Forecasting airborne<i>Platanus</i>pollen in the Madrid region

Ruiz, Silvia Sabariego; Bustillo, Adela Montserrat Gutiérrez; Morales, Patricia Cervigón; Cuesta, Pedro

doi:10.1080/00173130802218574

Cited by 15 publications

(3 citation statements)

References 22 publications

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“…The commonly used tools for this problem are linear regression models (Fornaciari et al 2002;Vázquez et al 2003;Angosto et al 2005;Smith andEmberlin 2005, 2006;Rodrí-guez-Rajo et al 2005;Ruiz et al 2008) and autoregressive models (ARIMA) (Rodríguez-Rajo et al 2006;Ocana-Peinado et al 2008). Other studies have used more advanced techniques such as neural network or neuro-fuzzy models (Ranzi et al 2003;Sánchez Mesa et al 2005;Aznarte et al 2007).…”

Section: Introductionmentioning

confidence: 97%

“…The influence of the pollen concentration for the 2 previous days (Galán et al 2000) and the pollen concentration for the previous day (Galán et al 2000;Angosto et al 2005) has already been studied. According to Ruiz et al (2008), the pollen concentration for the two previous days is the best predictive variable. The role of the mean seasonal variation of the daily pollen value and the average pollen value of the three preceding days were also found to be important (Makra et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the daily ragweed pollen concentration with previous-day meteorological variables using regression and quantile regression analysis for Szeged, Hungary

Makra

Matyasovszky

2010

Aerobiologia

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Time-varying parametric linear and timevarying nonparametric regression models as well as a time-varying nonparametric median regression model are developed to predict the daily pollen concentration for Szeged in Hungary using previous-day meteorological parameters and the daily pollen concentration. The models are applied to rainy days and non-rainy days, respectively. The most important predictor is the previous-day pollen concentration level, and the only other predictor retained by a stepwise regression procedure is the daily mean global solar flux for rainy days and the daily mean temperature for non-rainy days. Although the variance percentage explained by these two predictors is higher for non-rainy (55.2%) days than for rainy (51.9%) days, the prediction rate is slightly better for rainy than for non-rainy days. Nonparametric regression yields substantially better estimates, especially for rainy days indicating a nonlinear relationship between the predictors and the pollen concentration. The explained variance percentage is 71.4 and 64.6% for rainy and non-rainy days, respectively. Concerning the mean absolute error, the nonparametric median regression provides the best estimate. The quantile regression shows that probability distribution of daily ragweed concentration is much more skewed for non-rainy days, while the more concentrated probability distribution for rainy days exhibits relatively stable ragweed pollen concentrations. The possible lowest limits of concentrations are also calculated. Under highly favorable conditions for peak concentrations, the pollen level reaches at least 350 grains m -3 and 450 grains m -3 for rainy and non-rainy days, respectively. These values again underline the excessive ragweed pollen load over the area of Szeged.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Assessment of the daily ragweed pollen concentration with previous-day meteorological variables using regression and quantile regression analysis for Szeged, Hungary

Makra

Matyasovszky

2010

Aerobiologia

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“…The correlation between earlier pollen dispersal and increasing spring temperature has been widely demonstrated, e.g., in species belonging to the order Fagales in temperate climates (Bortenschlager and Bortenschlager 2005), and in Betula (Emberlin et al 2002;Frei and Gassner 2008;Yli-Panula et al 2009;Veriankaitė et al 2010 and references therein), Alnus (Emberlin et al 2007;Gonzáles-Parrado et al 2006;Rodríguez-Rajo et al 2006), and Castanea (Rodríguez-Rajo et al 2005. A similar advance with increasing winter/spring temperature has been observed also in Platanus (Norris-Hill 1998; Sabariego Ruiz et al 2008). These spring-flowering trees share a similar response to environmental conditions establishing winter dormancy, which is related not only to decreasing temperatures but also to shortened photoperiod, giving rise to autumn bud set (Kozlowski and Pallardy 2002;Allona et al 2008;Caffarra et al 2011).…”

Section: Introductionmentioning

confidence: 55%

Models to predict the start of the airborne pollen season

et al. 2014

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Aerobiological data can be used as indirect but reliable measures of flowering phenology to analyze the response of plant species to ongoing climate changes. The aims of this study are to evaluate the performance of several phenological models for predicting the pollen start of season (PSS) in seven spring-flowering trees (Alnus glutinosa, Acer negundo, Carpinus betulus, Platanus occidentalis, Juglans nigra, Alnus viridis, and Castanea sativa) and in two summer-flowering herbaceous species (Artemisia vulgaris and Ambrosia artemisiifolia) by using a 26-year aerobiological data set collected in Turin (Northern Italy). Data showed a reduced interannual variability of the PSS in the summer-flowering species compared to the spring-flowering ones. Spring warming models with photoperiod limitation performed best for the greater majority of the studied species, while chilling class models were selected only for the early spring flowering species. For Ambrosia and Artemisia, spring warming models were also selected as the best models, indicating that temperature sums are positively related to flowering. However, the poor variance explained by the models suggests that further analyses have to be carried out in order to develop better models for predicting the PSS in these two species. Modeling the pollen season start on a very wide data set provided a new opportunity to highlight the limits of models in elucidating the environmental factors driving the pollen season start when some factors are always fulfilled, as chilling or photoperiod or when the variance is very poor and is not explained by the models.

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Models for forecasting airborne Cupressaceae pollen levels in central Spain

et al. 2011

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The influence of meteorological variables on airborne Cupressaceae pollen levels in central Spain was analyzed, and prediction models based on polynomial and multiple regressions were used to predict pollen counts throughout the pollen season. The Cupressaceae pollen type was selected in view of both its abundance in the atmosphere of the central Iberian Peninsula (particularly from January to March) and its allergenic importance. Sampling was performed uninterruptedly over a 5-year period, using a Hirst volumetric sampler and the sampling method established by the Spanish Aerobiology Network. Temperature displayed the strongest (positive) correlation with Cupressaceae pollen counts. Polynomial and multiple regression analysis showed that maximum temperature was the most influential variable included in prediction models. The prediction equations obtained for the study period were reasonably satisfactory, accounting for 48% and 59% of the variation in airborne pollen levels.

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Forecasting airbornePlatanuspollen in the Madrid region

Cited by 15 publications

References 22 publications

Assessment of the daily ragweed pollen concentration with previous-day meteorological variables using regression and quantile regression analysis for Szeged, Hungary

Assessment of the daily ragweed pollen concentration with previous-day meteorological variables using regression and quantile regression analysis for Szeged, Hungary

Models to predict the start of the airborne pollen season

Models for forecasting airborne Cupressaceae pollen levels in central Spain

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