Chemical composition of ambient aerosol, ice residues and cloud droplet residues in mixed-phase clouds: single particle analysis during the Cloud and Aerosol Characterization Experiment (CLACE 6)

Two different single particle mass spectrometers were operated in parallel at the Swiss High Alpine Research Station Jungfraujoch (JFJ, 3580 m a.s.l.) during the Cloud and Aerosol Characterization Experiment (CLACE 6) in February and March 2007. During mixed phase cloud events ice crystals from 5–20 micro m were separated from larger ice aggregates, non-activated, interstitial aerosol particles and supercooled droplets using an Ice-Counterflow Virtual Impactor (Ice-CVI). During one cloud period supercooled droplets were additionally sampled and analyzed by changing the Ice-CVI setup. The small ice particles and droplets were evaporated by injection into dry air inside the Ice-CVI. The resulting ice and droplet residues (IR and DR) were analyzed for size and composition by the two single particle mass spectrometers: a custom-built Single Particle Laser-Ablation Time-of-Flight Mass Spectrometer (SPLAT) and a commercial Aerosol Time-of-Flight Mass Spectrometer (ATOFMS, TSI Model 3800). During CLACE 6 the SPLAT instrument characterized 355 individual IR that produced a mass spectrum for at least one polarity and the ATOFMS measured 152 IR. The mass spectra were binned in classes, based on the combination of dominating substances, such as mineral dust, sulfate, potassium and elemental carbon or organic material. The derived chemical information from the ice residues is compared to the JFJ ambient aerosol that was sampled while the measurement station was out of clouds (several thousand particles analyzed by SPLAT and ATOFMS) and to the composition of the residues of supercooled cloud droplets (SPLAT: 162 cloud droplet residues analyzed, ATOFMS: 1094). The measurements showed that mineral dust was strongly enhanced in the ice particle residues. Close to all of the SPLAT spectra from ice residues did contain signatures from mineral compounds, albeit connected with varying amounts of soluble compounds. Similarly, close to all of the ATOFMS IR spectra show a mineral or metallic component. Pure sulfate and nitrate containing particles were depleted in the ice residues. Sulfate and nitrate was found to dominate the droplet residues (~90% of the particles). The results from the two different single particle mass spectrometers were generally in agreement. Differences in the results originate from several causes, such as the different wavelength of the desorption and ionisation lasers and different size-dependent particle detection efficiencies

Gebietskörperschaft

Gebietskörperschaften sind Körperschaften des öffentlichen Rechts, bei denen sich die Mitgliedschaft aus dem Wohnsitz im Gebiet der Körperschaft ergibt und die mit Gebietshoheit ausgestattet sind. Unter den Begriff der Gebietskörperschaft fallen die Gemeinden, die Kreise, die über eine eigene Staatlichkeit verfügenden Länder und der Bund

Integrated cantilever-based flow sensors with tunable sensitivity for in-line monitoring of flow fluctuations in microfluidic systems

For devices such as bio-/chemical sensors in microfluidic systems, flow fluctuations result in noise in the sensor output. Here, we demonstrate in-line monitoring of flow fluctuations with a cantilever-like sensor integrated in a microfluidic channel. The cantilevers are fabricated in different materials (SU-8 and SiN) and with different thicknesses. The integration of arrays of holes with different hole size and number of holes allows the modification of device sensitivity, theoretical detection limit and measurement range. For an average flow in the microliter range, the cantilever deflection is directly proportional to the flow rate fluctuations in the microfluidic channel. The SiN cantilevers show a detection limit below 1 nL/min and the thinnest SU-8 cantilevers a detection limit below 5 nL/min. Finally, the sensor is applied for in-line monitoring of flow fluctuations generated by external pumps connected to the microfluidic system

Towards detecting, characterizing and rating of road class errors in crowd-sourced road network databases

OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality which could then result in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability in order to suggest possible misclassifications to a human user. The methodology is applied exemplarily for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub