There is an internationally accorded standard method of pollen and spores monitoring consisting in the sampling with volumetric Hirst-type samplers. Manual pollen and spores recognition is based on the science of the Palynology which, through the observation of morphological characteristics of the particles, leads to the identification of the taxon. Because this is such a time- and labor-intensive method, the spatial and temporal resolution of the measurements is severely limited. Another drawback of this type of sampler is the inevitable delay between the sampling (that lasts for a week) and the analyses (often undertaken up to 8-9 days after the sampling beginning), which has important implications in terms of pollen and spore forecasts. More accurate predictions would represent a tremendous asset for both the scheduling of patients’ activities and the planning of their medical treatment. Also, knowing with a short delay of time the pollen and spore airborne concentration is of immediate help for the diagnostic, treatment and prevention of the allergies and phytopathologies. To respond to the need for real-time pollen information, numerous partly or fully automated monitoring systems have been developed and investigated over the past decade, with some recently having reached an operational level. A very promising approach takes advantage of the fluorescence features of bioaerosols since a large variety of organic materials like pollen grains can emit their own characteristic fluorescence. For instance, ground-level monitors have been recently developed using fluorescence and elastic light-scattering measurements combined with machine-learning algorithms to identify and quantify airborne pollen concentrations, showing promising results.

 

Figure 1: Different automatic pollen monitors tested in Payerne in parallel with the traditional Hirst trap. Credit: MeteoSwiss

Figure 1: Different automatic pollen monitors tested in Payerne in parallel with the traditional Hirst trap. Credit: MeteoSwiss