Identification and quantification of giant bioaerosol particles over the Amazon rainforest

Abstract

Eukarya dominate the coarse primary biological aerosol (PBA) above the Amazon rainforest canopy, but their vertical profile and seasonality is currently unknown. In this study, the stratification of coarse and giant PBA >5 µm were analyzed from the canopy to 300 m height at the Amazon Tall Tower Observatory in Brazil during the wet and dry seasons. We show that >2/3 of the coarse PBA were canopy debris, fungal spores commonly found on decaying matter were second most abundant (ranging from 15 to 41%), followed by pollens (up to 5%). The atmospheric roughness layer right above the canopy had the greatest giant PBA abundance. Measurements over 5 years showed an increased abundance of PBA during a low-rainfall period. Giant particles, such as pollen, are reduced at 300 m, suggesting their limited dispersal. These results give insights into the giant PBA emissions of this tropical rainforest, and present a major step in understanding the type of emitted particles and their vertical distribution.

Atmospheric Chemistry Surrogate Modeling With Sparse Identification of Nonlinear Dynamics

“…Following these promising results in short‐term prediction for air pollutants, there have been numerous attempts at machine‐learned surrogate models for full atmospheric chemistry operators (Y. Huang & Seinfeld, 2022; Keller & Evans, 2019; Keller et al., 2017; Kelp et al., 2018, 2020, 2022; Schreck et al., 2022), but all have observed either numerical instability or “mean drift”—where model predictions drift away from reasonable values as the simulation progresses—or have not included experiments which tested for these phenomena. (Efforts to emulate only part of the chemistry operator have been more successful (Sharma et al., 2023). ) These challenges reflect general issues with recurrent time series prediction using neural networks and similar models, including the compounding accumulation of error (Sorjamaa et al., 2007).…”