Leveraging CO2 sensor networks to address challenges in urban eddy-covariance measurements

Eddy-covariance measurements allow us to directly monitor the vertical turbulent CO₂ flux at a specific point in the urban atmosphere. Under some assumptions such as stationarity and sufficient turbulence, this flux corresponds to the net emissions in a variable footprint area. Combined with a footprint model and a biospheric CO₂ flux model, this method has a high potential for validating and optimizing urban emission inventories. However, the reliability of EC measurements depends on a careful site selection, data processing and quality control. Often, sensor heights below z=50 m a.g.l. are chosen to mitigate issues associated with horizontal heterogeneity, storage flux, and horizontal and vertical advection. The storage flux describes the temporal change of the CO₂ amount in the control volume between the surface and sensor height. Tall-tower sites (z>50 m a.g.l.) would be beneficial to capture emissions from a larger part of the city but require careful consideration of these issues. While a few studies have reported plausible EC measurements for urban tall-tower sites, little is known about the impact of the storage flux and advection terms. 
In the ICOS-Cities project, tall-tower EC systems and networks of mid-cost and low-cost CO₂ concentration sensors were installed in three cities. Here, we aim to better quantify the storage flux and identify periods with horizontal advection by leveraging data from the sensor networks in Zurich, Switzerland, and Munich, Germany, and thus improve the reliability of the observed net CO₂ emissions. The low-cost sensors were deployed in the urban canopy layer while the mid-cost sensors were mostly located at the rooftop level and collocated with wind and temperature sensors. We estimate the storage flux by dividing the control volume into three to four layers and averaging data from different sensors in the same layer. The storage flux is then added to the turbulent flux to estimate net surface emissions. To filter out periods in which this estimate is biased by horizontal advection, we consider horizontal CO₂ gradients determined using mid-cost sensors at rooftop sites. This approach is compared to the often-used filtering with a friction velocity threshold.
As expected, the storage flux is most important on days with a pronounced diurnal cycle in atmospheric stability. It reduces the net CO₂ emission estimates in the morning hours after sunrise and generally increases these estimates at night. From 1.5 to 5 h after sunrise, this effect amounts on average to -7.3 and -8.0 µmol m^-2 s^-1 in Zurich and Munich, respectively, while in the first 3.5 hours after sunset, it amounts to +4.7 and +3.0 µmol m^-2 s^-1 (46% and 24% of the turbulent flux) in Zurich and Munich, respectively. On days with a small diurnal cycle in stability, the storage flux plays a smaller role, especially in winter. We will also present insights in the frequency of horizontal advection and favorable conditions for it. Finally, we will discuss the plausibility of median diurnal cycles of the derived net CO₂ emissions, considering the directional dependence on land cover and associated sources and sinks.