A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe.

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant's reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating seasonal pollen integrals than those of the GM approach. These are the first published modelled maps using altitude correction and flowering phenology to recover missing pollen information. We created a web page (http://euragweedpollen.gmf.u-szeged.hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions.

A new approach used to explore associations of current Ambrosia pollen levels with current and past meteorological elements.

The paper examines the sensitivity of daily airborne Ambrosia (ragweed) pollen levels of a current pollen season not only on daily values of meteorological variables during this season but also on the past meteorological conditions. The results obtained from a 19-year data set including daily ragweed pollen counts and ten daily meteorological variables are evaluated with special focus on the interactions between the phyto-physiological processes and the meteorological elements. Instead of a Pearson correlation measuring the strength of the linear relationship between two random variables, a generalised correlation that measures every kind of relationship between random vectors was used. These latter correlations between arrays of daily values of the ten meteorological elements and the array of daily ragweed pollen concentrations during the current pollen season were calculated. For the current pollen season, the six most important variables are two temperature variables (mean and minimum temperatures), two humidity variables (dew point depression and rainfall) and two variables characterising the mixing of the air (wind speed and the height of the planetary boundary layer). The six most important meteorological variables before the current pollen season contain four temperature variables (mean, maximum, minimum temperatures and soil temperature) and two variables that characterise large-scale weather patterns (sea level pressure and the height of the planetary boundary layer). Key periods of the past meteorological variables before the current pollen season have been identified. The importance of this kind of analysis is that a knowledge of the past meteorological conditions may contribute to a better prediction of the upcoming pollen season.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications.

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

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

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.

Association of allergic rhinitis or asthma with pollen and chemical pollutants in Szeged, Hungary, 1999-2007.

The effect of biological (pollen) and chemical air pollutants on respiratory hospital admissions for the Szeged region in Southern Hungary is analysed. A 9-year (1999-2007) database includes--besides daily number of respiratory hospital admissions--daily mean concentrations of CO, PM10, NO, NO2, O3 and SO2. Two pollen variables (Ambrosia and total pollen excluding Ambrosia) are also included. The analysis was performed for patients with chronic respiratory complaints (allergic rhinitis or asthma bronchiale) for two age categories (adults and the elderly) of males and females. Factor analysis was performed to clarify the relative importance of the pollutant variables affecting respiratory complaints. Using selected low and high quantiles corresponding to probability distributions of respiratory hospital admissions, averages of two data sets of each air pollutant variable were evaluated. Elements of these data sets were chosen according to whether actual daily patient numbers were below or above their quantile value. A nonparametric regression technique was applied to discriminate between extreme and non-extreme numbers of respiratory admissions using pollen and chemical pollutants as explanatory variables. The strongest correlations between extreme patient numbers and pollutants can be observed during the pollen season of Ambrosia, while the pollen-free period exhibits the weakest relationships. The elderly group with asthma bronchiale is characterised by lower correlations between extreme patient numbers and pollutants compared to adults and allergic rhinitis, respectively. The ratio of the number of correct decisions on the exceedance of a quantile resulted in similar conclusions as those obtained by using multiple correlations.

The effects of the current and past meteorological elements influencing the current pollen concentrations for different taxa.

BACKGROUND: It is an important issue to separate the current and past components of the meteorological parameters influencing the current pollen concentration for different taxa. For this purpose a new statistical procedure, factor analysis with special transformation is introduced. The data set used covers an 11-year period (1997-2007) including daily pollen counts of 19 taxa and 4 climate variables (mean temperature, precipitation amount, global solar flux and relative humidity). RESULT: The taxa examined can be classified into three groups, namely arboreal deciduous (AD), arboreal evergreen (AE) and herbaceous (H) taxa. It was found that a better comparison can be established if the taxa are separated within each group according to the starting month of their pollen season. Within the group of AD taxa, Alnus, Populus and Ulmus are marked by a late summer - early autumn peak of the role of past meteorological elements exceeding the role of the current ones almost all over the pollen-free period. For Juglans, Morus, Platanus and Quercus, the major weights of the current meteorological elements in the spring and early summer show the most characteristic contribution to the pollen production. For AE taxa, the picture is no clear. For H taxa, the curves of Cannabis, Plantago, Rumex and Urtica indicate the most equalized course of weights. Ambrosia, Artemisia and Chenopodiaceae comprise the highest weights of the past weather conditions of all taxa until at least three months before the start of the pollination. Interactions between the phyto-physiological processes and the meteorological elements are evaluated. CONCLUSION: Separation of the weight of the current and past weather conditions for different taxa involves practical importance both for health care and agricultural production.

The effect of different transport modes on urban PM(10) levels in two European cities.

