Department of Plant Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.
Physical Chemistry Department, Faculty of Science, University of Vigo, 32004, Ourense, Spain.
Int J Biometeorol. 2019 Jun;63(6):735-745. doi: 10.1007/s00484-019-01688-z. Epub 2019 Feb 18.
Pollen forecasting models are a useful tool with which to predict episodes of type I allergenic risk and other environmental or biological processes. Parietaria is a wind-pollinated perennial herb that is responsible for many cases of severe pollinosis due to its high pollen production, the long persistence of the pollen grains in the atmosphere and the abundant presence of allergens in their cytoplasm and walls. The aim of this paper is to develop artificial neural networks (ANNs) to predict airborne Parietaria pollen concentrations in the northwestern part of Spain using a 19-year data set (1999-2017). The results show a significant increase in the length of time Parietaria pollen is in the air, as well as significant increases in the annual Parietaria pollen integral and mean daily maximum pollen value in the year. The Neural models show the ability to forecast airborne Parietaria pollen concentrations 1, 2, and 3 days ahead. A developed model with five input variables used to predict concentrations of airborne Parietaria pollen 1 day ahead shows determination coefficients between 0.618 and 0.652.
花粉预测模型是一种有用的工具，可用于预测 I 型过敏风险和其他环境或生物过程的发作。豚草是一种风媒多年生草本植物，由于其花粉产量高、花粉在空气中的持久时间长以及细胞质和细胞壁中存在大量过敏原，导致许多严重花粉过敏病例。本文旨在使用 19 年的数据集（1999-2017 年）建立人工神经网络（ANN）来预测西班牙西北部空气中的豚草花粉浓度。结果表明，豚草花粉在空气中的停留时间显著延长，以及全年豚草花粉积分和日最大花粉值显著增加。神经模型显示出提前 1、2 和 3 天预测空气中豚草花粉浓度的能力。一个使用五个输入变量开发的模型，用于预测提前 1 天空气中的豚草花粉浓度，其决定系数在 0.618 到 0.652 之间。
