Airborne ragweed (Ambrosia artemisiifolia L.) pollen concentrations in Croatia, 2002–2004

Peternel, Renata; Čulig, Josip; Hrga, Ivana; Hercog, Predrag

doi:10.1007/s10453-006-9028-9

Cited by 60 publications

(91 citation statements)

References 17 publications

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“…Furthermore, strong associations of Ambrosia, Poaceae and Populus pollen counts with temperature and rainfall have been determined by several authors. Pollen counts were found to increase with temperature and decrease with rainfall for Ambrosia (Bartková-Ščevková, 2003;Makra et al, 2004;Peternel et al, 2006a;Piotrowska & WeryszkoChmielewska, 2006;Kasprzyk, 2008;Ščevková et al, 2010) and for Poaceae (Bartková-Ščevková, 2003;Peternel et al, 2006b;Ščevková et al, 2010). In a more detailed study, Makra et al (2011) found that an association measure is negative between the annual cycles of the daily slopes of Ambrosia and Poaceae pollen concentration trends on the one hand and the annual cycles of the daily slopes of mean temperature trends on the other hand.…”

Section: Discussionmentioning

confidence: 96%

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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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Section: Discussionmentioning

confidence: 96%

“…Counting was done using a standard sampling procedure. Pollen concentrations were expressed as number of pollen grains/m 3 of air (Käpylä & Penttinen, 1981;Peternel et al, 2006a).…”

Section: Pollen Samplingmentioning

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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“…Counting was performed using a standard sampling procedure. Pollen concentrations were expressed as number of pollen grains · m −3 of air (Käpylä and Penttinen, 1981;Peternel et al, 2006). Note that due to the restrictions of the sampling procedure (daily pollen counts are available after a 7-day period, respectively), applicability of the statistical models for operative pollen forecast is limited in time.…”

Section: Pollen Sampling and Countingmentioning

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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“…Hungary is exposed to one of the most severe air pollutions in Europe (5); in addition, airborne pollen levels here are also high. The Carpathian basin, which includes Hungary (Figure 1), is considered the most polluted region with airborne ragweed (Ambrosia artemisiifolia) pollen in Europe (6)(7)(8)(9)(10). Ragweed in Hungary discharges the most pollen of all taxa (11,12).…”

mentioning

confidence: 99%

“…Applying simple statistical analysis, several studies have detected signifi cant positive correlations of daily ragweed pollen counts with daily maximum temperature (20), daily mean temperature (6,7,(21)(22)(23), daily mean wind speed (23), and daily maximum wind speed (22) and signifi cant negative correlations with relative humidity (21)(22)(23) and rainfall (7,23,24). Furthermore, Ziska et al (25) established that the association between higher temperatures and higher ragweed pollen counts was stronger in urban than in rural locations.…”

mentioning

confidence: 99%

Associations between weather conditions and ragweed pollen variations in Szeged, Hungary

Matyasovszky

Makra

Csépe

2012

Archives of Industrial Hygiene and Toxicology

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This study analyses potential associations between day-to-day variations in common ragweed pollen counts in the southern Hungarian district of Szeged and meteorological variables using adapted factor analysis. The database includes ten years (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) worth of data on daily common ragweed pollen ratios (value on the given day per value on the day before) and daily differences (value on the given day minus value on the day before) in eight meteorological variables (mean temperature, minimum temperature, maximum temperature, temperature range, irradiance, relative humidity, wind speed, and rainfall) over the ragweed pollen season. This method is new, as it has only been applied in the economics. In factor analysis it is advisable to combine all the weights of the factors and the resultant variable into one factor indicating the rank of importance of the given explanatory variables in infl uencing the resultant variable, while the remaining factors are uncorrelated with the resultant variable. The procedure shows that wind speed, rainfall, and temperature range are the most important, while minimum temperature and irradiance are the least important meteorological variables infl uencing daily pollen ratios. We found a tendency to stronger associations between the meteorological variables and the pollen variable when the pollen ratio was 1 or below. This is due to the fact that data corresponding to the pollen ratio over 1 come mainly from the prepeak pollen season, while data corresponding to less than 1 are characteristic of the post-peak pollen season (late summer to early autumn).

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Airborne ragweed (Ambrosia artemisiifolia L.) pollen concentrations in Croatia, 2002–2004

Cited by 60 publications

References 17 publications

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

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

Associations between weather conditions and ragweed pollen variations in Szeged, Hungary

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