Beneficiaries and their projects
|Dr. Maciej Bartosiewicz, University of Basel
|Coupling carbon processing to microbiomes in small permafrost ponds: A real time observation platform
|Dr. Guillaume Jouvet, ETHZ
|Autonomous retrieval of ice samples by Unmanned Aerial Vehicle
|Prof. Julia Schmale, EPFL
|Developing the ‘Modular Multiplatform Compatible Air Measurement System’ (MoMuCAMS)
|Dr. Martin Vollmer, Empa
|DAAMAA: Digital Air Archive Markers in the Arctic and Antarctic
Dr. Maciej Bartosiewicz
Project: Coupling carbon processing to microbiomes in small permafrost ponds: A real time observation platform
Keywords: Climate change, carbon emissions, thermokarst, methanogens, microbial diversity
Even though thermokarst lakes and ponds, formed abundantly on the thawing permafrost, often transport large amounts of the old carbon to the atmosphere in the form of greenhouse gases (GHGs), real-time observations of these emissions in relation to hydrodynamic conditions are scarce. Consequently, of how closely the processing of old carbon in thermokarst waters is coupled to the functionality and diversity of the associated microbiomes is limited. This proposal aims to develop an independent monitoring platform to be deployed in multiple small and shallow ponds located within the Kytalyk Nature Reserve (Siberia) and record diurnal variability in temperature, conductivity, shear and convective mixing as well as O2, CO2 and CH4 concentrations in relations to microbial abundance and community structure.
The first purpose of the platform will be to assess the spatiotemporal variability in the hydrodynamics within the instrumented ponds in response to different meteorological forcing. This will allow us to extrapolate real-time findings to a broader range of thermokarst ponds located in Siberia, Greenland and Canada. We will establish whether and when surface turbulence is driven by winds or heat fluxes. In this context it is important to note that shallow and small thermokarst ponds that are often sheltered from winds show unique, yet insufficiently characterized, stratification behavior and that additional efforts are required to accurately quantify the turbulence under future climate conditions. Understanding hydrodynamic conditions and their controlling factors is crucial to quantify and predict gaseous exchanges at the water-atmosphere interface.
The second purpose of the proposed development is to record changes in oxygenation and CO2 saturation levels for instrumented ponds in response to changing hydrodynamics but also to different organic carbon inputs (i.e., ponds with contrasting organic carbon levels). This real-time dataset will be complemented by discrete sampling for microbial abundance and communities taken daily in instrumented ponds.
The third, and potentially most crucial, purpose of the platform development, will be to allow us for a continuous monitoring of changes in CH4 saturation levels. The dataset resulting from this project will allow us to complete budgeting of the carbonaceous greenhouse gases for these thermokarst ponds. Again, the real-time monitoring of concentrations over several days (to weeks) will be complemented by a quantitative characterization of the microbial community structure toward methane generating and degrading microbes and further amended by isotopic (13C, D) characterization of methane to better constrain contribution from different production and consumption processes.
Dr. Guillaume Jouvet
Project: Autonomous retrieval of ice samples by Unmanned Aerial Vehicle
Keywords: UAV, glacier, in situ measurements, ice core sampling
The biogeochemical composition of ice from glaciers, ice sheets, and icebergs contains invaluable information for many disciplines such as climatology, glaciology, and marine ecology. However, ice sampling can be dangerous and expensive in general, and some environments are impossible for humans to access. Unmanned aerial vehicles (UAVs or drones) can be used to explore dangerous glacial environments relatively inexpensively while eliminating risks to humans. This project aims to combine recent developments in UAV, computer vision, and ice drilling technologies to develop an autonomous retrieval method of ice samples in harsh glacial environments by UAVs.
For that purpose, a high lift capacity multirotor UAV will be outfitted with an existing lightweight ice coring drill, which proved his worth providing the UAV to be piloted manually during the most critical phases. Key challenges for making it fully autonomous are to select a priori an obstacle-free, flat, and debris-free area to drill, to land there very smoothly and accurately, and to ensure stability while drilling. To deal with these challenges, preliminary photogrammetry and thermal flights will be performed to produce accurate optical and thermal maps to identify the most suitable drilling sites. Then, a navigation method based on a priori knowledge of the terrain will be developed to ensure highly accurate landing capabilities.
The technology will be tested at Gorner glacier, Switzerland, which is ideal for autonomous ice core sampling for both logistical and scientific reasons. As a first application, the drilling drone will serve to collect ice samples for dating purpose, thereby contributing to learn about the long-term ice dynamics of the glacier. If successful, the methods developed during this project will enable researchers to safely collect ice cores in other remote and inaccessible glacial environments, such as in polar regions.
