Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study

This paper considers the feasibility of numerical simulation of large-scale atmospheric transport of allergenic pollen. It is shown that at least small grains, such as birch pollen, can stay in the air for a few days, which leads to a characteristic scale for their transport of ∼103 km. The analytical consideration confirmed the applicability of existing dispersion models to the pollen transport task and provided some reference parameterizations of the key processes, including dry and wet deposition. The results were applied to the Finnish Emergency Dispersion Modelling System (SILAM), which was then used to analyze pollen transport to Finland during spring time in 2002–2004. Solutions of the inverse problems (source apportionment) showed that the main source areas, from which the birch flowering can affect Finnish territory, are the Baltic States, Russia, Germany, Poland, and Sweden—depending on the particular meteorological situation. Actual forecasting of pollen dispersion required a birch forest map of Europe and a unified European model for birch flowering, both of which were nonexistent before this study. A map was compiled from the national forest inventories of Western Europe and satellite images of broadleaf forests. The flowering model was based on the mean climatological dates for the onset of birch forests rather than conditions of any specific year. Utilization of probability forecasting somewhat alleviated the problem, but the development of a European-wide flowering model remains the main obstacle for real-time forecasting of large-scale pollen distribution.

[1]  P. Anttila,et al.  Dry and wet deposition of chernobyl aerosols in Southern Finland , 1987 .

[2]  A. Rantio‐Lehtimäki,et al.  Pollen allergen reports help to understand preseason symptoms , 2002 .

[3]  A. Rantio‐Lehtimäki Short, medium and long range transported airborne particles in viability and antigenicity analyses , 1994 .

[4]  Kaj Andersson,et al.  Mapping Forest in Europe by Combining Earth Observation Data and Forest Statistics , 2003 .

[5]  G. Seufert,et al.  Novel maps for forest tree species in Europe , 2001 .

[6]  B. Vogel,et al.  Numerical modelling of pollen dispersion on the regional scale , 2004 .

[7]  M. Mietus,et al.  Seasonal variations in the atmospheric Betula pollen count in Gdańsk (southern Baltic coast) in relation to meteorological parameters , 2002 .

[8]  J. Corden,et al.  Predicting the start of the birch pollen season at London, Derby and Cardiff, United Kingdom, using a multiple regression model, based on data from 1987 to 1997 , 2002 .

[9]  Tapio Linkosalo,et al.  Mutual regularity of spring phenology of some boreal tree species: predicting with other species and phenological models , 2000 .

[10]  Mikhail Sofiev,et al.  Forward and Inverse Simulations with Finnish Emergency Model Silam , 2004 .

[11]  M. Hjelmroos Long-distance transport ofBetula pollen grains and allergic symptoms , 1992 .

[12]  J. Corden,et al.  A comparison of Betula pollen seasons at two European sites; Derby, United Kingdom and Poznan, Poland (1995–1999). , 2002 .

[13]  Risto Sarvas,et al.  Investigations on the annual cycle of development of forest trees. Active period. , 1972 .

[14]  T. Linkosalo Analyses of the spring phenology of boreal trees and its response to climate change , 2000 .

[15]  M. Sofiev,et al.  An Approach to Simulation of Long-Range Atmospheric Transport of Natural Allergens: An Example of Birch Pollen , 2007 .

[16]  K. Jylhä Empirical scavenging coefficients of radioactive substances released from chernobyl , 1991 .

[17]  F. B. Smith,et al.  The transport and deposition of airborne debris from the Chernobyl nuclear power plant accident with special emphasis on the consequences to the United Kingdom , 1989 .

[18]  D. Aylor,et al.  Settling speed of corn (Zea mays) pollen , 2002 .

[19]  Measured particle bound activity size-distribution, deposition velocity, and activity concentration in rainwater after the chernobyl accident , 1987 .

[20]  C. Porsbjerg,et al.  Airborne pollen in Nuuk, Greenland, and the importance of meteorological parameters , 2003 .

[21]  I. Ilyin,et al.  Co-operative Programme for Monitoring and Evaluation of the Long-Range Transmission of Air Pollutants in Europe EMEP , 2001 .

[22]  Jörg Schaber,et al.  Physiology-based phenology models for forest tree species in Germany , 2003, International journal of biometeorology.

[23]  Y. Waisel,et al.  Annual variations of air-borne pollen in the Coastal Plain of Israel , 1991 .

[24]  Pim Martens,et al.  Phenology and human health: allergic disorders , 2003 .

[25]  An Example of Application of Data Assimilation Technique and Adjoint Modelling to an Inverse Dispersion Problem Based on the ETEX Experiment , 2007 .

[26]  Roberto Canullo,et al.  Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Part VIII, Assessment of Ground Vegetation. Expert Panel on Ground Vegetation Assessment, UN-ECE, ICP-Forests. , 2007 .

[27]  Heikki Hänninen,et al.  Modelling bud dormancy release in trees from cool and temperate regions. , 1990 .

[28]  Thomas Rötzer,et al.  Phenological maps of Europe , 2001 .

[29]  S. Polevova,et al.  Aeropalynological calendar for Moscow 1994 , 1996 .

[30]  Pisarenko,et al.  Development of Forest Resources in the European Part of the Russian Federation , 2000 .

[31]  D. Vokou,et al.  Transport of airborne pollen into the city of Thessaloniki: the effects of wind direction, speed and persistence , 2005, International journal of biometeorology.

[32]  Stein Rune Karlsen,et al.  The start dates of birch pollen seasons in Fennoscandia studied by NOAA AVHRR NDVI data , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[33]  M. Flannigan,et al.  Long-distance transport of pollen into the Arctic , 1999, Nature.