Airborne Pollen Transport

Abstract

This chapter reviews the present knowledge and previous developments concerning the pollen transport in the atmosphere. Numerous studies are classified according to the spatial scales of the applications, key processes considered, and the methodology involved. Space-wise, local, regional and long-range scales are distinguished. An attempt of systematization is made towards the key processes responsible for the observed patterns: initial dispersion of pollen grains in the nearest vicinity of the sources at micro-scale, transport with the wind, mixing inside the atmospheric boundary layer and dry and wet removal at the regional scale, and the long-range dispersion with synoptic-scale wind, exchange between the boundary layer and free troposphere, roles of dry and wet removal, interactions with chemicals and solar radiation at the large scales.
Atmospheric dispersion modelling can pursue two goals: estimation of concentrations from known source (forward problem), and the source apportionment (inverse problem). Historically, the inverse applications were made first, mainly using the simple trajectory models. The sophisticated integrated systems capable of simulating all main processes of pollen lifecycle have been emerging only during last decade using experience of the atmospheric chemical composition modelling.
Several studies suggest the allergen existence in the atmosphere separately from the pollen grains – as observed in different parts of the world. However, there is no general understanding of the underlying processes, and the phenomenon itself is still debated. Another new area with strongly insufficient knowledge is the interactions of airborne allergens and chemical pollutants.

Full text

127
M. Sofi ev and K-C. Bergmann (eds.), Allergenic Pollen: A Review of the Production,
Release, Distribution and Health Impacts, DOI 10.1007/978-94-007-4881-1_5,
© Springer Science+Business Media Dordrecht 2013
Abstract This chapter reviews the present knowledge and previous developments
concerning the pollen transport in the atmosphere. Numerous studies are classi fi ed
according to the spatial scales of the applications, key processes considered, and the
methodology involved. Space-wise, local, regional and long-range scales are distin-
guished. An attempt of systematization is made towards the key processes respon-
sible for the observed patterns: initial dispersion of pollen grains in the nearest
vicinity of the sources at micro-scale, transport with the wind, mixing inside the
atmospheric boundary layer and dry and wet removal at the regional scale, and the
M. So fi ev (*)
Air Quality Research, Finnish Meteorological Institute , P.O. Box 503,
Erik Palménin Aukio 1 , 00560 Helsinki , Finland
e-mail: mikhail.so fi ev@fmi. fi
J. Belmonte
Botany Unit and Institut de Ciència i Tecnologies Ambientals ,
Universitat Autònoma de Barcelona , Barcelona , Spain
R. Gehrig
Bio- & Umweltmeteorologie, MeteoSwiss , Zurich , Switzerland
R. Izquierdo
Centre de Recerca Ecològica I Aplicacions Foretsals ,
Universitat Autònoma de Barcelona ,
Barcelona , Spain
M. Smith
National Pollen and Aerobiology Research Unit ,
University of Worcester , Worcester , UK
Å. Dahl
Department of Biological and Environmental Sciences,
University of Gothenburg , Gothenburg , Sweden
P. Siljamo
Meteorological Research, Finnish Meteorological Institute , P.O. Box 503,
Erik Palménin Aukio 1 , 00560 Helsinki , Finland
Chapter 5
Airborne Pollen Transport
Mikhail So fi ev , Jordina Belmonte , Regula Gehrig , Rebeca Izquierdo ,
Matt Smith , Åslög Dahl , and Pilvi Siljamo

