Dataset Information

Development and characterization of a high-efficiency, aircraft-based axial cyclone cloud water collector.

ABSTRACT: A new aircraft-mounted probe for collecting samples of cloud water has been designed, fabricated, and extensively tested. Following previous designs, the probe uses inertial separation to remove cloud droplets from the airstream, which are subsequently collected and stored for offline analysis. We report details of the design, operation, and modelled and measured probe performance. Computational fluid dynamics (CFD) was used to understand the flow patterns around the complex interior geometrical features that were optimized to ensure efficient droplet capture. CFD simulations coupled with particle tracking and multiphase surface transport modelling provide detailed estimates of the probe performance across the entire range of flight operating conditions and sampling scenarios. Physical operation of the probe was tested on a Lockheed C-130 Hercules (fuselage mounted) and de Havilland Twin Otter (wing pylon mounted) during three airborne field campaigns. During C-130 flights on the final field campaign, the probe reflected the most developed version of the design and a median cloud water collection rate of 4.5 mL min^-1 was achieved. This allowed samples to be collected over 1-2 min under optimal cloud conditions. Flights on the Twin Otter featured an inter-comparison of the new probe with a slotted-rod collector, which has an extensive airborne campaign legacy. Comparison of trace species concentrations showed good agreement between collection techniques, with absolute concentrations of most major ions agreeing within 30 %, over a range of several orders of magnitude.

SUBMITTER: Crosbie E

PROVIDER: S-EPMC8051007 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and characterization of a high-efficiency, aircraft-based axial cyclone cloud water collector.

Crosbie Ewan E Brown Matthew D MD Shook Michael M Ziemba Luke L Moore Richard H RH Shingler Taylor T Winstead Edward E Lee Thornhill K K Robinson Claire C MacDonald Alexander B AB Dadashazar Hossein H Sorooshian Armin A Beyersdorf Andreas A Eugene Alexis A Collett Jeffrey J Straub Derek D Anderson Bruce B

Atmospheric measurement techniques 20180801 9

A new aircraft-mounted probe for collecting samples of cloud water has been designed, fabricated, and extensively tested. Following previous designs, the probe uses inertial separation to remove cloud droplets from the airstream, which are subsequently collected and stored for offline analysis. We report details of the design, operation, and modelled and measured probe performance. Computational fluid dynamics (CFD) was used to understand the flow patterns around the complex interior geometrical ...[more]

PMID: 33868504

Similar Datasets

Project description:UnlabelledAir travel can rapidly transport infectious diseases globally. To facilitate the design of biosensors for infectious organisms in commercial aircraft, we characterized bacterial diversity in aircraft air. Samples from 61 aircraft high-efficiency particulate air (HEPA) filters were analyzed with a custom microarray of 16S rRNA gene sequences (PhyloChip), representing bacterial lineages. A total of 606 subfamilies from 41 phyla were detected. The most abundant bacterial subfamilies included bacteria associated with humans, especially skin, gastrointestinal and respiratory tracts, and with water and soil habitats. Operational taxonomic units that contain important human pathogens as well as their close, more benign relatives were detected. When compared to 43 samples of urban outdoor air, aircraft samples differed in composition, with higher relative abundance of Firmicutes and Gammaproteobacteria lineages in aircraft samples, and higher relative abundance of Actinobacteria and Betaproteobacteria lineages in outdoor air samples. In addition, aircraft and outdoor air samples differed in the incidence of taxa containing human pathogens. Overall, these results demonstrate that HEPA filter samples can be used to deeply characterize bacterial diversity in aircraft air and suggest that the presence of close relatives of certain pathogens must be taken into account in probe design for aircraft biosensors.Practical implicationsA biosensor that could be deployed in commercial aircraft would be required to function at an extremely low false alarm rate, making an understanding of microbial background important. This study reveals a diverse bacterial background present on aircraft, including bacteria closely related to pathogens of public health concern. Furthermore, this aircraft background is different from outdoor air, suggesting different probes may be needed to detect airborne contaminants to achieve minimal false alarm rates. This study also indicates that aircraft HEPA filters could be used with other molecular techniques to further characterize background bacteria and in investigations in the wake of a disease outbreak.

