Diversity and Abundance of Ice Nucleating Strains of Pseudomonas syringae in a Freshwater Lake in Virginia, USA

The bacterium Pseudomonas syringae is found in a variety of terrestrial and aquatic environments. Some strains of P. syringae express an ice nucleation protein (hereafter referred to as Ice+) allowing them to catalyze the heterogeneous freezing of water. Though P. syringae has been sampled intensively from freshwater sources in France, little is known about the genetic diversity of P. syringae in natural aquatic habitats in North America. We collected samples of freshwater from three different depths in Claytor Lake, Virginia, USA between November 2015 and June 2016. Samples were plated on non-selective medium (TSA) and on medium selective for Pseudomonas (KBC) and closely related species to estimate the total number of culturable bacteria and of Pseudomonas, respectively. A droplet freezing assay was used to screen colonies for the Ice+ phenotype. Ice+ colonies were then molecularly identified based on the cts (citrate synthase) gene and the 16S rDNA gene. Phylogenetic analysis of cts sequences showed a surprising diversity of phylogenetic subgroups of P. syringae. Frequencies of Ice+ isolates on P. syringae selective medium ranged from 0 to 15% per sample with the highest frequency being found in spring. Our work shows that freshwater lakes can be a significant reservoir of Ice+ P. syringae. Future work is needed to determine the contribution of P. syringae from freshwater lakes to the P. syringae populations present in the atmosphere and on plants and, in particular, if freshwater lakes could be an inoculum source of P. syringae-caused plant disease outbreaks

Modification of the Mycotoxin Deoxynivalenol Using Microorganisms Isolated from Environmental Samples

The trichothecene mycotoxin deoxynivalenol (DON) is a common contaminant of wheat, barley, and maize. New strategies are needed to reduce or eliminate DON in feed and food products. Microorganisms from plant and soil samples collected in Blacksburg, VA, USA, were screened by incubation in a mineral salt media containing 100 μg/mL DON and analysis by gas chromatography mass spectrometry (GC/MS). Two mixed cultures derived from soil samples consistently decreased DON levels in assays using DON as the sole carbon source. Nuclear magnetic resonance (NMR) analysis indicated that 3-keto-4-deoxynivalenol was the major by-product of DON. Via 16S rRNA sequencing, these mixed cultures, including mostly members of the genera Acinetobacter, Leadbetterella, and Gemmata, were revealed. Incubation of one of these mixed cultures with wheat samples naturally contaminated with 7.1 μg/mL DON indicated nearly complete conversion of DON to the less toxic 3-epimer-DON (3-epi-DON). Our work extends previous studies that have demonstrated the potential for bioprospecting for microorganisms from the environment to remediate or modify mycotoxins for commercial applications, such as the reduction of mycotoxins in fuel ethanol co-products

Aerobiology over the Southern Ocean – implications for bacterial colonization of Antarctica

The Antarctic is experiencing drastic ecosystem change due to rapid and record warming. Understanding dispersal processes is paramount to assess the risks of microbiological invasions. Here, we analyze bacterial biodiversity in the circumpolar air above the Southern Ocean. This diversity had both local and global origins and presented from very low to very high Shannon biodiversity indices with consistent low richness, compatible with a scenario whereby samples are composed of a suite different species in very low abundances. However, only 4 of ASVs were identified in both Polar and non-Polar air masses, suggesting that the air mass over the Southern Ocean can act as a selective dispersal filter. Furthermore, air microbial diversity varied significantly with meteorological data, suggesting that regional bacterial biodiversity could be sensitive to changes in weather patterns. Future changes in weather patterns could, therefore, potentially alter the existing pattern of microbial deposition in the Antarctic

Ice nucleating particles carried from below a phytoplankton bloom to the arctic atmosphere