128 M. So fi ev et al.
long-range dispersion with synoptic-scale wind, exchange between the boundary
layer and free troposphere, roles of dry and wet removal, interactions with chemi-
cals and solar radiation at the large scales.
Atmospheric dispersion modelling can pursue two goals: estimation of concen-
trations from known source (forward problem), and the source apportionment
(inverse problem). Historically, the inverse applications were made fi rst, mainly
using the simple trajectory models. The sophisticated integrated systems capable of
simulating all main processes of pollen lifecycle have been emerging only during
last decade using experience of the atmospheric chemical composition modelling.
Several studies suggest the allergen existence in the atmosphere separately from
the pollen grains – as observed in different parts of the world. However, there is no
general understanding of the underlying processes, and the phenomenon itself is
still debated. Another new area with strongly insuf fi cient knowledge is the interac-
tions of airborne allergens and chemical pollutants.
Keywords Airborne pollen • Atmospheric pollen transport • Dispersion modelling
5.1 Introduction
The atmospheric pathway is the fastest and the simplest way for biological agents to
spread over terrestrial ecosystems. Many organisms can signi fi cantly increase the
ef fi ciency of their movements by taking advantage of air currents (Isard et al. 2005 ) .
Biota that is present in the atmosphere ranges in size from very small (viruses, bacteria,
pollen and spores) to quite large (seeds, aphids, butter fl ies and moths, songbirds, and
waterfowl) (Gage et al. 1999 ; Westbrook and Isard 1999 ) . The link between these
biological systems and the atmosphere is the key to understanding the population
dynamics of and diseases spread by, aerobiota. Within this chapter, the emphasis is
placed on identifying biologically and medically relevant temporal and spatial scales
of atmospheric motions and meteorological parameters, which help control the abun-
dance and distribution of airborne biota, such as pollen and other aeroallergens.
Biologically-relevant dispersion of bioaerosols affects the structure of ecosystems,
since pollen is responsible for gene fl ow (Ellstrand 1992 ; Ennos 1994 ; Burczyck
et al. 2004 ; Belmonte et al. 2008 ) , and it contributes in determining the spatial
distribution of plant species (Ellstrand 1992 ; Smouse et al. 2001 ; Sharma and
Khanduri 2007 ; Schmidt-Lebuhn et al. 2007 ; Belmonte et al. 2008 ) . Therefore,
understanding the pollen gene dispersal is instrumental for the interpretation of the
biogeographic range of plants and plant conservation issues. A review by Di-Giovanni
and Kevan ( 1991 ) on the factors affecting the pollen dispersion in natural habitats
can be recommended for the corresponding processes.
Apart from gene fl ow, the transport of bioaerosols causes concern because of its
potential to distribute pathogens and allergens, which can affect human health, agri-
culture, and farming (Belmonte et al. 2000 ; Grif fi n et al. 2001a, b ; Taylor 2002 ;
Brown and Hovmoller 2002 ; Shinn et al. 2003 ; Garrison et al. 2003 ; Wu et al. 2004 ;
Kellogg et al. 2004 ; Grif fi n 2007 ; Paz and Broza 2007 ; Polymenakou et al. 2008 ) .

129
5 Airborne Pollen Transport
The pollen records from aerobiological monitoring sites have traditionally been
interpreted as if the grains always originate from the local environment. Consequently,
pollen forecasting tools have been designed by taking into account only local mete-
orological variables and phenological observations in the neighbourhood. This view
is currently changing to acknowledge much broader bioaerosol movements, based
on increasing evidence of pollen and spore transport at much greater distances,
including continental (Belmonte et al. 2000, 2008 ; So fi ev et al. 2006a ; Siljamo et al.
2007, 2008b, c ;Skjøth et al. 2009 ) and intercontinental scales (Prospero et al. 2005 ;
Kellogg and Grif fi n 2006 ; Rousseau et al. 2008 ) .
5.1.1 Basic Terms
5.1.1.1 Spatial and Temporal Scales
Pollen-related processes in the atmosphere take place at a wide range of scales.
For the needs of the current chapter the following terms will be used following the
classical de fi nitions after Seinfeld and Pandis ( 2006 ) – Fig. 5.1 :
Processes at the micro-scale are connected with pollen release and take place •
within a few metres from the plants
Local-scale processes include initial dispersion of grains that happen within the •
nearest kilometre from the source
100 yr
10 yr
1 yr
1 day
1 h
100
1 s
1 m 10 m
OH
100 m 1 km 10 km 100 km 1000 km 10,000 km
Pollen ranges
Moderately Long
Lived Speices CO
Long-lived
Species CFCS
Aerosols
DMS
Inter-hemispheric
Mixing Time
Inter-hemispheric
Mixing Time
Boundary Layer
Mixing Time
Short-lived
SpeciesCH3O2
CH3Br
CH3CCl3
C3H6
C5H8
HO2
SO2
CH4
Trop O3
H2O2
NO3
N2O
NOX
Microscale
Urban or
Local scale
Regional or
Mesoscale
Synoptic to
Global scale
Temporal Scale
Fig. 5.1 Spatial and temporal scales of variability of the atmospheric constituents (Modi fi ed from
Seinfeld and Pandis
2006 )

