Effect of air temperature on forecasting the start of the Betula pollen season at two contrasting sites in the south of Europe (1995–2001)

Rodríguez-Rajo, F. J.; Frenguelli, Giuseppe; Jato, M. V.

doi:10.1007/s00484-002-0153-z

Cited by 58 publications

(19 citation statements)

References 27 publications

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“…Regression analysis is used by many authors to predict and determine the effect of weather on pollen seasons (among others, Emberlin et al 1993; Dahl and Strandhede 1996; Adams-Groom et al 2002; Latałowa et al 2002; Rodriguez-Rajo et al 2003). Meteorological conditions are the main factors that modify pollen seasons, but they are not the only ones; this explains the fact why it is so difficult to find a very good fit of a predictive model.…”

Section: Discussionmentioning

confidence: 99%

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The effect of meteorological factors on airborne Betula pollen concentrations in Lublin (Poland)

Piotrowska

Kubik-Komar

2012

Aerobiologia

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The present study investigated the pattern of the birch atmospheric pollen seasons in Lublin in the period 2001–2010. Pollen monitoring was conducted using a Lanzoni VPPS 2000 sampler. The atmospheric pollen seasons were determined with the 98% method. Regression analysis was used to determine correlations between meteorological conditions and the pattern of the birch pollen season. On average, the birch pollen season started on 12 April, ended on 13 May, and lasted 32 days. The peak value and the Seasonal Pollen Index showed the greatest variation in particular years. All the seasons were right-skewed. During the study years, a trend was found towards earlier occurrence of the seasonal peak. Regression equations were developed for the following parameters of the atmospheric pollen season: start, duration, peak value and average pollen concentration during the season. The obtained model fit was at a level of 64–81%. Statistical analysis shows that minimum temperature of February and March and total rainfall in June in the year preceding pollen release have the greatest effect on the birch atmospheric pollen season in Lublin. Low temperatures in February promote the occurrence of high pollen concentrations.

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Section: Discussionmentioning

confidence: 99%

“…The start date of the pollen season is most frequently predicted based on regression analysis (Adams-Groom et al 2002; Rodriguez-Rajo et al 2003). We are convinced that it is also important to predict season duration and severity.…”

Section: Introductionmentioning

confidence: 99%

The effect of meteorological factors on airborne Betula pollen concentrations in Lublin (Poland)

Piotrowska

Kubik-Komar

2012

Aerobiologia

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“…Birch pollen seasons were calculated by the percentage method. Having compared the different percentage methods, like 90 % (Nilsson and Persson 1981; Latałowa et al 2002), 95 % (Andersen 1991; Rodriguez-Rajo et al 2003; Stach et al 2008) and 98 % (Emberlin et al 1993), the combined 98/95 % method was chosen. The number of days with “no” pollen grains before and after the dense pollen occurrence, the value of pollen concentration in the first days with pollen grains, the value of coefficient of variation and also other author suggestions (Jato et al 2006) were taken into consideration.…”

Section: Methodsmentioning

confidence: 99%

“…Most of the published results were obtained using the regression analysis preceded by determining the relationship between pollen season characteristics calculated by different percentage methods (Andersen 1991; Corden et al 2002; Rodriguez-Rajo et al 2003; Stach et al 2008), cumulative methods (Drissen et al 1990; Norris-Hill 1998; Groom-Adams et al 2002; Laadi 2001b) or threshold values (Laadi 2001a; Radišic and Šikoparija 2005) and meteorological conditions (10-day mean values, mean monthly values, heat units, temperature accumulation). The beginning of pollen season, annual total and peak concentration are the most often predicted.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series

Myszkowska

2012

Aerobiologia

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The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman’s correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March–April, while the peak day was predicted using the temperature during the last 10 days of March.

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“…There is no general consensus about the definition of the pollen season, and hence, season dates might differ according to their definition [11]. This notwithstanding, in Spain, the first symptoms are observed over 25 grains/m 3 [33]. Accordingly, this level is selected to define the boundary dates of the main pollen season as the first and the last day that 25 grains/m 3 are observed, corresponding to the start and the end of the peak season, respectively.…”

Section: Experimental Designmentioning

confidence: 99%

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Navares¹,

Aznarte²

2019

Atmosphere

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Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

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Effect of air temperature on forecasting the start of the Betula pollen season at two contrasting sites in the south of Europe (1995–2001)

Cited by 58 publications

References 27 publications

The effect of meteorological factors on airborne Betula pollen concentrations in Lublin (Poland)

The effect of meteorological factors on airborne Betula pollen concentrations in Lublin (Poland)

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

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