Germany Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technical University and Helmholtz Center, Munich 80802, Germany.
Department of Botany, Ecology and Plant Physiology, Agrifood Campus of International Excellence CeiA3, University of Cordoba, Rabanales Campus, Celestino Mutis Building, E-14071 Córdoba, Spain; Andalusian Inter-University Institute for Earth System IISTA, University of Cordoba, Spain.
Sci Total Environ. 2024 Jun 25;931:172913. doi: 10.1016/j.scitotenv.2024.172913. Epub 2024 May 1.
This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.
本研究考察了气象因素和空气污染物对自动花粉监测设备性能的影响,这是在德国慕尼黑举行的 EUMETNET Autopollen COST ADOPT 互比活动的一部分,该活动正值 2021 年花粉季节。该活动提供了一个独特的机会,可以比较当时所有可用的自动监测器,包括一个 Plair Rapid-E、一个 Hund-Wetzlar BAA500、一个 OPC Alphasense、一个 KH-3000 Yamatronics、三个 Swisens Polenos、一个 PollenSense APS、一个 FLIR IBAC2、一个 DMT WIBS-5、一个 Aerotape Sextant,以及平均四个手动 Hirst 陷阱,在相同的环境条件下。该研究旨在阐明气象因素和空气污染如何影响粒子捕获和识别效率。分析结果表明,对于大多数设备,环境条件与花粉浓度之间的相关性具有一致性。这一方面反映了天气和空气中花粉浓度之间存在显著相关性,另一方面反映了设备在测量条件下提供有意义数据的能力。然而,各设备之间的相关性强度存在差异,这反映了设计、算法或使用的传感器的差异。此外,还观察到应用于同一数据集的不同算法会导致不同的浓度输出,这突出了算法设计在这些系统中的作用(监测器+算法)。值得注意的是,没有观察到空气污染物对花粉浓度有显著影响,这表明在这些系统中,任何潜在的影响差异可能需要更高的空气污染浓度或更复杂的相互作用。然而,一些监测器的结果受到特定天气变量的轻微影响。我们的研究结果表明,在城市环境中应用实时设备应侧重于对花粉分类的相关算法。只要污染水平与像慕尼黑这样的欧洲大城市相似,空气污染的影响虽然不应排除,但仍处于次要关注地位。