Lagrangian coherent structures are associated with fluctuations in airborne microbial populations

In the wind: Invasive species travel along predictable atmospheric pathways

Invasive species such as insects, pathogens, and weeds reaching new environments by traveling with the wind, represent unquantified and difficult-to-manage biosecurity threats to human, animal, and plant health in managed and natural ecosystems. Despite the importance of these invasion events, their complexity is reflected by the lack of tools to predict them. Here, we provide the first known evidence showing that the long-distance aerial dispersal of invasive insects and wildfire smoke, a potential carrier of invasive species, is driven by atmospheric pathways known as Lagrangian coherent structures (LCS). An aerobiological modeling system combining LCS modeling with species biology and atmospheric survival has the potential to transform the understanding and prediction of atmospheric invasions. The proposed modeling system run in forecast or hindcast modes can inform high-risk invasion events and invasion source locations, making it possible to locate them early, improving the chances of eradication success.

Fog-Induced Alteration in Airborne Microbial Community: a Study over Central Indo-Gangetic Plain in India

Fog supports an increase in airborne microbial loading by providing water with nutrients and protecting it from harmful incoming solar radiation. To improve our present understanding of fog-induced alteration in an atmospheric microbial community, a study was conducted during 1 to 14 January 2021 for continuous investigation of airborne bacteria over a rural site, Arthauli (25.95°N, 85.10°E), in central Indo-Gangetic Plain (IGP) in India. An increase of 36% ± 0.4% in airborne bacterial loading was noticed under fog versus prefog conditions, and a decrease of 48% ± 0.4% was noticed under the postfog condition. Airborne bacterial loading had a strong correlation with RH (R2 = 0.56; P < 0.05), temperature (R2 = -0.55, P < 0.05), and wind speed (R2 = -0.52, P < 0.05). Unique types of bacteria, representing about 29% of the whole community, were detected only under foggy conditions, likely by a continuous supply of nutrients and water from a cold, calm, and humid atmosphere. As a result, no significant diurnal variation of bacterial loading was noticed on a foggy day, with a higher daily mean concentration of about (8.4 ± 1.7) × 105 cells · m-3 than that on a typical winter day [(6.3 ± 3.8) × 105 cells · m-3]. A typical winter day experienced about a 60% decrease in bacterial loading in the afternoon in comparison to that in the morning. A 3-day back-trajectory analysis suggests a slow movement of airmass along with the wind blowing from west to central IGP. Fog pauses wind movement, which reduces continuous transportation of urban sources while increasing airborne bacteria from local sources. The abundances of Gp6 (14.8% ± 8.6%), Anaeromyxobacter (7.1% ± 2.8%), and Gp7 (6.8 ± 2.6%) have been observed to increase due to occurrences of fog over central IGP. IMPORTANCE Fog was investigated in the present study as a cause of alteration in the airborne microbial community. Occurrences of fog were responsible for an increase in airborne microbial loading (36%) over central IGP in India due to the easy availability of nutrients and water in the air and dimming of harmful solar radiation. More than 90% of unique bacteria were detected under fog (64%) and postfog (28%) conditions. A few bacteria, like Gp18 (0.5% ± 0.3%), Alicyclobacillus (0.5% ± 0.1%), Sinomonas (0.4% ± 0.2%), and Phenylobacterium (0.4% ± 0.2%), were detected only under foggy conditions. A strong correlation between meteorological parameters and bacterial loading was found in the current research work. The present study provides additional support toward a new direction in interdisciplinary science for the detailed investigations of the effects of meteorological conditions on airborne bacteria and their implications for society.

Finite-horizon, energy-efficient trajectories in unsteady flows

Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

Particle capture in a model chaotic flow

To better understand and optimize the capture of passive scalars (particles, pollutants, greenhouse gases, etc.) in complex geophysical flows, we study capture in the simpler, but still chaotic, time-dependent double-gyre flow model. For a range of model parameters, the domain of the double-gyre flow consists of a chaotic region, characterized by rapid mixing, interspersed with nonmixing islands in which particle trajectories are regular. Capture units placed within the domain remove all particles that cross their perimeters without altering the velocity field. To predict the capture capability of a unit at an arbitrary location, we characterize the trajectories of a uniformly seeded ensemble of particles as chaotic or nonchaotic, and then use them to determine the spatially resolved fraction of time that the flow is chaotic. With this information, we can predict where to best place units for maximum capture. We also examine the time dependence of the capture process, and demonstrate that there can be a trade-off between the amount of material captured and the capture rate.