130 M. So fi ev et al.
Regional and meso-scales are considered as synonyms and cover the processes •
responsible for the dispersion and removal of the bulk of pollen grains – at distances
of up to a hundred kilometres.
The hierarchy of the scales related to long-range transport consists of synoptic, •
continental, and global scales, which include processes of up to 1,000–2,000 km,
up to 5,000 km, and over 5,000 km, respectively.
Connections between the above spatial and corresponding temporal scales are
shown in Fig.
5.1 .
Distinction between the scales is not always unequivocal and speci fi c borders
can vary depending on the speci fi c application and criteria used. For the purposes of
this review, we will also consider the cases as “local” or “regional” if the conditions
at both source and the receptor points can be (roughly) described by a single or few
observation stations. In the long-range transport case, such a description always
requires many stations distributed over the area and atmospheric modelling as a way
to evaluate the transport conditions.
5.1.1.2 Pollen Life Time in the Atmosphere
Atmospheric lifetime is the key parameter for each tracer, which has a direct
connection to its spatial scale of distribution and temporal scale of its variations
(Seinfeld and Pandis 2006 ) . Several example species are marked in Fig. 5.1 follow-
ing their characteristic lifetimes. With pollen and allergens, the situation is more
complicated. Indeed, the pollen atmospheric lifetime of a few days (due to substan-
tial gravitational sedimentation) de fi nes it as a local-scale pollutant with some minor
connection to regional scales (So fi ev et al. 2006a ) . However, as discussed below,
in many cases the released amount is so large that the medical impact can be
substantial at much larger distances – up to continental scales. This ambiguity is
re fl ected in Fig. 5.1 where the pollen-related processes are delineated by a separate
rectangular.
5.1.1.3 Aerobiological Phases of Pollen and Other Biogenic Aerosols
in the Atmosphere
A schematic model for passively transported biological substances includes presen-
tation of the biological material, its release in the atmosphere, dispersion, transfor-
mation, deposition, and impact (Fig. 5.2 , extended from Isard and Gage 2001 ) .
Sometimes, after deposition, the grains can be subjected to re fl oating processes,
thus being resuspended back into the air. In this chapter we will consider only
release, transport, transformation, and deposition phases as the most important and
studied so far.

131
5 Airborne Pollen Transport
5.1.1.4 Types of Biogenic Aerosols
The most-studied biogenic aerosol is evidently the pollen grain. It transfers the male
gametophyte to the female reproductive organs, which is termed as pollination process.
Once pollen grains are deposited on the reproductive female organs the recognition
process starts via protein exchange to allow the germination and liberation of the
male gametes for fecundation. Several proteins inside the grain are considered to be
allergens since, in some occasions, the human immune system can react on their
presence by triggering allergenic reactions. Direct studies of these proteins in connec-
tion to atmospheric transport are very limited, therefore the main attention in this
chapter will be given to pollen grains. The allergen-related studies will be included
wherever possible. Other types of biogenic particulates, such as spores, seeds, etc.,
are beyond the scope of the review.
5.1.1.5 Main Features of Pollen and Allergen as Atmospheric Tracers
A pollen grain, from the point of view of the atmospheric transport, is a very large
but comparatively light aerosol. Aerodynamic features of most of the pollen grains
(except for the largest ones with diameter over 100 m m) allow the classical model
considerations when the particle is considered to be embedded into all atmospheric
fl ows including the small-scale turbulent eddies (So fi ev et al. 2006a ) . The life span
of the particles in the atmosphere strongly depends on their deposition intensity, i.e.
Ecological and
environmental factors
Health impact
Deposition
Sedimentation Scavenging
Transport
Release
Transformation,
Interaction with
chemicals
Presentation Health impact
Deposition
Sedimentation Scavenging
Transport
Transformation,
Interaction with
chemicals
Fig. 5.2 Phases of aerobiological processes related to transport of chemically inactive biogenic
aerosol (Extended from Isard and Gage
2001 )

