Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Int J Environ Res Public Health. 2022 May 25;19(11):6444. doi: 10.3390/ijerph19116444.
Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported () and () as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93-0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor's weed and grass pollen identification algorithms require further improvement.
季节性花粉是引起过敏性呼吸道疾病的常见原因。在美国，花粉监测是通过手动计数进行的，这种方法既费力又有相当长的时间延迟。在本文中，我们报告了一种新型自动化实时花粉成像传感器在佐治亚州亚特兰大的现场测试结果。我们首先比较了 2020 年在亚特兰大西北部一家过敏诊所与 Rotorod M40 采样器并列放置的自动实时花粉传感器 (APS-300，Pollen Sense LLC) 测量的花粉浓度。然后，在 2021 年花粉季节期间，我们在亚特兰大市中心用两个并列的 APS-300 传感器进行了内部一致性评估。我们还使用 APS-300 测量结果研究了花粉浓度的空间异质性。总体而言，APS-300 和 Rotorod M40 与手动计数报告的每日花粉浓度在花粉高峰期具有很强的相关性(r = 0.85)。APS-300 报告的树木花粉类群较少，导致总花粉计数略有低估。APS-300 和 Rotorod M40 都报告在高峰树木花粉季节期间，()和()为主要花粉类群。APS-300 在夏季和秋季报告的花粉浓度不太准确。两个并列的 APS-300 传感器报告的每日总花粉和分类花粉浓度高度相关(r = 0.93-0.99)。从三个地点报告的花粉浓度在时空上具有显著的异质性，在三个地点达到高峰水平。APS-300 传感器能够提供内部一致的实时花粉浓度，与高峰期的当前黄金标准测量值具有很强的相关性。与手动计数方法相比，全自动传感器具有移动性的显著优势，能够提供实时花粉数据。然而，传感器的杂草和草花粉识别算法需要进一步改进。
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