Automating greenhouse gas measurements
Urban areas are climate hot spots that contribute some two-thirds of the total anthropogenic emissions. When determining the emissions of greenhouse gases (GHGs), researchers have typically relied on bottom-up approaches that inventory all the stationary sources, such as power plants, and calculate estimates for mobile sources, such as cars and trucks. An alternative top-down method remotely captures emissions data by measuring the average GHG concentration in a vertical column of the atmosphere. Researchers suspect that the column measurements are more accurate than surface measurements: They’re not only insensitive to the dynamics of the boundary layer directly above Earth’s surface but also less influenced by local disturbances.
Now graduate student Florian Dietrich
MUCCnet operates via the principle of differential column measurements: A small set of sensors collects the average concentration of carbon dioxide, methane, and other GHGs over a large area. The researchers strategically arranged the sensors in locations upwind and downwind of the city, and the software automatically calculates Munich’s GHG emissions as the difference of the averages.
Shown in the picture above is one of the sensors in the network. Enclosed in the device is a Fourier-transform IR (FTIR) spectrometer that measures how much sunlight a sample of atmospheric gas absorbs. A Fourier transform of the raw absorption spectrum produces a spectrum of sharp peaks for identifying the GHGs in a sample. The entire GHG monitoring process is automatic: Each FTIR spectrometer connects to a computer that shares the data over the internet with the other sensors, and the total emissions are then calculated.
F. Dietrich et al., Atmos. Meas. Tech. 14, 1111 (2021)
Since the MUCCnet was deployed in 2019, it has had no data-collection interruptions, even in the midst of the coronavirus pandemic. The initial results (blue line) in the figure to the right faithfully reproduce the uptake of CO2 by photosynthesizing plants and track the significant decrease in Munich’s congestion (red line) during the quarantine period (gray shaded box). Many applications of MUCCnet’s GHG data remain. Dietrich, Chen, and their colleagues are, for example, pairing the data with an atmospheric transport model to continuously monitor city emissions and to search for any potential correlations of GHG emissions with time of day, season, and weather. (F. Dietrich et al., Atmos. Meas. Tech. 14, 1111, 2021
More about the Authors
Alex Lopatka. alopatka@aip.org