132 M. So fi ev et al.
the sum of dry and wet deposition. For coarse particles, the gravitational settling is
the most important deposition pathway (Seinfeld and Pandis 2006 ) , which makes the
sedimentation velocity the primary parameter deciding the atmospheric life time of
pollen. For birch, it is about 1.2 cm sec
−1 (So fi ev et al. 2006a ) , which corresponds to
the lifetime ranging from a few tens of hours up to a couple of days depending on
the vertical transport and mixing.
Pollen is not soluble in water but can easily be scavenged via impaction (in case
of sub-cloud scavenging). Processes occurring inside the clouds are very poorly
studied but one can expect that the grains can be embedded into the forming droplets
and scavenged together with them.
A pollen allergen is usually a sub-micron aerosol with high water solubility
(Vrtala et al. 1993 ; Taylor et al. 2004 ) . It can therefore be scavenged with precipita-
tion but has no substantial dry deposition velocity (see Chap. 19 of Seinfeld and
Pandis 2006 ) . As a result, one can expect the allergen to stay in air much longer than
pollen – days and, possibly, weeks in the case of no precipitation.
Among the transformation processes occurring during the atmospheric transport,
the most frequent one is a loss and gain of water depending on the air humidity,
an ability of the pollen grain called harmomegathy (Wodehouse 1935 ) . Sometimes
this situation provokes the pollen grain rupture and allergen release. Secondly, it is
a loss of viability due to temperature and UV radiation. Thirdly, a chemical damage
of the grains by strong oxidants can take place in polluted environments. Finally,
the release of the pollen content due to the grain rupture or (pseudo-) germination
has been con fi rmed by several studies (Pacini et al. 2006 ; Motta et al. 2006 ; Taylor et al.
2002, 2004, 2006 ) .
5.2 Meteorological Drivers of Pollen Dispersion
in the Atmosphere
The pollen lifecycle in the atmosphere starts from the release from the anthers –
arguably the smallest-scale relevant atmospheric process. In many anemophilous
trees, the anther’s burst is a result of dehydration due to high temperature, solar
exposure, low humidity and moderate wind (see Helbig et al. 2004 ; Linskens and
Cresti 2000 and references therein). In other plants, fl oral parts, such as the fi laments
in grasses actively push the anthers into an exposed position, and anther’s dehis-
cence may be a result of passive desiccation or active reabsorption. Reabsorption
enables the anther opening at any time of day, whereas evaporation may occur only
during dry hours. The Asteraceae, which includes the genera of Artemisia and
Ambrosia , is characterized by the secondary pollen presentation, which in this case
means that the pollen mass is pushed to the top of the fl ower where it is exposed to
atmospheric stress (von Wahl and Puls 1989 ; Kazlauskas et al. 2006 ) . In this case,
the pollen detachment from the capitulum can take place later than the anther opening.
In particular, it could take place at any time during the day when the wind and
turbulence are strong enough to pick up the pollen from the fl ower. In the Urticaceae,
pollen emission is explosive and is caused by the spring-like release of the anthers

