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.

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.

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.