Exposure to pollen and fungal spores can trigger asthma/allergic symptoms and affect health. Rising temperatures from climate change have been associated with earlier seasons and increasing intensity for some pollen, with weaker evidence for fungal spores. It is unclear whether climate change has resulted in changes in the exposure-response function between temperature and pollen/fungal spore concentrations over time. This study examined associations between temperature and pollen/fungal spores in different time periods and assessed potential adaptation using the longest pollen/fungal spore dataset in existence (52 years). Daily concentrations of pollen (birch and grass) and fungal spores (Cladosporium, Alternaria, Sporobolomyces and Tilletiopsis) collected between April and October from Derby (1970-2005) and Leicester (2006-2021), UK, were analysed. Cumulative seasonal concentrations (seasonal integral) and start-of-season were calculated and linked to seasonal mean temperatures (Tmeans) using generalized additive models. Daily concentrations were evaluated against daily Tmean with distributed lagged nonlinear models. Models were adjusted for precipitation, relative humidity, long-term trend and location. Seasonal and daily analyses were respectively stratified into two periods (1970-1995, 1997-2021) and five decades. Warmer seasonal Tmeans were associated with higher seasonal integral for birch, Cladosporium and Alternaria, as well as earlier start-of-season for birch, grass and Cladosporium. There were indications of changing associations with temperature in the recent decades. A warmer January was associated with higher seasonal integral for grass in 1997-2021, but not in 1970-1995. In 2000-2021, daily concentrations of birch pollen tended to remain at higher levels, vs. decrease during 1990s, when Tmean was between 13 and 15 °C. Our study suggests higher temperatures experienced in recent decades are associated with higher overall abundance of some pollen/fungal spores, which may increase future disease burdens of allergies. The changing responses of some pollen to higher temperatures over time may indicate adaptation to increasing temperatures and should be considered in climate change mitigation and adaptation planning.

Asthma and seasonal allergic rhinitis are common conditions affecting health, wellbeing, and work place productivity. Current pollen monitoring requires experienced technicians and has a lag of at least 24 h, which limits usefulness for patient management. We evaluated agreement of daily pollen concentrations from an automated real-time sampler, the Swisens Poleno Jupiter, with a traditional Hirst-type trap using technician counting over a single monitoring season in England. We used regression models to assess the impact of weather covariates on differences. Total pollen showed a strong correlation (r = 0.74) but moderate Intraclass Correlation (ICC = 0.52) between devices, due to concentrations (total and taxa level) being higher for the manual than the automated sampler. For individual pollen taxa, strong correlation and good agreement was observed for Pinus, Alnus and Fraxinus throughout the monitoring period. Strong correlation but low/moderate agreement was observed for Poaceae (grass), Betula, Quercus and Taxus/Cupressus. No correlation or agreement was observed for Corylus and Platanus. Temperature explained most of the variability between samplers. We conclude that daily total pollen can be tracked using an automated Poleno, as can grass within its flowering season, but Poleno recognition algorithms developed for mainland Europe need further refinements for UK species. Improving agreement in quantification is of particular importance given that current allergenic thresholds are based on manual readings.

Pollen and fungal spores are important for human health in both outdoor and indoor environments. They are linked to several respiratory illnesses which range in severity from minor to deadly. Better detection and forecasting of pollen and fungal spores would allow for interventions to be developed that would reduce their risk to human health. The current methodologies available for the detection of pollen and fungal spores are either expensive or time consuming, and often both. This hugely limits their use. For example, the UK Met Office currently only has available 11 regulatory grade sites for pollen monitoring from which their pollen forecast is based upon. This equates to about one regulatory pollen monitoring station per 11 million people in the UK. Similarly, regulatory agencies lack cheap methodologies to detect fungal spores in both outdoor and indoor locations. A cheaper, more agile detection method would much increase the UK's capacity for the detection and forecasting of pollen and fungal spores. The AIPS project has combine several rapidly developing technologies. It brings together a distributed internet-of-things (IoT) sensor arrays in combination with regulatory grade equipment and artificial intelligence (AI) techniques. The IoT sensors measure the size distribution of the small particles that are present within the air. The sources and compositions of these particles are many and varied. Atmospheric particles include bioaerosols that are composed of fragments from the biosphere, including pollen and fundal spores. Finding these bioaerosols within the much larger populations of other atmospheric aerosols, is like finding a needle in a haystack. Fortunately for this project, pollen and fungal spores have well defined sizes that are distinct to the background aerosol which makes detection possible. AI approaches will use machine learning algorithms to classify the pollen and fungal spore species of interest and generate approaches to detect them in real time. The results are then compared to regulatory grade equipment to assess the skill of the low-cost approach. This real time detection will allow for data-driven real-time forecasts of the pollen and spore species of interest.The presentation will provide an overview of the AIPS project, and discuss the efficacy and applicability of the new AI and IoT tools with respect to bioaerosol detection and forecasting needs.