柱测量（DCM）原理。这些色谱柱测量值和色谱柱浓度差异对示踪剂质量和城市上风地表通量的垂直重新分布相对不敏感，因此，它们是反演框架的有利输入，因此是量化温室气体排放的合适人选。然而，建立这样的固定传感器网络需要自动测量原理。我们开发了自己的全自动封闭系统，用于测量色谱柱平均CO_在图2中，使用太阳能跟踪傅里叶变换光谱仪（EM27 / SUN）以全自动和长期的方式测量CH ₄和CO的浓度。它还包括可以自动启动和停止测量的软件，并且可以独立于外壳系统使用。此外，我们还通过展示部署在慕尼黑及其周围地区的五个传感器系统的测量结果，演示了这种传感器网络的新颖应用。 。这些结果包括自2015年以来的CO ₂季节性周期以及城市上风和下风站点之间的浓度梯度。由于自动化，我们还能够在2020年春季COVID-19锁定期间继续进行测量。通过关联CO ₂列浓度梯度到交通量，我们证明了我们的网络能够检测城市排放量的变化。我们独特的传感器网络的测量结果将与我们目前正在开发的反模型框架相结合，以监测整个城市的温室气体排放量年，确定未知的排放源并评估当前缓解策略的有效性。总而言之，我们在自动完成温室气体柱测量方面的成就将使全世界的研究人员能够建立这种方法来对城市地区的长期温室气体进行监测。