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46(14), (2019): 8572-8581, doi: 10.1029/2019GL083039.As Arctic temperatures rise at twice the global rate, sea ice is diminishing more quickly than models can predict. Processes that dictate Arctic cloud formation and impacts on the atmospheric energy budget are poorly understood, yet crucial for evaluating the rapidly changing Arctic. In parallel, warmer temperatures afford conditions favorable for productivity of microorganisms that can effectively serve as ice nucleating particles (INPs). Yet the sources of marine biologically derived INPs remain largely unknown due to limited observations. Here we show, for the first time, how biologically derived INPs were likely transported hundreds of kilometers from deep Bering Strait waters and upwelled to the Arctic Ocean surface to become airborne, a process dependent upon a summertime phytoplankton bloom, bacterial respiration, ocean dynamics, and wind‐driven mixing. Given projected enhancement in marine productivity, combined oceanic and atmospheric transport mechanisms may play a crucial role in provision of INPs from blooms to the Arctic atmosphere.We sincerely thank the U.S. Coast Guard and crew of the Healy for assistance with equipment installation and guidance, operation of the underway and CTD systems, and general operation of the vessel during transit and at targeted sampling stations. We would also like to thank Allan Bertram, Meng Si, Victoria Irish, and Benjamin Murray for providing INP data from their previous studies. J. M. C., R. P., P. L., L. T., and E. B. were funded by the National Oceanic and Atmospheric Administration (NOAA)’s Arctic Research Program. J. C. was supported by the NOAA Experiential Research & Training Opportunities (NERTO) program. T. A. and N. C. were supported through the NOAA Earnest F. Hollings Scholarship program. A. P. was funded by the National Science Foundation under Grant PLR‐1303617. Russel C. Schnell and Michael Spall are acknowledged for insightful discussions during data analysis and interpretation. There are no financial conflicts of interest for any author. INP data are available in the supporting information, while remaining DBO‐NCIS data presented in the manuscript are available online (at https://www2.whoi.edu/site/dboncis/).2020-01-1

Coordinated Sampling of Microorganisms Over Freshwater and Saltwater Environments Using an Unmanned Surface Vehicle (USV) and a Small Unmanned Aircraft System (sUAS)

Biological aerosols (bioaerosols) are ubiquitous in terrestrial and aquatic environments and may influence cloud formation and precipitation processes. Little is known about the aerosolization and transport of bioaerosols from aquatic environments. We designed and deployed a bioaerosol-sampling system onboard an unmanned surface vehicle (USV; a remotely operated boat) to collect microbes and monitor particle sizes in the atmosphere above a salt pond in Falmouth, MA, United States and a freshwater lake in Dublin, VA, United States. The bioaerosol-sampling system included a series of 3D-printed impingers, two different optical particle counters, and a weather station. A small unmanned aircraft system (sUAS; a remotely operated airplane) was used in a coordinated effort with the USV to collect microorganisms on agar media 50 m above the surface of the water. Samples from the USV and sUAS were cultured on selective media to estimate concentrations of culturable microorganisms (bacteria and fungi). Concentrations of microbes from the sUAS ranged from 6 to 9 CFU/m3 over saltwater, and 12 to 16 CFU/m3 over freshwater (over 10-min sampling intervals) at 50 m above ground level (AGL). Concentrations from the USV ranged from 0 (LOD) to 42,411 CFU/m3 over saltwater, and 0 (LOD) to 56,809 CFU/m3 over freshwater (over 30-min sampling intervals) in air near the water surface. Particle concentrations recorded onboard the USV ranged from 0 (LOD) to 288 μg/m3 for PM1, 1 to 290 μg/m3 for PM2.5, and 1 to 290 μg/m3 for PM10. A general trend of increasing concentration with an increase in particle size was recorded by each sensor. Through laboratory testing, the collection efficiency of the 3D-printed impingers was determined to be 75% for 1 μm beads and 99% for 3 μm beads. Additional laboratory tests were conducted to determine the accuracy of the miniaturized optical particle counters used onboard the USV. Future work aims to understand the distribution of bioaerosols above aquatic environments and their potential association with cloud formation and precipitation processes

Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)

Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future

Drone-based Water Sampling and Characterization of Three Freshwater Harmful Algal Blooms in the United States

Freshwater harmful algal blooms (HABs), caused mostly by toxic cyanobacteria, produce a range of cyanotoxins that threaten the health of humans and domestic animals. Climate conditions and anthropogenic influences such as agricultural run-off can alter the onset and intensity of HABs. Little is known about the distribution and spread of freshwater HABs. Current sampling protocols in some lakes involve teams of researchers that collect samples by hand from a boat and/or from the shoreline. Water samples can be collected from the surface, from discrete-depth collections, and/or from depth-integrated intervals. These collections are often restricted to certain months of the year, and generally are only performed at a limited number of collection sites. In lakes with active HABs, surface samples are generally sufficient for HAB water quality assessments. We used a unique DrOne Water Sampling SystEm (DOWSE) to collect water samples from the surface of three different HABs in Ohio (Grand Lake St Marys, GLSM and Lake Erie) and Virginia (Lake Anna), United States in 2019. The DOWSE consisted of a 3D-printed sampling device tethered to a drone (uncrewed aerial system, or UAS), and was used to collect surface water samples at different distances (10–100 m) from the shore or from an anchored boat. One hundred and eighty water samples (40 at GLSM, 20 at Lake Erie, and 120 at Lake Anna) were collected and analyzed from 18 drone flights. Our methods included testing for cyanotoxins, phycocyanin, and nutrients from surface water samples. Mean concentrations of microcystins (MCs) in drone water samples were 15.00, 1.92, and 0.02 ppb for GLSM, Lake Erie, and Lake Anna, respectively. Lake Anna had low levels of anatoxin in nearly all (111/120) of the drone water samples. Mean concentrations of phycocyanin in drone water samples were 687, 38, and 62 ppb for GLSM, Lake Erie, and Lake Anna, respectively. High levels of total phosphorus were observed in the drone water samples from GLSM (mean of 0.34 mg/L) and Lake Erie (mean of 0.12 mg/L). Lake Anna had the highest variability of total phosphorus with concentrations that ranged from 0.01 mg/L to 0.21 mg/L, with a mean of 0.06 mg/L. Nitrate levels varied greatly across sites, inverse with bloom biomass, ranging from below detection to 3.64 mg/L, with highest mean values in Lake Erie followed by GLSM and Lake Anna, respectively. Drones offer a rapid, targeted collection of water samples from virtually anywhere on a lake with an active HAB without the need for a boat which can disturb the surrounding water. Drones are, however, limited in their ability to operate during inclement weather such as rain and heavy winds. Collectively, our results highlight numerous opportunities for drone-based water sampling technologies to track, predict, and respond to HABs in the future

Data Generated during the 2018 LAPSE-RATE Campaign: An Introduction and Overview

Unmanned aircraft systems (UASs) offer innovative capabilities for providing new perspectives on the atmosphere, and therefore atmospheric scientists are rapidly expanding their use, particularly for studying the planetary boundary layer. In support of this expansion, from 14 to 20 July 2018 the International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) hosted a community flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE; de Boer et al., 2020a). This field campaign spanned a 1-week deployment to Colorado\u27s San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. These groups conducted intensive field operations using unmanned aircraft and ground-based assets to develop comprehensive datasets spanning a variety of scientific objectives, including a total of nearly 1300 research flights totaling over 250 flight hours. This article introduces this campaign and lays the groundwork for a special issue on the LAPSE-RATE project. The remainder of the special issue provides detailed overviews of the datasets collected and the platforms used to collect them. All of the datasets covered by this special issue have been uploaded to a LAPSE-RATE community set up at the Zenodo data archive (https://zenodo.org/communities/lapse-rate/, last access: 3 December 2020)

Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science During the LAPSE-RATE Campaign

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 °C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS

A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation : insights from the Multidisciplinarydrifting Observatory for the Study of Arctic Climate (MOSAiC) expedition

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019-2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January-March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.Peer reviewe