This project is a collaboration with Dr. Daniel Carlson (Helmholtz-Zentrum Geesthacht).
Prof. Julia Schmale
Project: Developing the ‘Modular Multiplatform Compatible Air Measurement System’ (MoMuCAMS)
Keywords: Vertical profiling, aerosols, Arctic air pollution, high-quality small sensors
Aerosols play an important role in the Arctic’s radiative budget, either directly by scattering or absorbing light or through aerosol-cloud interactions. The aerosols’ influence on climate depends on particle properties such as size, number concentration and chemical composition but also vertical distribution. Aerosols also have an impact on human health, as fine particles can cause respiratory diseases when inhaled.
Despite a denser network of ground-based measurement facilities throughout the world, little is still known about the vertical distribution of aerosols, especially in polar regions. We know that aerosol properties and distributions exhibit seasonal variation due to the relationship between prevalent sources and meteorological conditions influencing transport from source regions and chemical reactions. However, many uncertainties remain regarding the physical and chemical processes leading to aerosol dispersion and their impacts.
The MoMuCAMS (Modular Multiplatform Compatible Air Measurement System) project will help fill the lack of vertical aerosol and trace gas measurements in high latitude regions and allow the investigation of long-standing questions. The system includes a series of lightweight and small-sized instruments placed in a box, which hangs under a helikite (tethered balloon) for vertical atmospheric measurements in extreme environments. The payload is a modular platform allowing for easy modification of the instrumentation setup based on lifting capacities of the helikite and measurement objectives. MoMuCAMS contains four main instrumentation packages (1. An aerosol particle package 2. A trace gas package 3. A meteorological package 4. A turbulence and radiation package [to be developed at a later stage]) for a complete understanding of the vertical aerosol distribution and impact on Arctic climate. A central payload computer will collect and store data from each instrument and enable remote communication with the ground station via a serial radio system. Remote communication will provide real-time data monitoring for decision making on sampling strategies and for remote activation of certain instruments.
A first objective of MoMuCAMS will be the collection of data in remote Arctic regions (i.e. Villum, Greenland) to primarily understand the vertical distribution of aerosols in clean environments. Analysis will also focus on exchanges between the planetary boundary layer and free troposphere in order to understand whether Arctic clouds are primarily fed from local or remote sources.
A second objective is to deploy the package in high latitude urban regions (i.e. Fairbanks, Alaska) where the combination of ground-based temperature inversions and local sources of pollution from industrial activities and domestic heating causes the different pollutants to be trapped in the inversion layer. Measurements close to polluting sources will help us understand chemical reactions between different aerosols and trace gas species during nighttime and at cold temperatures.
Dr. Martin Vollmer
Project: DAAMAA: Digital Air Archive Markers in the Arctic and Antarctic
Keywords: High-resolution mass spectrometry, trace gases, remote locations, air archive, machine learning
The main driver of global climate change in the Anthropocene is the human-induced increase in atmospheric trace gases in the atmosphere. Polar regions are ideal locations to quantify the changes in atmospheric composition on a global scale, due to their remoteness from immediate pollution, their north-south hemispheric gradients, and because of their links between the present and past climate through the signatures stored in polar firn and ice. These important facts, combined with the pioneering of new analytical capabilities calls for studying the modern atmosphere at these locations.
The project DAAMAA (Digital Air Archive Marker in the Arctic and Antarctic) allows creating a time marker of the trace gas composition of the current polar atmospheres by detecting molecule fragments of nearly all presently known and yet unknown trace gases. With respect to established techniques, this approach results in a complete fingerprint of the polar background air that will be used for both classic spectrometric analysis and more explorative machine learning tools that are currently being developed.
In DAAMAA we will collect air samples from the Zeppelin (Artic) and Troll (Antarctic) stations. The samples will be analyzed by APRECON-GC-ToF-MS (air preconcentration gas chromatography time-of-flight mass spectrometry) to create the first digital time marker of the modern atmosphere. Machine learning tools will be applied for the detection of unknown atmospheric trace gases (non-target analysis method). This is expected to lead to the discovery of hitherto unknown trace gases which contribute to climate warming or that impact the sensitive ecosystems of the polar regions. In addition to the digital fingerprint, the physical samples will be stored to create a long-term archive for additional studies linking the past atmosphere (stored in firn and ice) with the modern or future atmospheric composition.
DAAMAA is a project by Empa in collaboration with the Norwegian Institute for Air Research (NILU) and benefits from collaborations with the long-term Advanced Global Atmospheric Gases Experiment (AGAGE).