Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen.

ABSTRACT

Allergic diseases, including respiratory conditions of allergic rhinitis (hay fever) and asthma, affect up to 500 million people worldwide. Grass pollen are one major source of aeroallergens globally. Pollen forecast methods are generally site-based and rely on empirical meteorological relationships and/or the use of labour-intensive pollen collection traps that are restricted to sparse sampling locations. The spatial and temporal dynamics of the grass pollen sources themselves, however, have received less attention. Here we utilised a consistent set of MODIS satellite measures of grass cover and seasonal greenness (EVI) over five contrasting urban environments, located in Northern (France) and Southern Hemispheres (Australia), to evaluate their utility for predicting airborne grass pollen concentrations. Strongly seasonal and pronounced pollinating periods, synchronous with satellite measures of grass cover greenness, were found at the higher latitude temperate sites in France (46-50° N. Lat.), with peak pollen activity lagging peak greenness, on average by 2-3weeks. In contrast, the Australian sites (34-38° S. Lat.) displayed pollinating periods that were less synchronous with satellite greenness measures as peak pollen concentrations lagged peak greenness by as much as 4 to 7weeks. The Australian sites exhibited much higher spatial and inter-annual variations compared to the French sites and at the Sydney site, broader and multiple peaks in both pollen concentrations and greenness data coincided with flowering of more diverse grasses including subtropical species. Utilising generalised additive models (GAMs) we found the satellite greenness data of grass cover areas explained 80-90% of airborne grass pollen concentrations across the three French sites (p<0.001) and accounted for 34 to 76% of grass pollen variations over the two sites in Australia (p<0.05). Our results demonstrate the potential of satellite sensing to augment forecast models of grass pollen aerobiology as a tool to reduce the health and socioeconomic burden of pollen-sensitive allergic diseases.