133
5 Airborne Pollen Transport
caused by dehydration. A more detailed account of pollen release can be found in
the Chap. 3 of this book and dedicated reviews, such as (Pacini and Hesse 2004 ).
Once the pollen grains are released into the atmosphere, the mechanical force of
air fl ow, either induced by mean wind or by turbulent eddies, becomes the only
process that keeps the grains in the air. The initial mixing and uplift driven by the
turbulent motions largely determines the fraction of the released grains that will
come to larger-scale dispersion (e.g. Gregory
1961 ) . At the meso-scale, the mean
wind becomes the main transport force while the turbulent mixing keeps the grains
aloft and further redistributes them along the vertical – inside and beyond the atmo-
spheric boundary layer. At regional and large scales, both horizontal and vertical
mean-wind components are responsible for the transport on-par with turbulent mixing.
At all scales, the processes of dry deposition, by impact or gravitational sedimenta-
tion, as well as wet deposition by scavenging with precipitation, are responsible for
the removal of grains from the atmosphere. However, their importance varies being
the highest at large scales.
5.3 Micro- and Local Scales
Micro- and local-scale transport of pollen grains includes the initial dispersion of
pollen grains from the anthers and the transport over the fi rst kilometre(s) from the
source. An outlook of the main factors affecting the dispersion can be found in
(Di-Giovanni and Kevan 1991 ) . The knowledge about micro- to local scale pollen
transport is based on fi eld experiments with pollen release from a well-de fi ned
source. The dispersion of the pollen plume at these scales can be modelled with
various types of dispersion models (Lagrangian, Gaussian plume model, large eddy
simulation, or quasi-mechanistic models – e.g. Aylor et al. 2006 ; Arritt et al. 2007 ;
Chamecki et al. 2009 ; Jarosz et al. 2003, 2004 ; Klein et al. 2003 ; Kuparinen 2006 ;
Kuparinen et al. 2007 ; Schueler and Schlünzen 2006 ) .
Experiments studying the dispersion and deposition of pollen were already made
in the 1960s and 1970s by Raynor, Ogden and Hayes. For ragweed pollen, Raynor
et al. ( 1970 ) used a set of point and area sources surrounded by four to fi ve rings
of samplers located concentrically from 1 to 69 m from the centre of the plot.
The samples were taken at four heights from 0.5 to 4.6 m. It was found that about
half of the ragweed pollen grains reaching the edge of the source are still airborne
some 55–65 m further away. Extrapolation towards greater distances indicates that
about 1% of the pollen grains remain airborne at 1 km. Raynor et al. ( 1970 ) state
that since pollen clearly disperses similarly to other (small) particles, calculation of
pollen dispersion by use of existing diffusion models should be practical. Further
studies for Timothy grass and maize pollen were published by Raynor et al. ( 1972a , b ) .
Particular interest to the pollen transport was caused by the gene fl ow problem at
local scales. Gleaves ( 1973 ) has developed a local-scale empirical gene- fl ow model
and performed a series of sensitivity studies of the exchange ef fi ciency of the genetic
material depending on the mutual position of the plants, transport conditions, etc.
A motivation for the study was that, despite “pollen can be blown hundreds of miles,

134 M. So fi ev et al.
the bulk of cross-pollination occurs over very short distances”. Therefore, the analysis
has been performed for the transport range of a few tens of metres. Govindaraju
( 1988 ) has statistically demonstrated that the pollination mechanism directly
in fl uences the gene fl ow and showed that the dependence is the strongest for the
wind-pollinating plants, being much weaker for animal- and self-pollinating species.
Quantitative estimates for the tree species can be found in Govindaraju (
1989 ) .
Several recent fi eld experiments have been designed to estimate the three-dimensional
plume shape of airborne pollen grains, as well as the amount deposited. The out-
come was also used for evaluation of the dispersion models. With numerous samplers
placed around the source, instrument towers, and occasionally even remote-piloted
planes and aircrafts, pollen concentrations up to several tens of meters above the
ground were measured. The experiments for maize were made by Jarosz et al. ( 2003,
2004 ) , Aylor et al. ( 2003 , 2006), Boehm et al. ( 2008 ) , Klein et al. ( 2003 ) , Arritt
et al. ( 2007 ) ; for ragweed pollen by Chamecki et al. ( 2009 ) ; and for oilseed rape
pollen by McCartney and Lacey ( 1991 ).
The introduction of genetically modi fi ed organisms (GMO) in agriculture
favoured the research of pollen transport processes on local scales for establishing a
basis for assessment of the gene fl ow from these plants to other crops or to natural
vegetation. An overview of these efforts is given by the European Environment
Agency ( 2002 ) . This report considers the signi fi cance of pollen-mediated gene fl ow
from six major crops: oilseed rape, sugar beet, potatoes, maize, wheat and barley.
The EEA ( 2002 ) report con fi rmed that the majority of airborne pollen is depos-
ited at very short distance from the pollen source, although out-crossing of maize
pollen has been recorded at up to 800 m from the source and, in extreme cases, there
is the evidence of wind transfer of oilseed rape pollen up to at least 1.5 km (Timmons
et al. 1995 ) . Studies of maize pollen dispersal from small plots (e.g. Raynor et al.
1972b ; Jarosz et al. 2003 ) have suggested that there is relatively little impact of pollen
beyond a few hundred meters from the source. However, the impact of pollen may
extend to much greater distances because the dispersal distribution has a long
extending tail (Aylor et al. 2003 ) . The tail is expected to be much more evident for
large source areas (Aylor et al. 2006 ) . Brunet et al. ( 2004 ) used measurements taken
by aircraft and found the viable pollen throughout the entire depth of the atmo-
spheric boundary layer. Their results imply that updrafts due to turbulent eddies in
the boundary layer overcome the terminal fall velocity of maize pollen grains and
transport pollen to considerable heights, so that it could travel long distances before
settling (Arritt et al. 2007 ).
One of the key parameters originating from the local-scale studies is the estima-
tion of the pollen source strength, as seen at various scales, and its dependence on
the plant type and atmospheric conditions.
A detailed micro-scale experiment by Laursen et al. (
2007 ) included 15-min
measurements of the key meteorological parameters accompanied by the pollen
concentration monitoring in the nearest vicinity of the plants. An empirical
multi-linear regression fi t was obtained for concentrations of Artemisia grains as a
function of temperature, humidity, and wind speed. As was shown, wind and
temperature are synergetic, i.e. higher wind and temperature both promote pollen

