Department of Physics (IMARENAB), University of León, Spain.
Department of Biodiversity and Environmental Management, University of León, Spain; Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy.
Sci Total Environ. 2021 May 1;767:145426. doi: 10.1016/j.scitotenv.2021.145426. Epub 2021 Jan 27.
Bioaerosols play a major role in the plant life of ecosystems. In addition, they have a profound impact on human health, since they may cause lung diseases or allergies. The key objective of this study is to assess the below cloud scavenging effect of rainfall on pollen concentration. The analysis is based on a sampling carried out in León, Spain, between 2015 and 2018. The rainfall variables and the pollen concentrations have been obtained with a disdrometer and a volumetric Hirst type spore-trap, respectively. In order to evaluate the scavenging, three parameters have been calculated: scavenging efficiency (through the concentration-weighted average (%ΔC)), the scavenging coefficient (λ) and the percentage of events with a decrease in pollen concentration (%ES) also called events with effective scavenging. 71% of rain events presented an effective scavenging that affected all types of pollen. The %ΔC mean value of total pollen was 24 ± 18% (positive values indicate an effective scavenging) and the types of pollen with the highest values were Castanea and Cupressaceae (71 and 40%, respectively). A linear model (R = 0.94) to estimate the pollen concentration after rain was built with variables such as pollen concentration before rain and other variables from a weather station and a disdrometer. Furthermore, we have shown the possibility of knowing in real time the probable Cupressaceae pollen concentration, from the initial pollen concentration and the physical parameters of rain (such as raindrop size, rain intensity or volume swept by raindrops in their falling path).
生物气溶胶在生态系统的植物生命中起着重要作用。此外，它们对人类健康有深远的影响，因为它们可能导致肺部疾病或过敏。本研究的主要目的是评估降雨对花粉浓度的云下清除作用。该分析基于 2015 年至 2018 年在西班牙莱昂进行的采样。利用雨滴谱仪和体积型赫氏孢子捕捉器分别获得降雨变量和花粉浓度。为了评估清除作用，计算了三个参数：清除效率（通过浓度加权平均值（%ΔC））、清除系数（λ）和花粉浓度降低的事件百分比（%ES），也称为有效清除的事件。71%的降雨事件具有有效清除作用，影响所有类型的花粉。总花粉的%ΔC平均值为 24±18%（正值表示有效清除），且具有最高值的花粉类型为栗属和柏科（分别为 71%和 40%）。建立了一个线性模型（R=0.94），用于根据降雨前的花粉浓度和来自气象站和雨滴谱仪的其他变量来估计雨后的花粉浓度。此外，我们还展示了从初始花粉浓度和雨的物理参数（如雨滴大小、雨强或雨滴在下落路径中被雨滴扫除的体积）实时了解可能的柏科花粉浓度的可能性。