Local meteorological conditions, shape and desiccation influence dispersal capabilities for airborne microorganisms

The atmosphere plays an important role in the dispersal of microorganisms, as well as in the connectivity of most of the planet's ecosystems. In recent decades, interest in microbial diversity and dispersion in the atmosphere has increased due to its importance in various fields. However, there are few studies on the abundance of airborne microorganisms and the factors, such as meteorology, that affect their distribution. Likewise, the physical-mathematical models attempting to reproduce their possible origins also require integrating some biological features. We collected airborne microorganisms under different meteorological conditions at a sampling station over a 12-day period to expand the knowledge about abundance of airborne microorganisms, their relationship with atmospheric conditions and their possible origins with a biological perspective. Total abundance and size distribution of microorganisms were measured in all samples using epifluorescence techniques. Their possible origins were estimated using refined mathematical simulation models of the air masses back-trajectories considering dry deposition. Our results showed microbial abundance values similar to those found in temperate regions over land surface. In our contribution we report a clear relationship between the abundance and, considered as a whole, local meteorological conditions. Despite most of the captured particles were small spherical microorganisms (diameter < 20 μm), large filamentous microorganisms, surprisingly up to 400 μm, were also found. We demonstrate the possibility that these large microorganisms can have their origin at long distances, showing thus probability of remarkable long dispersal, without ruling out a nearby origin, when their equivalent spherical diameter (ESD) and drying capacity are considered.

A Method for Detecting Atmospheric Lagrangian Coherent Structures Using a Single Fixed-Wing Unmanned Aircraft System

The transport of material through the atmosphere is an issue with wide ranging implications for fields as diverse as agriculture, aviation, and human health. Due to the unsteady nature of the atmosphere, predicting how material will be transported via the Earth’s wind field is challenging. Lagrangian diagnostics, such as Lagrangian coherent structures (LCSs), have been used to discover the most significant regions of material collection or dispersion. However, Lagrangian diagnostics can be time-consuming to calculate and often rely on weather forecasts that may not be completely accurate. Recently, Eulerian diagnostics have been developed which can provide indications of LCS and have computational advantages over their Lagrangian counterparts. In this paper, a methodology is developed for estimating local Eulerian diagnostics from wind velocity data measured by a single fixed-wing unmanned aircraft system (UAS) flying in a circular arc. Using a simulation environment, driven by realistic atmospheric velocity data from the North American Mesoscale (NAM) model, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and also predict the passage of LCSs. This methodology requires only a single flying UAS, making it easier and more affordable to implement in the field than existing alternatives, such as multiple UASs and Dopler LiDAR measurements. Our method is general enough to be applied to calculate the gradient of any scalar field.

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.

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.

Diversity and Ice Nucleation Activity of Microorganisms Collected With a Small Unmanned Aircraft System (sUAS) in France and the United States

Many microbes relevant to crops, domestic animals, and humans are transported over long distances through the atmosphere. Some of these atmospheric microbes catalyze the freezing of water at higher temperatures and facilitate the onset of precipitation. We collected microbes from the lower atmosphere in France and the United States with a small unmanned aircraft system (sUAS). 55 sampling missions were conducted at two locations in France in 2014 (an airfield in Pujaut, and the top of Puy de Dôme), and three locations in the U.S. in 2015 (a farm in Blacksburg, Virginia, and a farm and a lake in Baton Rouge, Louisiana). The sUAS was a fixed-wing electric drone equipped with a remote-operated sampling device that was opened once the aircraft reached the desired sampling altitude (40-50 meters above ground level). Samples were collected on agar media (TSA, R4A, R2A, and CA) with and without the fungicide cycloheximide. Over 4,000 bacterial-like colonies were recovered across the 55 sUAS sampling missions. A positive relationship between sampling time and temperature and concentrations of culturable bacteria was observed for sUAS flights conducted in France, but not for sUAS flights conducted in Louisiana. A droplet freezing assay was used to screen nearly 2,000 colonies for ice nucleation activity, and 15 colonies were ice nucleation active at temperatures warmer than -8°C. Sequences from portions of 16S rDNA were used to identify 503 colonies from 54 flights to the level of genus. Assemblages of bacteria from sUAS flights in France (TSA) and sUAS flights in Louisiana (R4A) showed more similarity within locations than between locations. Bacteria collected with sUAS on TSA in France and Virginia were significantly different across all levels of classification tested (P < 0.001 for class, order, family, and genus). Principal Coordinates Analysis showed a strong association between the genera Curtobacterium, Pantoea, and Pseudomonas from sUAS flights in Virginia, and Agrococcus, Lysinibacillus, and Paenibacillus from sUAS flights in France. Future work aims to understand the potential origin of the atmospheric microbial assemblages collected with sUAS, and their association with mesoscale atmospheric processes.

