Pollen and Climate: Modeling Pollen Emission and their Fate in the Atmosphere

Abstract

Primary Biological Aerosol Particles (PBAP) are a significant component of aerosols in the atmosphere, and encompass a wide variety of particles including pollen, fungal spores, viruses, and bacteria. Wind-dispersed pollen grains, a type of PBAP, contribute to plant fertilization and gene dispersal and are emitted from a broad variety of vegetation, including trees, grasses and weeds. They can alter climate by interacting with clouds and radiation and trigger respiratory diseases such as allergic rhinitis (hay fever) and asthma. Consequently, they are receiving increasing attention in the fields of climate science and public health. This dissertation focuses on understanding pollen emissions and their atmospheric fate, and explores novel pathways to advance pollen observations and modeling.

Environmental factors such as temperature, precipitation, and wind drive the timing and magnitude of pollen emissions, and their spatial distribution is influenced by vegetation species and land cover. By developing a climate-flexible pollen emission model and driving it with future climate data, we find that warmer end-of-century temperatures will shift the start of spring emissions earlier and delay summer/fall weeds and grasses emissions, leading to an overall lengthening of the season duration. Additionally, increasing atmospheric CO2 in conjunction with climate may increase end-of-century pollen emissions up to 200%. This indicates that climate change will intensify future pollen seasons and increase the likelihood of seasonal allergies.

Emitted pollen grains can also impact regional climate by acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs). Under high humidity conditions, pollen can rupture and form up to 106 smaller sub-pollen particles (SPPs)/grain, which also participate in cloud formation processes, hence intensifying pollen’s climate impacts. To quantify these impacts, we incorporate the pollen emission and rupture processes into the Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations and estimate pollen ice nucleating properties using lab-derived INP parameterizations. The results show that SPPs have a larger effect than intact pollen on a convective system, driving up to a 50% increase in cloud ice and water and potentially extending a convective system.

Current modeling and understanding of pollen emissions and their corresponding effects are largely constrained by observational data. Remote sensing products provide the optical properties of atmospheric aerosols (e.g., aerosol optical depth, aerosol extinction and backscatter, and depolarization of aerosols), and these properties can be used to identify pollen. Here, we combine aerosol optical properties from satellite observations (e.g., Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)), meteorological conditions, and phenological timing with a machine learning model to estimate atmospheric pollen concentration and composition. This technique offers an efficient and powerful approach for mapping pollen concentrations over large spatial regions and long-term scales, improving the ability to estimate pollen distributions over the United States.

In summary, this research explores how pollen interacts with meteorology conditions and develops novel methods for detecting and modeling pollen concentration in the atmosphere. By combining modeling tools and satellite observations, we improve current understanding of the spatial and temporal variability of pollen concentrations and their environmental impacts. The study addresses key gaps in knowledge regarding the effects of climate change on ecosystems, the role of biological aerosols in climate system, and provides a foundation for public health assessments related to respiratory diseases in rural areas where pollen monitoring is limited.