Bioaerosol Dispersal Across Scales: Regional Patterns, Field Study, and Model Evaluation

Abstract

Bioaerosols--including seeds, pollen, fungal spores, bacteria, and viruses--are fundamental agents connecting atmospheric processes to agriculture, ecosystem function, and human and animal health. This dissertation uses Lagrangian stochastic (LS) models to simulate how these particles travel and deposit across scales relevant for cross-pollination, with applications to many types of biological aerosols. First, we map seasonal and regional patterns of windborne hemp pollen across the United States by running LS models with weather data to simulate day- and night-time dispersal from summer through fall. These simulations identify areas more susceptible to cross-pollination and show how patterns shift across seasons and between day and night. We find regions more vulnerable to cross-pollination, with seasonal and diurnal shifting patterns in dispersal. Next, we work to detect and model genetically modified switchgrass pollen released from a small field in low-wind conditions during three sampling campaigns with a suite of novel samplers. We find that only our highest-volume samplers were able to detect pollen and that reducing the averaging window in the simulations substantially improved emission-rate estimates. Finally, we evaluate the 3D LS models used in this dissertation by comparing them to a high-fidelity model driven by large-eddy simulation (LES) in seven daytime convective boundary layer conditions. The LS models show good accuracy in strongly convective conditions, but they fail in near-neutral conditions due to issues in how they are parameterized rather than in their underlying equations. Together, these results clarify when LS models can effectively substitute for more computationally intensive LES, reveal how sampler design and averaging choices shape what can be extracted from field measurements, and demonstrate the value of weather-aware modeling for cross-pollination risk assessment and broader questions of bioaerosol transport. Collectively, this work strengthens the scientific foundation needed to predict, manage, and mitigate the movement of biological aerosols in an increasingly variable atmosphere.