Lagrangian coherent structures and inertial particle dynamics

In this work we investigate the dynamics of inertial particles using finite-time Lyapunov exponents (FTLE). In particular, we characterize the attractor and repeller structures underlying preferential concentration of inertial particles in terms of FTLE fields of the underlying carrier fluid. Inertial particles that are heavier than the ambient fluid (aerosols) attract onto ridges of the negative-time fluid FTLE. This negative-time FTLE ridge becomes a repeller for particles that are lighter than the carrier fluid (bubbles). We also examine the inertial FTLE (iFTLE) determined by the trajectories of inertial particles evolved using the Maxey-Riley equations with nonzero Stokes number and density ratio. Finally, we explore the low-pass filtering effect of Stokes number. These ideas are demonstrated on two-dimensional numerical simulations of the unsteady double-gyre flow.

Lagrangian based methods for coherent structure detection

There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

Highways in the sky: scales of atmospheric transport of plant pathogens

Many high-risk plant pathogens are transported over long distances (hundreds of meters to thousands of kilometers) in the atmosphere. The ability to track the movement of these pathogens in the atmosphere is essential for forecasting disease spread and establishing effective quarantine measures. Here, we discuss the scales of atmospheric dispersal of plant pathogens along a transport continuum (pathogen scale, farm scale, regional scale, and continental scale). Growers can use risk information at each of these dispersal scales to assist in making plant disease management decisions, such as the timely application of appropriate pesticides. Regional- and continental-scale atmospheric features known as Lagrangian coherent structures (LCSs) may shuffle plant pathogens along highways in the sky. A promising new method relying on overlapping turbulent back-trajectories of pathogen-laden parcels of air may assist in localizing potential inoculum sources, informing local and/or regional management efforts such as conservation tillage. The emergence of unmanned aircraft systems (UASs, or drones) to sample plant pathogens in the lower atmosphere, coupled with source localization efforts, could aid in mitigating the spread of high-risk plant pathogens.

Front propagation in a chaotic flow field

We investigate numerically the dynamics of a propagating front in the presence of a spatiotemporally chaotic flow field. The flow field is the three-dimensional time-dependent state of spiral defect chaos generated by Rayleigh-Bénard convection in a spatially extended domain. Using large-scale parallel numerical simulations, we simultaneously solve the Boussinesq equations and a reaction-advection-diffusion equation with a Fischer-Kolmogorov-Petrovskii-Piskunov reaction for the transport of the scalar species in a large-aspect-ratio cylindrical domain for experimentally accessible conditions. We explore the front dynamics and geometry in the low-Damköhler-number regime, where the effect of the flow field is significant. Our results show that the chaotic flow field enhances the front propagation when compared with a purely cellular flow field. We quantify this enhancement by computing the spreading rate of the reaction products for a range of parameters. We use our results to quantify the complexity of the three-dimensional front geometry for a range of chaotic flow conditions.

Forecasting sudden changes in environmental pollution patterns

The lack of reliable forecasts for the spread of oceanic and atmospheric contamination hinders the effective protection of the ecosystem, society, and the economy from the fallouts of environmental disasters. The consequences can be dire, as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010. We present a methodology to predict major short-term changes in environmental contamination patterns, such as oil spills in the ocean and ash clouds in the atmosphere. Our approach is based on new mathematical results on the objective (frame-independent) identification of key material surfaces that drive tracer mixing in unsteady, finite-time flow data. Some of these material surfaces, known as Lagrangian coherent structures (LCSs), turn out to admit highly attracting cores that lead to inevitable material instabilities even under future uncertainties or unexpected perturbations to the observed flow. These LCS cores have the potential to forecast imminent shape changes in the contamination pattern, even before the instability builds up and brings large masses of water or air into motion. Exploiting this potential, the LCS-core analysis developed here provides a model-independent forecasting scheme that relies only on already observed or validated flow velocities at the time the prediction is made. We use this methodology to obtain high-precision forecasts of two major instabilities that occurred in the shape of the Deepwater Horizon oil spill. This is achieved using simulated surface currents preceding the prediction times and assuming that the oil behaves as a passive tracer.