The aim of the study is to identify transport patterns that may have an important influence on PM10 levels in two European cities, namely Szeged in East-Central Europe and Bucharest in Eastern Europe. 4-Day, 6-hourly three-dimensional (3D) backward trajectories arriving at these locations at 1200 GMT are computed using the HYSPLIT model over a 5-year period from 2004 to 2008. A k-means clustering algorithm using the Mahalanobis metric is applied in order to develop trajectory types. Two statistical indices are used to evaluate and compare exceedances of critical daily PM10 levels corresponding to the trajectory clusters. For Bucharest, the major PM10 transport can be clearly associated with air masses arriving from Central and Southern Europe, as well as the Western Mediterranean. Occasional North African dust intrusions over Romania are also found. For Szeged, Southern Europe with North Africa, Central Europe and Eastern Europe with regions over the West Siberian Plain are the most important sources of PM10. The occasional appearance of North-African-origin dust over Hungary is also detected. A statistical procedure is developed in order to separate medium- and long-range PM10 transport for both cities. Considering the 500 m arrival height, long-range transport plays a higher role in the measured PM10 concentration both for non-rainy and rainy days for Bucharest and Szeged, respectively.

Climate sensitivity of allergenic taxa in Central Europe associated with new climate change related forces.

The aim of the study was to analyse trends of the pollen season with its duration, start and end dates, as well as trends of the annual total pollen count and annual peak pollen concentration for the Szeged agglomeration in Southern Hungary. The data set covered an 11-year period (1997-2007) that included eight taxa and seven daily climate variables. Trend analysis was performed on both annual and daily bases. Trend analysis on a daily basis is a new approach that provides information on the annual cycles of the trends. To quantify the strength of the relationship between the annual cycle of the slope of a pollen concentration trend and the annual cycles of the slopes of the climate variable trends, an association measure and a multiple association measure are introduced. Individual taxa were sorted into three categories according to their climate sensitivities. These were compared with two novel climate change-related forces, namely risk potential and expansion potential due to the climate change. The total annual pollen counts indicated significant trends for 4 taxa and 3 of these 4 trends increased on a daily basis. At the same time, significant changes were detected for the pollen season characteristics of three taxa. The association measures performed well when compared to the climate change-related forces. Significant changes in pollen season characteristics were also in accordance with the risk potential and expansion potential due to the climate change. A novel procedure was applied to separate the effects of the past and current weather conditions that influence the current Ambrosia pollen concentrations. The potential effect of land use changes on pollen release of the given taxa was also discussed using the CORINE Land Cover Database.

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

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 (1997-2006) 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 influencing 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 influencing 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).

Association of allergic asthma emergency room visits with the main biological and chemical air pollutants.

Joint effect of biological (pollen) and chemical air pollutants on asthma emergency room (ER) visits was analyzed for Szeged region of Southern Hungary. Our database of a nine-year period (1999-2007) includes daily number of asthma emergency room (ER) visits, and daily mean concentrations of CO, PM(10), NO, NO(2), O(3) and SO(2), furthermore two pollen variables (Ambrosia and total pollen excluding Ambrosia), as well. The analysis was performed for ER visits of asthma bronchiale using two age groups (adults and the elderly) of males and females for three seasons. Factor analysis was performed in order to clarify the relative importance of the pollutant variables affecting asthma ER visits. Asthma ER visits denote notably stronger associations with the pollutants in adult male than in adult female patients both for the pollen season of Ambrosia and the pollen-free season. Furthermore, adults are substantially more sensitive to severe asthma attack than the elderly for the season of total pollen excluding Ambrosia pollen. The joint effect of the chemical and pollen variables is the highest for the asthma ER cases in the pollen season of Ambrosia, basically due to the extra impact of the total pollen excluding Ambrosia pollen and partly due to Ambrosia pollen. A nonparametric regression technique was applied to discriminate between events of ER visit-no ER visit using pollen and chemical pollutants as explaining variables. Based on multiple correlations, the strongest relationships between ER visits and pollutants are observed during the pollen-free season. The elderly group with asthma bronchiale is characterized by weaker relationships between ER visits and pollutants compared to adults. Ratio of the number of correct decisions on the events of ER visit-no ER visit is lowest for the season of total pollen excluding Ambrosia pollen. Otherwise, similar conclusions hold as those received by multiple correlations.

Forecasting ragweed pollen characteristics with nonparametric regression methods over the most polluted areas in Europe.

Nonparametric time-varying regression methods were developed to forecast daily ragweed pollen concentration, and the probability of the exceedance of a given concentration threshold 1 day ahead. Five-day and 10-day predictions of the start and end of the pollen season were also addressed with a nonparametric regression technique combining regression analysis with the method of temperature sum. Our methods were applied to three of the most polluted regions in Europe, namely Lyon (Rhône Valley, France), Legnano (Po River Plain, Italy) and Szeged (Great Plain, Hungary). For a 1-day prediction of both the daily pollen concentration and daily threshold exceedance, the order of these cities from the smallest to largest prediction errors was Legnano, Lyon, Szeged and Legnano, Szeged, Lyon, respectively. The most important predictor for each location was the pollen concentration of previous days. The second main predictor was precipitation for Lyon, and temperature for Legnano and Szeged. Wind speed should be considered for daily concentration at Legnano, and for daily pollen threshold exceedances at Lyon and Szeged. Prediction capabilities compared to the annual cycles for the start and end of the pollen season decreased from west to east. The order of the cities from the lowest to largest errors for the end of the pollen season was Lyon, Legnano, Szeged for both the 5- and 10-day predictions, while for the start of the pollen season the order was Legnano, Lyon, Szeged for 5-day predictions, and Legnano, Szeged, Lyon for 10-day predictions.