Project description:In the future, water may not be as readily available due to increases in competition from a growing human population, wildlife, and other agricultural sectors, making selection for water efficiency of beef cattle increasingly important. Substantial selection emphasis has recently been placed on feed efficiency in an effort to reduce production costs, but no emphasis has been placed on making cattle more water efficient due to lack of data. Thus, the objective of this study was to calculate water efficiency metrics for cattle and evaluate their relationship to growth, feed intake (FI), and feed efficiency. Individual daily FI and water intake (WI) records were collected on 578 crossbred steers over a 70-d test period. Animals with low water intake ate less feed, had lower gains, and were more water efficient (as defined by water to gain ratio, W/G, and residual water intake, RWI). However, the amount of water consumed by animals had minimal phenotypic relationship with feed efficiency (residual feed intake [RFI], R2 = 0.1050 and feed to gain ratio (F/G) ratio R2 = 0.0726). Cattle that had low DMI consumed less water, had lower gains, had lower RFI, and had higher F/G. The level of feed consumed had minimal relationship with water efficiency. WI, W/G, RWI, and ADG had moderate heritability estimates of 0.39, 0.39, 0.37, and 0.37, respectively. High heritability estimates were observed for DMI and RFI (0.67 and 0.65, respectively). Feed to gain had a low heritability estimate of 0.16. WI had a strong positive genetic correlation with W/G (0.99) and RWI (0.88), thus selecting for decreased WI should also make cattle more water efficient. The genetic correlation between WI and ADG was 0.05; thus, selecting for low WI cattle should have little effect on growth. There is a low to moderate genetic correlation between WI and DMI (0.34). RWI has a positive genetic correlation with W/G ratio (0.89) and F/G ratio (0.42) and is negatively genetically correlated with RFI (-0.57). Water to gain and F/G had a strong positive genetic correlation (0.68). RFI has a positive genetic correlation with W/G ratio (0.37) and F/G (0.88). Minimal antagonisms seem to be present between WI and ADG, although it should be noted that standard errors were large and often not significantly different from zero due to the small sample size. However, care should be taken to ensure that unintended changes do not occur in DMI or other production traits and incorporation of WI into a selection index would likely prove to be the most effective method for selection.

Project description:Aerosol-cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (N d). Even though rigorous first-principle approaches exist to calculate N d, the cloud and aerosol research community also relies on empirical approaches such as relating N d to aerosol mass concentration. Here we analyze relationships between N d and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log-log linear regressions were performed to predict N d using chemical composition. Single-species regressions reveal that the species that best predicts N d is total sulfate ( Radj2=0.40 ). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with N d was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition-N d correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between N d with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with N d for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with N d at cloud top, whereas iron and oxalate correlated best with N d at cloud base.

Project description:This article presents the development of a Portable Aerosol Collector and Spectrometer (PACS), an instrument designed to measure particle number, surface area, and mass concentrations continuously and time-weighted mass concentration by composition from 10 nm to 10 μm. The PACS consists of a six-stage particle size selector, a valve system, a water condensation particle counter to detect number concentrations, and a photometer to detect mass concentrations. The stages of the selector include three impactor and two diffusion stages, which resolve particles by size and collect particles for later chemical analysis. Particle penetration by size was measured through each stage to determine actual collection performance and account for particle losses. The data inversion algorithm uses an adaptive grid-search process with a constrained linear least-square solver to fit a tri-modal (ultrafine, fine, and coarse), log-normal distribution to the input data (number and mass concentration exiting each stage). The measured 50% cutoff diameter of each stage was similar to the design. The pressure drop of each stage was sufficiently low to permit its operation with portable air pumps. Sensitivity studies were conducted to explore the influence of unknown particle density (range from 500 to 3,000 kg/m3) and shape factor (range from 1.0 to 3.0) on algorithm output. Assuming standard density spheres, the aerosol size distributions fit well with a normalized mean bias of -4.9% to 3.5%, normalized mean error of 3.3% to 27.6%, and R2 values of 0.90 to 1.00. The fitted number and mass concentration biases were within ±10% regardless of uncertainties in density and shape. However, fitted surface area concentrations were more likely to be underestimated/overestimated due to the variation in particle density and shape. The PACS represents a novel way to simultaneously assess airborne aerosol composition and concentration by number, surface area, and mass over a wide size range.

Dataset Information

Development and characterization of a high-efficiency, aircraft-based axial cyclone cloud water collector.

Publications

Development and characterization of a high-efficiency, aircraft-based axial cyclone cloud water collector.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets