Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the need of information about pollen concentrations in the air. Initial manual networks, based on pollen trapping coupled to microscope identification and counting, have provided valuable information to authorities, medical doctors and the public for many decades. However, as technology has advanced, new measurement systems have been developed and pollen monitoring is becoming automated. These new instruments use optical and/or imaging techniques to measure particles (not just pollen) in the air, and rely on artificial intelligence to identify them. Automatic bioaerosol monitoring is growing worldwide and brings with it many benefits and challenges. This thesis addresses some of these challenges in order to make the most of the benefits.

In this thesis, we contribute to the development of real-time operational bioaerosol monitoring and start by exploring its main advantage, real-time measurements. We begin by developing a classification algorithm that allows the world's first real-time monitoring of a fungal spore associated with crop diseases and respiratory allergies. We then demonstrate that using both imaging and optical fluorescence measurements together for particle identification leads to a significant improvement in accuracy. In addition, we create a comprehensive database of well-characterised examples of automatic pollen measurements that can be used by artificial intelligence to learn how to identify these particles. Finally, we take advantage of several years of operational real-time measurements to investigate the relationship between airborne pollen concentrations and precipitation washout.

Overall, this thesis covers all aspects of the development of a new monitoring network. It contributes to the success and increases the impact of automatic bioaerosol monitoring with direct implications for public health, but also for agriculture in the monitoring of pathogenic fungi.