Airborne allergenic pollen impact the health of a great part of the global population. Under climate change conditions, the abundance of airborne pollen has been rising dramatically and so is the effect on sensitized individuals. The first line of allergy management is allergen avoidance, which, to date, is by rule achieved via forecasting of daily pollen concentrations. The aim of this study was to elaborate on 3-hourly predictive models, one of the very few to the best of our knowledge, attempting to forecast pollen concentration based on near-real-time automatic pollen measurements. The study was conducted in Augsburg, Germany, during four years (2016–2019) focusing on Betula and Poaceae pollen, the most abundant and allergenic in temperate climates. ARIMA and dynamic regression models were employed, as well as machine learning techniques, viz. artificial neural networks and neural network autoregression models. Air temperature, relative humidity, precipitation, air pressure, sunshine duration, diffuse radiation, and wind speed were additionally considered for the development of the models. It was found that air temperature and precipitation were the most significant variables for the prediction of airborne pollen concentrations. At such fine temporal resolution, our forecasting models performed well showing their ability to explain most of the variability of pollen concentrations for both taxa. However, predictive power of Betula forecasting model was higher achieving R² up to 0.62, whereas Poaceae up to 0.55. Neural autoregression was superior in forecasting Betula pollen concentrations, whereas, for Poaceae, seasonal ARIMA performed best. The good performance of seasonal ARIMA in describing variability of pollen concentrations of both examined taxa suggests an important role of plants’ phenology in observed pollen abundance. The present study provides novel insight on per-hour forecasts to be used in real-time mobile apps by pollen allergic patients. Despite the huge need for real-time, short-term predictions for everyday clinical practice, extreme weather events, like in the year 2019 in our case, still comprise an obstacle toward highly performing forecasts at such fine timescales, highlighting that there is still a way to go to this direction.

空气传播的致敏花粉影响全球很大一部分人口的健康。在气候变化条件下，空气中花粉的丰度急剧上升，因此对敏感人群的影响也在增加。过敏管理的第一线是避免过敏原，到目前为止，通常是通过预测每日花粉浓度来实现的。这项研究的目的是建立3小时预测模型，这是我们所知极少的模型之一，它试图基于近实时的自动花粉测量来预测花粉浓度。该研究在德国奥格斯堡（Augsburg）进行了为期四年（2016–2019）的研究，重点关注桦木和禾本科花粉，在温带气候中含量最丰富且致敏。使用了ARIMA和动态回归模型，以及机器学习技术。人工神经网络和神经网络自回归模型。模型的开发还考虑了气温，相对湿度，降水，气压，日照时间，扩散辐射和风速。发现空气温度和降水是预测空气中花粉浓度的最重要变量。在如此精细的时间分辨率下，我们的预测模型表现良好，显示出它们能够解释两种类群的大多数花粉浓度变化的能力。但是，Betula预测模型的预测能力达到了更高的水平R ²最高为0.62，而禾本科最高为0.55。神经自回归在预测桦树花粉浓度方面表现优异，而对于禾本科，季节性ARIMA表现最佳。季节ARIMA在描述两个受检类群的花粉浓度变化方面表现出色，这表明植物物候在观察到的花粉丰度中起着重要作用。本研究对花粉过敏患者实时移动应用中使用的每小时预测提供了新颖的见解。尽管对于日常临床实践非常需要实时，短期的预测，但极端天气事件（例如在我们的案例中为2019年）仍然构成了在如此精细的时标上进行高性能预测的障碍，突显了仍然存在朝这个方向前进的方法。