Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data
Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data
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DOI：
10.1007/s10453-013-9305-3
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发表时间：
2014-03-01
期刊：
影响因子：
2
通讯作者：
Travaglini, A.
中科院分区：
文献类型：
--
作者：
Brighetti, M. A.;Costa, C.;Travaglini, A.
在短期内预测花粉浓度是有氧生物学中一个重要的课题。文献中提出的预测模型数量众多且越来越复杂，但它们在至少25%的情况下失败，并且并非适用于所有植物物种。本研究为利用气象数据建立花粉浓度超过一定阈值的预测模型提供了可能。在意大利，大约25%的人口患有过敏症，其中80%的病例是由空气中的过敏原引起的，包括农业感兴趣的分类群，如Poaceae。花粉在空气中的弥散是由植物物候期和气象条件共同决定的；花粉的存在根据年、月甚至一天中的时间而变化。环境因子、花粉浓度与花粉症之间存在相关性。采用偏最小二乘判别分析方法，利用14 A（1997-2010）的14个气象变量和花粉变量，预测了3、5、7 d的大气中禾科花粉的存在。结果表明，该方法预测花粉临界浓度的准确度在85.4 ~ 88.0%之间。这项研究有望在使用统计方法方面迈出积极的第一步，在未来可能会有临床应用。
Forecasting pollen concentrations in the short term is a topic of major importance in aerobiology. Forecasting models proposed in the literature are numerous and increasingly complex, but they fail in at least 25 % of cases and are not available for all botanical species. This work makes it possible to build a forecast model from meteorological data for estimating pollen concentration over a certain threshold of Poaceae, an allergenic family. In Italy, about 25 % of the population suffer from allergies, these in 80 % of cases being caused by airborne allergens, including taxa of agricultural interest such as Poaceae. The pollen dispersion in air is determined by both the phenological stage of plants and the meteorological conditions; the pollen presence varies according to the year, month and even the time of the day. There is a correlation between environmental factors, pollen concentrations and pollinosis. A partial least squares discriminant analysis approach was used in order to predict the presence of Poaceae pollen in the atmosphere with a time lag of 3, 5, 7 days, on the basis of a data set of 14 meteorological and pollen variables over a period of 14 years (1997-2010). The results show a high accuracy in predicting pollen critical concentrations, with values ranging from 85.4 to 88.0 %. This study is hopefully a positive first step in the use of a statistical approach that in the next future could have clinical applications.