Evidence map›Paper›PMID 42543389›Full record

ArticleEnvironmental monitoring and assessment2026

From field to sky: measurement and modeling of transgenic switchgrass pollen dispersal in the atmosphere.

Manu Nimmala, Hope A Gruszewski, Regina Hanlon, Landon Bilyeu, Tyler Newton, Jessica Stockdale, Reginald J Millwood, Charles Neal Stewart, Craig W Powers, Shane D Ross and 2 more

Abstract read
In one paragraph

Article in Environmental monitoring and assessment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Manu NimmalaMechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
Hope A GruszewskiSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Regina HanlonSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Landon BilyeuSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Tyler NewtonDepartment of Plant Sciences, University of Tennessee, Knoxville, TN, 37996, USA.
Jessica StockdaleDepartment of Plant Sciences, University of Tennessee, Knoxville, TN, 37996, USA.
Reginald J MillwoodDepartment of Plant Sciences, University of Tennessee, Knoxville, TN, 37996, USA.
Charles Neal StewartDepartment of Plant Sciences, University of Tennessee, Knoxville, TN, 37996, USA. nealstewart@utk.edu.
Craig W PowersSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Shane D RossAerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
Hosein ForoutanCivil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
David G Schmale IiiSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA. dschmale@vt.edu.

Funding

U.S. Department of Agriculture's National Institute of Food and Agriculture 2019-33522-29989
6 · The paper itself

Abstract

Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. "Performer") were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.

Indexed as

Air PollutantsAtmosphereEnvironmental MonitoringPanicumPlants, Genetically ModifiedPollenAir PollutionWindAir PollutantsBioaerosolDrone samplingFluorescenceGenetically Engineered CropPollen dispersalPollen transportSwitchgrassTransgenic

Identifiers

PMID42543389
PMCPMC13429552

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.