Influence of temperature, rainfall and wind trends on grass pollination in Malaga (western Mediterranean coast)

Recio, Marta; Docampo, Silvia; García-Sánchez, José; Trigo, M.M.; Melgar, Marta; Cabezudo, Baltasar

doi:10.1016/j.agrformet.2010.02.012

Cited by 57 publications

(21 citation statements)

References 29 publications

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“…In the Mediterranean area, the grass pollen season may continue throughout the year, but is most intense in late spring and early summer. In contrast to the results from Sevilla, Recio et al ( 2010 ) found increasing grass pollen counts and more days with numbers over 100 grains/m 3 in Malaga during the springs 1992-2007, which they related to increased temperature and minimum temperatures during early spring, i.e., at seed germination and vegetative growth. Also the study from Sevilla, Spain (Gonzalez Minero et al 1998 ) , the total pollen index increased with increasing rainfall between 1 January and 2 months onward.…”

Section: Flowering Intensity In Grasses and Other Herbaceous Speciescontrasting

confidence: 79%

The Onset, Course and Intensity of the Pollen Season

et al. 2012

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The onset, duration and intensity of the period when pollen is present in the air varies from year to year. Amongst other things, there is an effect upon the quality of life of allergy sufferers. The production and emission of pollens are governed by interacting environmental factors. Any change in these factors may affect the phenology and intensity of the season. Readiness to fl ower in a plant, and the amount of pollen produced, is the result of conditions during an often long period foregoing fl owering. When a plant is ready to fl ower, temporary ambient circumstances e.g., irradiation and humidity, determine the timing of the actual

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Section: Flowering Intensity In Grasses and Other Herbaceous Speciescontrasting

confidence: 79%

The Onset, Course and Intensity of the Pollen Season

et al. 2012

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“…However, actual grasses of habitats can also produce more pollen without habitat shift, as a result of higher rainfall. Similarly, cultivated crops (like wheat or maize) can have higher biomass due to increased rainfall, which means higher pollen production in their cases, as well (Tables I, II; Recio et al, 2010;Sicard et al, 2010). Poaceae show high sensitivity to the amount of available water (Tables I, II).…”

Section: Discussionmentioning

confidence: 99%

“…Bartková-Ščevková, 2003;Rodríguez-Rajo et al, 2005;Štefanič et al, 2005;Kasprzyk, 2008;Recio et al, 2010), while others, based on meteorological data, use different techniques for predicting pollen characteristics (e.g. Galán et al, 2001;Aznarte et al, 2007;García-Mozo et al, 2009).…”

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confidence: 99%

The influence of extreme high and low temperatures and precipitation totals on pollen seasons of Ambrosia, Poaceae and Populus in Szeged, southern Hungary

Makra

Matyasovszky

Páldy

et al. 2012

Grana

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“…Recent climate warming is associated with the modification of the distribution areas of plants producing allergenic pollen (Laaidi et al, 2011;Ziska et al, 2011), furthermore, with an earlier Science of the Total Environment 476-477 (2014) [542][543][544][545][546][547][548][549][550][551][552] onset (Frei, 2008;Rodríguez-Rajo et al, 2011), an earlier end dates (Stach et al, 2007;Recio et al, 2010), a longer pollen season (Stach et al, 2007;Ariano et al, 2010), an increase in the total annual pollen load (Cristofori et al, 2010;Ariano et al, 2010;Laaidi et al, 2011), as well as an increase of patient number sensitised to pollen throughout the year (Ariano et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Csépe

Makra

Voukantsis

et al. 2014

Science of The Total Environment

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• For Szeged, MLP and tree-based models, while for Lyon only MLP performs well for predicting pollen concentration.• When predicting alarm levels, the performance of MLP is the best for both cities.• When forecasting high pollen episodes, the more complex CI methods prove better for both cities.• The selection of the optimal method depends on climate, as a function of geographical location and relief. a b s t r a c t a r t i c l e i n f o Forecasting ragweed pollen concentration is a useful tool for sensitive people in order to prepare in time for high pollen episodes. The aim of the study is to use methods of Computational Intelligence (CI) (Multi-Layer Perceptron, M5P, REPTree, DecisionStump and MLPRegressor) for predicting daily values of Ambrosia pollen concentrations and alarm levels for 1-7 days ahead for Szeged (Hungary) and Lyon (France), respectively. Ten-year daily mean ragweed pollen data (within 1997-2006) are considered for both cities. 10 input variables are used in the models including pollen level or alarm level on the given day, furthermore the serial number of the given day of the year within the pollen season and altogether 8 meteorological variables. The study has novelties as (1) daily alarm thresholds are firstly predicted in the aerobiological literature; (2) data-driven modelling methods including neural networks have never been used in forecasting daily Ambrosia pollen concentration; (3) algorithm J48 has never been used in palynological forecasts; (4) we apply a rarely used technique, namely factor analysis with special transformation, to detect the importance of the influencing variables in defining the pollen levels for 1-7 days ahead. When predicting pollen concentrations, for Szeged Multi-Layer Perceptron models deliver similar results with tree-based models 1 and 2 days ahead; while for Lyon only Multi-Layer Perceptron provides acceptable result. When predicting alarm levels, the performance of Multi-Layer Perceptron is the best for both cities. It is presented that the selection of the optimal method depends on climate, as a function of geographical location and relief. The results show that the more complex CI methods perform well, and their performance is case-specific for ≥2 days forecasting horizon. A determination coefficient of 0.98 (Ambrosia, Szeged, one day and two days ahead) using Multi-Layer Perceptron ranks this model the best one in the literature.

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Influence of temperature, rainfall and wind trends on grass pollination in Malaga (western Mediterranean coast)

Cited by 57 publications

References 29 publications

The Onset, Course and Intensity of the Pollen Season

The Onset, Course and Intensity of the Pollen Season

The influence of extreme high and low temperatures and precipitation totals on pollen seasons of Ambrosia, Poaceae and Populus in Szeged, southern Hungary

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

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