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首页> 外文期刊>Aerobiologia >Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data
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Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data

机译：多变量统计预测模型通过气象数据预测禾本科花粉临界浓度

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摘要

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.
机译：短期内预测花粉浓度是航空生物学中最重要的话题。文献中提出的预测模型数量众多，而且日趋复杂，但是至少有25％的情况下它们无法使用，并且不适用于所有植物物种。这项工作使从气象数据建立预测模型成为可能，以估计过敏原禾本科的某个阈值上的花粉浓度。在意大利，约25％的人口患有过敏症，其中80％的情况是由空气传播的过敏原引起的，包括农业上感兴趣的类群，如禾本科。花粉在空气中的分散度取决于植物的物候期和气象条件。花粉的存在会根据年，月甚至一天中的时间而变化。环境因素，花粉浓度和花粉症之间存在相关性。基于14个气象和花粉变量的数据集，使用偏最小二乘判别分析方法来预测大气中禾本科花粉的存在，时滞为3、5、7天。 14年（1997-2010）。结果表明，预测花粉临界浓度具有很高的准确性，其范围从85.4％到88.0％。希望这项研究是使用统计学方法的积极的第一步，这种统计学方法将在未来的将来得到临床应用。

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  • 来源
    《Aerobiologia》 |2014年第1期|25-33|共9页
  • 作者

    M. A. Brighetti; C. Costa; P. Menesatti; F. Antonucci; S. Tripodi; A. Travaglini;

  • 作者单位

    Department Biology, University of Rome 'Tor Vergata', Via Della Ricerca Scientifica, 00133 Rome, Italy;

    Consiglio per la Ricerca e la Sperimentazione in Agricoltura (Unita di ricerca per 1'ingegneria agraria), Via Della Pascolare 16, 00015 Monterotondo Scalo, RM, Italy;

    Consiglio per la Ricerca e la Sperimentazione in Agricoltura (Unita di ricerca per 1'ingegneria agraria), Via Della Pascolare 16, 00015 Monterotondo Scalo, RM, Italy;

    Consiglio per la Ricerca e la Sperimentazione in Agricoltura (Unita di ricerca per 1'ingegneria agraria), Via Della Pascolare 16, 00015 Monterotondo Scalo, RM, Italy;

    Pediatric Allergology Unit, Sandro Pertini Hospital, Via Dei Monti Tiburtini 389, 00157 Rome, Italy;

    Department Biology, University of Rome 'Tor Vergata', Via Della Ricerca Scientifica, 00133 Rome, Italy;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Poaceae; Aerobiology; Forecasting models; Partial least squares discriminant analysis;

    机译：禾本科;航空生物学;预测模型;偏最小二乘判别分析;

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