Alternaria is a human/animal allergen and plant/animal pathogen. Cereal harvesting emits a large amount of Alternaria spores into the atmosphere. However, estimating the peak spore periods and source areas from large areas is often a challenge because of insufficient observation stations. The purpose of this study was to examine, using remote sensing and an atmospheric transport and dispersion model, the contribution of cereal harvesting to peak Alternaria spore concentrations. Daily Alternaria spores were collected using Hirst-type traps in 12 sites in Europe for the period 2016-2018. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory and dispersion model was integrated with Sentinel-2 satellite imagery, Corine Land Cover 2018 (CLC2018) and Eurostat cereal data 2016 to map the Alternaria spore peaks and source areas in the 12 sites. Ground truth harvest data, collected at Worcester, UK, in 2018, and meteorological data were used to determine any effect of cereal harvesting and weather on peak spore concentrations. The results showed that the Sentinel-2 satellite detected agricultural areas that underwent intensive harvesting and this coincided with a rapid increase of Alternaria spore concentrations. Furthermore, local agricultural areas cultivated with cereals were the main sources of the peak Alternaria spore concentrations in all the study sites. Remote agricultural and non-agricultural sources, to a lesser extent, contributed to the peak spore concentrations at some sites, e.g. Borstel, Leicester and Worcester. Temperature during the harvesting periods (July and August) was found to significantly contribute to the peak spore concentrations. Overall, the study showed that it is possible to use Sentinel-2 satellite data alongside atmospheric transport and dispersion models to estimate periods of peak Alternaria spore concentrations and sources at a continental scale. This approach can be replicated for other bioaerosols that affect human health, agriculture and forestry.

链格孢属是人类/动物过敏原和植物/动物病原体。谷物收获会向大气中排放大量链格孢属孢子。然而，由于观测站不足，从大面积估计孢子高峰期和源区往往是一个挑战。本研究的目的是使用遥感和大气传输和扩散模型来检查谷物收获对链格孢属孢子峰值浓度的贡献。每日链格孢2016-2018 年期间，在欧洲 12 个地点使用赫斯特型捕集器收集孢子。混合单粒子拉格朗日综合轨迹 (HYSPLIT) 后向轨迹和扩散模型与 Sentinel-2 卫星图像、Corine Land Cover 2018 (CLC2018) 和 Eurostat 谷物数据 2016 相结合，以绘制 12 个地点的链格孢子峰值和来源区域. 2018 年在英国伍斯特收集的地面实况收获数据和气象数据用于确定谷物收获和天气对孢子峰值浓度的任何影响。结果表明，Sentinel-2 卫星探测到的农业区域进行了密集收割，这与链格孢菌的迅速增加相吻合。孢子浓度。此外，在所有研究地点，种植谷物的当地农业区是链格孢孢子浓度峰值的主要来源。偏远的农业和非农业来源在较小程度上促成了某些地点的孢子浓度峰值，例如 Borstel、Leicester 和 Worcester。发现收获期间（7 月和 8 月）的温度对孢子浓度峰值有显着影响。总体而言，该研究表明，可以使用 Sentinel-2 卫星数据以及大气传输和扩散模型来估计交链孢的高峰期大陆尺度的孢子浓度和来源。这种方法可以复制用于影响人类健康、农业和林业的其他生物气溶胶。