Review article: how emerging technologies could reshape pollen monitoring for epidemic thunderstorm asthma

Grass pollen constitutes a significant public health concern in Australia, particularly for individuals with seasonal allergic rhinitis and asthma, due to its strong association with epidemic thunderstorm asthma (ETSA) events, which are getting more frequent due to climate change. The combined effects of humidity and lightning during ETSA events cause intact grass pollen to rupture and release small particles that can trigger severe asthma attacks, leading to increased hospital admissions. To help prevent this, researchers began collecting and monitoring local pollen data that contribute to ETSA warning systems. In Australia, expert pollen counters rely on Hirst-type traps, but these machines take 24 h to operate. Some modern machines have been designed to monitor pollen faster, but they are not able to determine the state of the pollen (i.e., intact or ruptured). The advent of artificial intelligence (AI) presents an opportunity to enhance pollen monitoring by accurately identifying not only pollen taxa but also the state of the pollen. Using AI to monitor ruptured pollen could improve the accuracy and efficiency of warning systems, thereby helping to prevent the severe outcomes associated with ETSA events.