135
5 Airborne Pollen Transport
release. Interestingly, relative humidity was not included into the equation (although
measured during the experiment).
Aylor et al. ( 2006 ) estimated the maize pollen release rates 10–700 grains per
square meter per second depending on weather factors and the day-to-day and diurnal
variation of pollen production. In many cases pollen is not released steadily but
reacts on wind gusts that “shake” the anthers. Grasses are known to disperse pollen
at a certain time of the day, which varies between the species and environmental
conditions. Some grass species exhibit a bimodal diurnal pattern (Davidson
1941 ;
Subba Reddi et al. 1988 ) .
Ambrosia pollen is initially shed from the staminate fl owers in large pollen
clumps containing hundreds of grains. Turbulent wind stress breaks these pollen
clumps quickly into smaller ones, so that at regional scales Ambrosia pollen is
released as a largely homogeneous plume. Modelling with large eddy simulation
by Chamecki et al. ( 2009 ) provided an estimate of the total pollen emission of a
ragweed fi eld and its deposition partition inside the fi eld and downwind. About 60%
of pollen is deposited inside the fi eld, 14% was deposited downwind inside the
simulation domain, which is about 1,000 m long, while 26% of the total pollen
remained airborne and left the domain.
Bricchi et al. ( 2000 ) found that the highest Platanus pollen deposition is recorded
close to the plants and decreases quite quickly at a distance greater than 400 m. More
than 88% of the pollen is deposited within a range of 2,750 m from the source.
A measuring experiment of local Betula pollen dispersion was presented by
Michel et al. ( 2010 ) , aiming at 3-D representation of the plume originating from an
isolated birch stand.
A study by Spieksma et al. ( 2000 ) about the in fl uence of nearby stands of Artemisia
on pollen concentrations at street-level versus roof-top-level shows that the street-level
concentrations were in average 4.8 times higher than the ones at 25 m on the roof.
5.4 Regional Scale
Regional-scale pollen dispersion over distances of about a hundred kilometres
(or slightly more) poses different challenges in comparison with local scales. First
of all, only a fraction of emitted pollen reaches these scales – so-called “regional
component” (Faegri et al. 1989 ) or “escape fraction” (Gregory 1961 ) of the emission.
Secondly, the observations at the receptor location are not fully representative for
the pollen release area. They can, however, provide rough estimates for the transport
conditions between the source and receptor points.
This connection of regional-scale meteorology and climatology with the pollen
dispersion has been known for decades and is used in practically all studies. For exam-
ple, the relation between measurements of Castanea sativa pollen concentration and
the meteorological variables registered in different locations of Po Valley (Italy) were
studied by Tampieri et al. (
1977 ) . Peeters and Zoller ( 1988 ) and Gehrig and Peeters
( 2000 ) connected the Betula and Castanea pollen distribution in Switzerland with
such factors as altitude, prevailing meteorological patterns during spring time, etc.

