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.

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.

An objective classification system of air mass types for Szeged, Hungary, with special attention to plant pollen levels.

This paper discusses the characteristic air mass types over the Carpathian Basin in relation to plant pollen levels over annual pollination periods. Based on the European Centre for Medium-Range Weather Forecasts dataset, daily sea-level pressure fields analysed at 00 UTC were prepared for each air mass type (cluster) in order to relate sea-level pressure patterns to pollen levels in Szeged, Hungary. The database comprises daily values of 12 meteorological parameters and daily pollen concentrations of 24 species for their pollination periods from 1997 to 2001. Characteristic air mass types were objectively defined via factor analysis and cluster analysis. According to the results, nine air mass types (clusters) were detected for pollination periods of the year corresponding to pollen levels that appear with higher concentration when irradiance is moderate while wind speed is moderate or high. This is the case when an anticyclone prevails in the region west of the Carpathian Basin and when Hungary is under the influence of zonal currents (wind speed is high). The sea level pressure systems associated with low pollen concentrations are mostly similar to those connected to higher pollen concentrations, and arise when wind speed is low or moderate. Low pollen levels occur when an anticyclone prevails in the region west of the Carpathian Basin, as well as when an anticyclone covers the region with Hungary at its centre. Hence, anticyclonic or anticyclonic ridge weather situations seem to be relevant in classifying pollen levels.