science

Fears climate crisis could increase allergy season severity by up to 60%


The climate emergency could increase future allergy season severity by up to 60%, a new pollen forecasting system suggests, while a separate system could predict the severity of grass pollen seasons months in advance.

Such tools could help health professionals prepare for an increase in hay fever cases, or hospital admissions for allergy-related asthma. Seasonal pollen forecasts could also enable some hay fever sufferers to avoid particularly severe seasons by, for example, travelling abroad.

About a quarter of people in Britain experience pollen allergy or hay fever, with the numbers increasing every year. Although daily pollen counts can be predicted based on the next day’s weather forecast and the time of year, making longer-term predictions for specific towns or regions is more difficult – meaning there is no clear guidance on how best to prepare for allergy seasons.

Whereas previous studies have predicted an increase in allergies as a result of the climate crisis, they have usually involved extrapolating observations of increased pollen concentrations in recent decades forwards. “These studies are by nature retrospective and do not predict what will happen 30 years from now,” said Carsten Skjøth, professor in atmospheric sciences at the University of Worcester.

To address these gaps, Skjøth and his colleagues built two separate forecasting systems. The first, which is designed for longer-term assessments such as the impact of the climate emergency or mitigation scenarios, combined observations of how carbon dioxide affects grass growth with data on how the weather affects pollen productivity.

“Here we have a dynamic vegetation model that predicts – and displays on a map – the expected increase in pollen concentrations in a future climate,” Skjøth said.

Assuming a doubling of carbon dioxide levels, this would translate into an increase in future grass pollen allergy season severity of up to 60%, although how quickly this happened would depend on the effectiveness of climate crisis mitigation strategies.

The second forecasting system combined measurements of air temperature, precipitation, and pollen concentrations at 28 pollen monitoring stations in north-west Europe over many years. Tests showed it could predict the severity of the grass pollen season based on pre-seasonal weather patterns, which can affect grass growth and pollen production.

“This is a new type of information that can be used for long-term planning instead of day-to-day planning, and importantly for planning before the season has started,” said Skjøth, whose research was published in Science. “It doesn’t give you a precise number, but it will tell you whether the upcoming season will be particularly severe – a bit like when the Met Office releases its predictions in the autumn about whether we will get a wet or severe winter.

“If we know in advance that we are going to get a very severe pollen season, it could provide information about how much medication shops need to put on their shelves. It can also help individuals to plan when to take annual leave, or even avoid the pollen season by travelling to a different country.”

The new method could be combined with the latest generation of weather forecasting systems, to create more accurate daily forecasts, he said.

Dr Rachel McInnes, science manager of air quality impacts at the Met Office, and a co-author of the paper said: “For sufferers, hay fever can be extremely debilitating and grass pollen is the worst offender. So any insight into the likely severity of the forthcoming pollen season across the UK will be truly welcomed.

“Until now it has been beyond the power of science to produce a long-range forecast, partly because calculating the extent of pollen relies on meteorological as well as land-use factors. But by combining land-use and weather models and monitoring results at a number of study sites, the team has been able to make a breakthrough and provide the tantalising prospect of long-range pollen forecasting.

“Further work is likely to include linking the team’s methods to atmospheric dispersion models, and detailed maps of different individual grass species to provide increasing sophistication.”



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