MUCCnet - Munich Urban Carbon Column network

Column Greenhouse Gas Concentrations

MUCCnet is the first permanent sensor network in Munich for the quantification of the greenhouse gases carbon dioxide (CO2) and methane (CH4) and is operated by the Technical University of Munich since September 2019. The sensor network consists of five high-precision optical measuring instruments (FTIR spectrometers), which analyze the solar spectrum and can thus determine the concentration of greenhouse gases in the atmosphere. More information about the measuring principle can be found here. Our cover picture shows the southern measuring station in Taufkirchen looking south during sunrise. In order to be able to analyze the solar spectrum unhindered and to prevent vandalism, all our measuring systems are located on the roofs of public buildings. We would like to thank the municipalities of Feldkirchen, Gräfelfing, Oberschleißheim and Taufkirchen for the permission to place our measurement systems on their buildings. In the interactive graphic below, you can view the concentrations of greenhouse gases measured to date. Further down the page, our five locations in downtown Munich (TUM), Taufkirchen (TAU), Gräfelfing (GRA), Oberschleißheim (OBE) and Feldkirchen (FEL) are each shown on a map. Since our measurement principle is based on the analysis of the solar spectrum, we can only measure during the daytime and not when it is too cloudy. Therefore, there are not corresponding measured values for all days.

All data generated by our MUCCnet stations since September 2019, can be found on retrieval.esm.ei.tum.de as plots.

For better expirience, please, turn your phone into horizontal position or request the desktop website.

Sensor Footprints

What do the concentration timeseries above tell us about actual GHG-emissions?

GHG = Green House Gas
Concentration = the ratio between the number of molecules of gas x in the air column to the total number of gas molecules in the air column
Emission = how many gas particles of x are produced per time and per ground area

We are only measuring the concentration in the air column above our sensors. Therefore we need to answer the question “Where did these particles come from?”

Sensor footprints – short “footprints” – are telling us about the context of these concentrations. A large emission source next to a sensor increases the detected concentration way more than an emission source located far away. Which emitters affect the sensor value by how much is summarized by a footprint. This is determined by the wind direction which implies the transport direction of gas particles.

Footprints do not specifically list certain emitters, but rather contain an “area which influences the sensor value”. The map below includes footprints for the MUCCNET stations on June 2nd 2020.

You can select the same day in the concentration plot above.

In the morning the yellow station (WEST) detects the highest concentration. The footprint tells us that the reason for that is the wind coming from the east. Once the wind shifts to north, the WEST-sensor-value evens out.

Emission Inventories

In addition to the measured concentrations and the three-dimensional wind field, the model used requires initial emission estimates for each individual emission cell. For this purpose, we use a so-called emission inventory (here: TNO-GHGco), as shown in the figure below.

Through calculations, such inventories roughly determine the emissions of individual cells based on parameters such as settlement and traffic density, number and size of industrial emitters, agriculture, etc. We use these values shown in the map as initial values for our model and correct each individual cell using our globally unique dataset of measured greenhouse gas concentrations.

In contrast to the calculated values of an emission inventory, only real measurements can detect previously unknown emission sources and can correct wrongly quantified emission sources. Thus, we make an essential contribution to determine the actual urban emission values with high spatial and temporal resolution.