Rolling out Ireland’s real-time fungal spore monitoring network: co-located Hirst–Poleno observations, training workflows, and a pathway to operational forecasting

Fungal spores are abundant bioaerosols with major impacts on respiratory health and crop protection, yet routine monitoring remains limited because reference methods are labour-intensive and typically only output results after substantial reporting lag. Met Éireann is establishing Ireland’s first national fungal spore monitoring network using co-located Hirst-type volumetric samplers and Swisens Poleno automatic bioaerosol sensors. This work describes the network design rationale, deployment progress to date, and a roadmap from pilot measurements to operational products.

By May 2026, six Poleno instruments (four Jupiter and two Mars) will be operational across an urban–rural transect in Ireland: Dublin, Cork, and Limerick cities (urban exposure and public-health relevance), Mullingar, Oak Park/Carlow and Claremorris/Mayo (rural, agricultural landscapes). Each Poleno is paired with a co-located Hirst sampler to provide a continuous reference dataset for validation and continuity with established aerobiological records. The same instruments, staff workflows, and training approaches are also used for pollen monitoring, enabling year-round multi-taxa surveillance and shared operational learning.

A core objective is to develop a reproducible training and validation pipeline for fungal spore classification from Poleno holographic imagery (and, for Jupiter, fluorescence-assisted measurements). We present an end-to-end workflow for generating labelled datasets: sourcing priority fungi, harvesting spores, controlled aerosolisation into a laboratory-based Poleno device, curation of particle image libraries, and iterative machine-learning model training before deployment to field units. Initial target taxa are selected to be those most readily identifiable to human analysts, allowing rapid iteration on training protocols before moving to all spore types. Once the workflow is robust, species selection will be expanded using a balanced prioritisation framework that weights both human health relevance and agricultural impact equally.

Preliminary outputs from the first operational year emphasise implementation and comparability. We summarise the siting and maintenance challenges encountered during deployment, including placing instruments in populated areas while avoiding local exhaust influences (e.g., rooftop fume hoods), coastal artefacts affecting Hirst tapes (salt deposition and particle overload during high-wind conditions), and biological interference in manual samplers (insects attracted to the adhesive/tape materials). We also outline harmonised quality assurance steps for co-located datasets, including the role of confidence thresholds, and the handling of non-biological interferents.

We will show first-year case studies for the first trained taxa (e.g., Alternaria), comparing daily Hirst counts with high-resolution Poleno output and describing how we calibrate and align the two methods.

Over the next 2–3 years our objectives are to: (1) build 12–24 month co-located Hirst reference datasets at each station; (2) expand the fungal spore training library to cover the most common and impactful taxa in Ireland; (3) produce annual spore calendars, trends, and meteorological drivers; and (4) eventually deliver near-real-time concentration products suitable for online dissemination.