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When exposed to convective thunderstorm conditions, pollen grains can rupture and release large numbers of allergenic sub-pollen particles (SPPs). These sub-pollen particles easily enter deep into human lungs, causing an asthmatic response named thunderstorm asthma (TA). Up to now, efforts to numerically predict the airborne SPP process and to forecast the occurrence of TAs are unsatisfactory. To overcome this problem, we have developed a physically-based pollen model (DREAM-POLL) with parameterized formation of airborne SPPs caused by convective atmospheric conditions. We ran the model over the Southern Australian grass fields for 2010 and 2016 pollen seasons when four largest decadal TA epidemics happened in Melbourne. One of these TA events (in November 2016) was the worldwide most extreme one which resulted to nine deaths and hundreds of hospital patient presentations. By executing the model on a day-by-day basis in a hindcast real-time mode we predicted SPP peaks exclusively only when the four major TA outbreaks happened, thus achieving a high forecasting success rate. The proposed modelling system can be easily implemented for other geographical domains and for different pollen types., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Allergic rhinitis affects half a billion people globally, including a fifth of the Australian population. As the foremost outdoor allergen source, ambient grass pollen exposure is likely to be altered by climate change. The AusPollen Partnership aimed to standardize pollen monitoring and examine broad-scale biogeographical and meteorological factors influencing interannual variation in seasonality of grass pollen aerobiology in Australia., Methods: Daily airborne grass and other pollen concentrations in four eastern Australian cities separated by over 1700 km, were simultaneously monitored using Hirst-style samplers following the Australian Interim Pollen and Spore Monitoring Standard and Protocols over four seasons from 2016 to 2020. The grass seasonal pollen integral was determined. Gridded rainfall, temperature, and satellite-derived grassland sources up to 100 km from the monitoring site were analysed., Results: The complexity of grass pollen seasons was related to latitude with multiple major summer-autumn peaks in Brisbane, major spring and minor summer peaks in Sydney and Canberra, and single major spring peaks occurring in Melbourne. The subtropical site of Brisbane showed a higher proportion of grass out of total pollen than more temperate sites. The magnitude of the grass seasonal pollen integral was correlated with pasture greenness, rainfall and number of days over 30 °C, preceding and within the season, up to 100 km radii from monitoring sites., Conclusions: Interannual fluctuations in Australian grass pollen season magnitude are strongly influenced by regional biogeography and both pre- and in-season weather. This first continental scale, Southern Hemisphere standardized aerobiology dataset forms the basis to track shifts in pollen seasonality, biodiversity and impacts on allergic respiratory diseases., (Copyright © 2022. Published by Elsevier Inc.)

The importance of grass pollen to the global burden of allergic respiratory disease is well established but exposure to subtropical and temperate pollens is difficult to discern. Current monitoring of airborne pollen relies on light microscopy, limiting identification of taxa to family level. This informs seasonal fluctuations in pollen aerobiology but restricts analysis of aerobiological composition. We aimed to test the utility of DNA metabarcoding to identify specific taxa contributing to the aerobiome of environmental air samples, using routine pollen and spore monitoring equipment, as well as assess temporal variation of Poaceae pollen across an entire season. Airborne pollen concentrations were determined by light microscopy over two pollen seasons in the subtropical city of Brisbane (27°32'S, 153°00E), Australia. Thirty daily pollen samples were subjected to high throughput sequencing of the plastid rbcL amplicon. Amplicons corresponded to plants observed in the local biogeographical region with up to 3238 different operational taxonomic units (OTU) detected. The aerobiome sequencing data frequently identified pollen to genus levels with significant quantitative differences in aerobiome diversity between the months and seasons detected. Moreover, multiple peaks of Chloridoideae and Panicoideae pollen were evident over the collection period confirming these grasses as the dominant Poaceae pollen source across the season. Targeted high throughput sequencing of routinely collected airborne pollen samples appears to offer utility to track temporal changes in the aerobiome and shifts in pollen exposure. Precise identification of the composition and temporal distributions of airborne pollen is important for tracking biodiversity and for management of allergic respiratory disease., Competing Interests: Declaration of competing interest The authors declare outside the scope of this research, Davies leads the NHMRC AusPollen Partnership Project (GNT 1116107) with matching cash and in kind co-sponsorship from The Australasian Society for Clinical Immunology and Allergy, Asthma Australia, Bureau of Meteorology, Commonwealth Scientific and Industrial Research Organisation, Stallergenes Australia, Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland. Davies is an investigator of current National Foundation for Medical Research Innovation grant with co-sponsorship from Abionic Switzerland. She is an inventor on patents assigned to QUT. Her institute has received Honorarium payments and travel expenses for education sessions and conference presentations from Stallergenes Australia and Wymedical in the last five years., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: In epidemic thunderstorm asthma (ETSA) events a large number of people develop asthma symptoms over a short period of time. This is thought to occur because of a unique combination of high amounts of pollen and certain meteorological conditions. However, the exact cause and mechanism of epidemic thunderstorm asthma remains unclear., Objectives: The objective of this study was to test the hypothesis that convergence lines may be a causative factor in ETSA events, by investigating whether convergence line weather events are associated with the occurrence of high asthma presentations days during the Victorian grass pollen season (October-December)., Methods: A case control method was used. All public hospitals within 75 km of the Melbourne weather radar were included, and data were taken from 2009 to 2017 during the Victorian grass pollen season. Cases hospital days were hospitals with a high number of asthma presentations within a 24-h period, and controls were hospitals with an expected number of asthma presentations. Exposure was defined as geographical