- Abstract: 

Background: Pollen is naturally emitted and is relevant for health, crop sciences and monitoring climate change, among others. Despite their relevance, pollen is often insufficiently monitored resulting in a lack of data. Thus, spatial modelling of pollen concentrations for unmonitored areas is necessary. The aim of this study was to develop an automatic system for calculating daily pollen concentrations at sites without regular pollen monitoring.

Method: We used data from 14 pollen taxa collected during 2015 at 26 stations distributed across Bavaria, Germany. The proposed system was based on the Kriging interpolation method to spatially model pollen concentrations for unmonitored areas, in combination with regression of environmental parameters. The method also took into account weather effects on daily pollen concentrations.

Results: An automatic system was developed for calculating current pollen concentrations at any location of the county. The results were displayed as daily pollen concentrations per min maps of 1km2 resolution. The models are trained automatically for every day by using the pollen and weather inputs. Automatic inputs will increase the usability of the model. In 50% of the cases, Gaussian Kriging was selected as the optimal model. An R2 of 0.5 is reached in external validation without considering the effect of the weather. An R2 of 0.7 is reached after considering the effect of daily weather parameters.

Conclusions: A fully automatic pollen network (ePIN) was built in Bavaria during 2018 that delivers data on-line without delay. The proposed method allows for a comparably small number of automatic devices per study area, but still providing information on pollen on any location in the study area.

  • Fachübergreifender Arbeitskreis „Bundesweites Pollenmonitoring“. Perspektiven für ein bundesweites Pollenmonitoring in Deutschland. Bundesgesundheitsblatt -Gesundheitsforschung -Gesundheitsschutz. 2019 Apr 23;62(5):652-661. doi: 10.1007/s00103-019-02940-y https://link.springer.com/article/10.1007/s00103-019-02940-y

- Zusammenfassung: Allergene Pollen sind natürliche, biologische luftgetragene Partikel und der Hauptauslöser allergischer Atemwegserkrankungen. Die Messung allergener Pollen ist Voraussetzung für die gesundheitliche Vorsorge und zeigt Veränderungen im Pollenspektrum an, wie sie z.B. im Rahmen des Klimawandels zu erwarten sind.

Das in Deutschland einzige bundesweite Pollenmessnetz wird durch die Stiftung Deutscher Polleninformationsdienst betrieben. Fortbestand und Weiterentwicklung (u.a. Hybridsystem aus manuellen und automatischen Pollenfallen) dieses Messnetzes sind finanziell jedoch nicht gesichert. Im Sommer 2017 formierte sich daher der fachübergreifende Arbeitskreis „Bundesweites Pollenmonitoring“, um sich über diesbezügliche Perspektiven für Deutschland auszutauschen.

Für den Austausch kam der Arbeitskreis zwischen November 2017 und Juni 2018 zu drei Gesprächstagen mit jeweils unterschiedlichen Themenschwerpunkten zusammen. Die internen Protokolle zu den Gesprächstagen bildeten anschließend die Grundlage für die Erstellung des vorliegenden Positionspapiers, mit dem der Arbeitskreis seine Arbeit abschließt.

Aufgrund der Bedeutung allergener Pollen für die menschliche Gesundheit und allergischer Erkrankungen für das Gesundheitssystem spricht sich der Arbeitskreis dafür aus, das bundesweite Pollenmonitoring in den Katalog staatlicher Aufgaben aufzunehmen, die der grundlegenden Versorgung der Bevölkerung mit wesentlichen Gütern und Dienstleistungen dienen (öffentliche Daseinsvorsorge).

Hinsichtlich möglicher Zuständigkeiten im Rahmen der öffentlichen Daseinsvorsorge wurden im Arbeitskreis mehrere Lösungsansätze diskutiert. Unabhängig von der zukünftigen Zuständigkeit kommt der Kooperation von messtechnischen, klinischen und wissenschaftlichen Einrichtungen eine grundsätzliche Bedeutung für die adäquate gesundheitliche Vorsorge zu.

  • Kuneš P, Abraham V, Werchan B, Plesková Z, Fajmon K, Jamrichová E, Role?ek J.

Relative pollen productivity estimates for vegetation reconstruction in central-eastern Europe inferred at local and regional scales. The Holocene. 2019 Jul 15. doi: 10.1177/0959683619862026

- Abstract: Understanding pollen-vegetation relationships is crucial for accurate land-cover and climate reconstructions, yet important parameters for quantifying past vegetation abundance are mostly unknown for large parts of Europe harbouring temperate thermophilous ecosystems. We collected pollen and vegetation data in central-eastern Europe, a region covered by patchy cultural landscapes of high biodiversity to estimate relative pollen productivity (RPP) for important pollen-equivalent taxa. Our study area was situated in the south-western part of the White Carpathians (Czechia–Slovakia borderland), where we collected 40 modern moss pollen samples scattered over 250 km2 and mapped vegetation within 100 m around each pollen site. Additional vegetation data were compiled from Forest management plans, Natura 2000 habitat mapping and floristic inventories over the entire area. We calculated RPP (referenced to Poaceae) by testing two approaches: the extended R-value (ERV) model by estimating relevant source area of pollen and the REVEALS-based productivity using regional scale vegetation estimates. Two models were applied to depict pollen dispersal: Lagrangian stochastic and the Gaussian plume (Prentice) models. We estimated RPP for 16 taxa using the ERV model and an additional nine taxa using REVEALS. Both approaches found Plantago lanceolata-type to be a high pollen producer, Quercus medium-to-high, Asteraceae subf. Cichorioideae, Anthemis-type, Ranunculus acris-type and Rubiaceae low-to-medium and Brassicaceae and Senecio-type as low pollen producers. Results for other, mainly tree taxa, significantly differed in both approaches mainly due to largely uneven representation in both local and regional vegetation. In comparison with other studies, our data demonstrate a high variability in the estimated RPPs which could be influenced by climatic conditions or potentially vegetation structure. We suggest that the accuracy of RPP estimates could be enhanced by comparing modern pollen data with large-scale vegetation data in the future.