proximity of a convergence line to the hospital case or control., Results: Eighty-one case hospital days and 157 hospital day controls were included in the study. The odds of exposure to a convergence line were significantly higher for cases than for controls at all exposure distances. At 4 km, 80 of the 81 cases had been exposed to a convergence line., Conclusion: Convergence lines appear to be a necessary, but not sufficient, element in the cause of epidemic thunderstorm asthma. This is the first study to show a clear link between epidemic thunderstorm asthma and convergence lines., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Seasonal allergic rhinitis (AR), also known as hay fever, is a common respiratory condition brought on by a range of environmental triggers. Previous work has characterised the relationships between community-level AR symptoms collected using mobile apps in two Australian cities, Canberra and Melbourne, and various environmental covariates including pollen. Here, we build on these relationships by assessing the skill of models that provide a next-day forecast of an individual's risk of developing AR and that nowcast ambient grass pollen concentrations using crowd-sourced AR symptoms as a predictor. Categorical grass pollen forecasts (low/moderate/high) were made based on binning mean daily symptom scores by corresponding categories. Models for an individual's risk were constructed by forward variable selection, considering environmental, demographic, behaviour and health-related inputs, with non-linear responses permitted. Proportional-odds logistic regression was then applied with the variables selected, modelling the symptom scores on their original five-point scale. AR symptom-based estimates of today's average grass pollen concentration were more accurate than those provided by two benchmark forecasting methods using various metrics for assessing accuracy. Predictions of an individual's next-day AR symptoms rated on a five-point scale were correct in 36% of cases and within one point on this scale in 82% of cases. Both outcomes were significantly better than chance. This large-scale AR symptoms measurement program shows that crowd-sourced symptom scores can be used to predict the daily average grass pollen concentration, as well as provide a personalised AR forecast., Competing Interests: Declaration of competing interest The authors declare they have no actual or potential competing financial interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Asthma and allergic rhinitis (or hay fever) are ubiquitous, chronic health conditions that seasonally affect a sizeable proportion of the population. Both are commonly triggered or exacerbated by environmental conditions including aeroallergens, air quality and weather. Smartphone technology offers new opportunities to identify environmental drivers by allowing large-scale, real-time collection of day-to-day symptoms. As yet, however, few studies have explored the potential of this technology to provide useful epidemiological data on environment-symptom relationships. Here, we use data from the smartphone app 'AirRater' to examine relationships between asthma and allergic rhinitis symptoms and weather, air quality and pollen loads in Hobart, Tasmania, Australia. We draw on symptom data logged by app users over a three-year period and use time-series analysis to assess the relationship between symptoms and environmental co-variates. Symptoms are associated with particulate matter (IRR 1.06, 95% CI: 1.04-1.08), maximum temperature (IRR 1.28, 95% CI: 1.13-1.44) and pollen taxa including Betula (IRR 1.04, 95% CI: 1.02-1.07), Cupressaceae (IRR 1.02, 95% CI: 1.01-1.04), Myrtaceae (IRR 1.06, 95% CI: 1.02-1.10) and Poaceae (IRR 1.05, 95% CI: 1.01-1.09). The importance of these pollen taxa varies seasonally and more taxa are associated with allergic rhinitis (eye/nose) than asthma (lung) symptoms. Our results are congruent with established epidemiological evidence, while providing important local insights including the association between symptoms and Myrtaceae pollen. We conclude that smartphone-sourced data can be a useful tool in environmental epidemiology., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Allergic Rhinitis (AR) affects over half a billion people worldwide with an estimated prevalence of 1 in 5 individuals in developed countries. Although ambient pollen exposure is a causal factor in AR, the symptom-exposure relationship is typically not studied in the broader community but in small, well-characterised cohorts drawn from clinical populations. To identify relationships between AR symptoms in the community and a range of environmental factors, we used a database containing over 96,000 symptom score reports collected over a 3-year period (2014-2016) through freely available smartphone apps released in two Australian cities, Melbourne and Canberra. Ambient pollen levels and symptom scores were strongly related, with grass pollen explaining most of the symptom variation. Other factors correlated with higher symptom scores included temperature (R > 0.73) and wind speed (R > 0.75). In general, worse symptom scores were reported by younger participants, women, and those who had taken medication for AR in the preceding 24 h. The strength of this relationship varied between the two cities. Smartphone-based symptom surveys offer a cost-effective means of studying real-world risk factors for AR in a broader 'extra-clinical' population., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The effect of height on pollen concentration is not well documented and little is known about the near-ground vertical profile of airborne pollen. This is important as most measuring stations are on roofs, but patient exposure is at ground level. Our study used a big data approach to estimate the near-ground vertical profile of pollen concentrations based on a global study of paired stations located at different heights. We analyzed paired sampling stations located at different heights between 1.5 and 50 m above ground level (AGL). This provided pollen data from 59 Hirst-type volumetric traps from 25 different areas, mainly in Europe, but also covering North America and Australia, resulting in about 2,000,000 daily pollen concentrations analyzed. The daily ratio of the amounts of pollen from different heights per location was used, and the values of the lower station were divided by the higher station. The lower station of paired traps recorded more pollen than the higher trap. However, while the effect of height on pollen concentration was clear, it was also limited (average ratio 1.3, range 0.7-2.2). The standard deviation of the pollen ratio was highly variable when the lower station was located close to the ground level (below 10 m AGL). We show that pollen concentrations measured at >10 m are representative for background near-ground levels., (Copyright © 2019 Elsevier Inc. All rights reserved.)