  • Werchan M, Werchan B, Bergmann KC

Deutscher Pollenflugkalender 4.0: Update der regionalen Pollenflugkalender 4.0 mit Messdaten von 2011 bis 2016. AllergoJ. 2019 Jul 30;28:16-17. Doi: 10.1007/s15007-019-1887-9

  • Oteros J, Sofiev M, Smith M, Clot B, Damialis A, Prank M, Werchan M, Wachter R, Weber A, Kutzora S, Heinze S, Herr CEW, Menzel A, Bergmann KC, Traidl-Hoffmann C, Schmidt-Weber CB, Buters JTM. Sci Total Environ. 2019 Oct 20;688:1263-1274. doi: 10.1016/j.scitotenv.2019.06.131

Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations.

  • Picornell A,Buters J, Rojo J, Traidl-Hoffmann C, Damialis A, Menzel A, Bergmann KC, Werchan M, Schmidt-Weber C, Oteros J. Sci Total Environ. 2019 Nov 10;690:1299-1309. doi: 10.1016/j.scitotenv.2019.06.485

Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.

-Abstract: Betula pollen is frequently found in the atmosphere of central and northern Europe. Betula pollen are health relevant as they cause severe allergic reactions in the population. We developed models of thermal requirements to predict start, peak and end dates of the Betula main pollen season for Bavaria (Germany). Betula pollen data of one season from 19 locations were used to train the models. Estimated dates were compared with observed dates, and the errors were spatially represented. External validation was carried out with time series datasets of 3 different locations (36years in total). RESULTS: The temperature requirements to detonate the main pollen season proved non-linear. For the start date model (error of 8,75days during external validation), daily mean temperatures above a threshold of 10°C from 28th of February onwards were the most relevant. The peak model (error of 3.58days) takes into account mean daily temperatures accumulated since the first date of the main pollen season in which the daily average temperature exceeded 11°C. The end model (error of 3.75days) takes into account all temperatures accumulated since the start of the main pollen season. CONCLUSION: These models perform predictions that enable the allergic population to better manage their disease. With the established relationship between temperatures and pollen season dates, changes in the phenological behaviour of Betula species due to climate change can be also estimated in future studies by taking into account the different climate scenarios proposed by previous climate change studies.

  • Pfaar O, Karatzas K, Bastl K, Berger U, Buters J, Darsow U, Demoly P, Durham SR, Galán C, Gehrig R, Gerth van Wijk R, Jacobsen L, Katsifarakis N, Klimek L, Saarto A, Sofiev M, Thibaudon M, Werchan B, Bergmann KC.

Pollen season is reflected on symptom load for grass and birch pollen-induced allergic rhinitis in different geographic areas-An EAACI Task Force Report.  Allergy. 2019 Nov 13. doi: 10.1111/all.14111


Background: The effectiveness of allergen immunotherapy (AIT) in seasonal and perennial allergic rhinitis (AR) depends on the definition of pollen exposure intensity or time period. We recently evaluated pollen and symptom data from Germany to examine the new definitions of the European Academy of Allergy and Clinical Immunology (EAACI) on pollen season and peak pollen period start and end. Now, we aim to confirm the feasibility of these definitions to properly mirror symptom loads for grass and birch pollen-induced allergic rhinitis in other European geographical areas such as Austria, Finland and France, and therefore their suitability for AIT and clinical practice support.

Methods: Data from twenty-three pollen monitoring stations from three countries in Europe and for 3 years (2014-2016) were used to investigate the correlation between birch and grass pollen concentrations during the birch and grass pollen season defined via the EAACI criteria, and total nasal symptom and medication scores as reported with the aid of the patient's hay-fever diary (PHD). In addition, we conducted a statistical analysis, together with a graphical investigation, to reveal correlations and dependencies between the studied parameters.

Results: The analysis demonstrated that the definitions of pollen season as well as peak pollen period start and end as proposed by the EAACI are correlated to pollen-induced symptom loads reported by PHD users during birch and grass pollen season. A statistically significant correlation (slightly higher for birch) has been found between the Total Nasal Symptom and Medication Score (TNSMS) and the pollen concentration levels. Moreover, the maximum symptom levels occurred mostly within the peak pollen periods (PPP) following the EAACI criteria.

Conclusions: Based on our analyses, we confirm the validity of the EAACI definitions on pollen season for both birch and grass and for a variety of geographical locations for the four European countries (including Germany from a previous publication) analyzed so far. On this basis, the use of the EAACI definitions is supported in future clinical trials on AIT as well as in daily routine for optimal patient care. Further evaluation of the EAACI criteria in other European regions is recommended.