136 M. So fi ev et al.
The situations of strong regional pollen dispersion and the conditions favouring
the regional-scale transport of Juniperus ashei pollen were described by Van de
Water et al. ( 2003 ) and Van de Water and Levetin ( 2001 ) . The authors qualitatively
estimated the conditions favouring or precluding the released pollen to be trans-
ported regionally. A trajectory model HYSPLIT and fi ve observation stations were
used to obtain a complex pattern of the regional pollen distribution and to evaluate
the allergenic threat in the region of Tulsa city. Conditions found to be favouring the
regional-scale transport were: sunshine level of greater than 65%, temperature over
5 °C, relative humidity less than 50% and wind speed exceeding 1.8 m/s. The con-
sidered region was about 1,000 by 1,000 km with a characteristic transport distance
being about 300–400 km in case of favourable conditions.
Many studies rely on single-point observations at a receptor site, thus concentrating
on evaluation of the origin of the observed pollen. Strong indication of the regional-
scale transport in such cases is high concentrations of pollen during night time when
the local release is low.
Examples of these studies are: the multi-species study of Damialis et al. (
2005 )
in Thessaloniki, the study of the regional impact of Copenhagen birch sources
(Skjøth et al. 2008b ) , impact assessment of the southern England birch population
onto London (Skjøth et al. 2009 ) , evaluation of the role of regional and long-range
transport on the Lithuanian pollen seasons by Veriankaité et al. ( 2010 ) ; the airborne
monitoring during a cruise across the East Mediterranean Sea (Waisel et al. 2008 ) .
Most of the above studies provided qualitative connections between the regional-
scale meteorological patterns and pollen distribution.
Some information helping to detect the pollen transport can be obtained using
the local phenological observations together with the pollen counts. For example,
the differences between fl owering dates recorded by phenology network and pollen
counts of Betula , Poaceae and Artemisia observed in Germany were correlated with
the regional and large-scale transport by Estrella et al. ( 2006 ) . Similar comparisons
were used by Veriankaité et al. ( 2010 ) , Siljamo et al. ( 2008b ) and others. However,
such comparison has to be performed with care because of very large uncertainties
of the phenological observations (Siljamo et al. 2008a ) .
In contrast with the above studies stressing the episodic features of the regional
transport, several assessments have concentrated on its long-term characteristics and
impact. The comparison of multi-annual data sets of airborne and deposited pollen in
northern Finland by Ranta et al. ( 2008 ) were used for qualitative description of the
main transport directions in the region. The other long-term studies included the redis-
tribution of genetically modi fi ed creeping bentgrass pollen (Van de Water et al. 2007 ) ;
and paleopalynological studies (Romero et al.
2003 ; Hooghiemstra et al. 2006 ) .
5.5 Long-Range Transport (LRT)
The large-scale dispersion of atmospheric constituents is controlled by synoptic-,
continental-, or hemispheric- scale meteorological phenomena. In particular, it means
that even combined observations at both source and receptor points cannot describe

137
5 Airborne Pollen Transport
the transport conditions. In fact, in many cases the mere connection between the
sources and receptors is dif fi cult to establish due to complicated large-scale disper-
sion patterns. Under such constraints, the indication of the LRT can be either foreign
pollen grains, which cannot be produced locally, or “wrong” time of appearance of
the grains, which is signi fi cantly outside the local fl owering period. Application of
dispersion models in either forward or inverse mode is practically inevitable for
analysis of such cases.
Studies of exotic grains observed in various parts of the globe started at least half
a century ago, mainly in application to polar altitudes where the diversity of plants
is comparatively low and exotic pollen is easier to fi nd (Nichols
1967 ; Ritchie and
Lichti-Federovich 1967 ; Janssen 1973 ; Ritchie 1974 ) . Probably, these were the fi rst
unequivocal proofs of existence of such long-range dispersion of pollen material.
Similar studies continued and extended towards temperate zones in later years
(Hicks 1985 ; Porsbjerg et al. 2003 ; Hicks et al. 2001 ; Campbell et al. 1999 ; Rousseau
et al. 2003, 2004, 2006 ) . In some cases, speci fi c meteorological conditions could be
identi fi ed as a driving force for the exotic pollen appearance (Campbell et al. 1999 ) .
However, usually the number of the exotic grains found is small and the practical
importance of the trans-continental transport for the human allergy and gene fl ow
has proven to be low.
More signi fi cant amounts of pollen are episodically transported from Northern
Africa, which has been identi fi ed as a source area in a number of studies. For
instance, Van Campo and Quet ( 1982 ) identi fi ed several pollen types that had been
transported from North Africa to Montpellier (France) together with mineral desert
dust. Further south, Cannabis sativa (marihuana) pollen originating in Morocco
was detected in Malaga, Southern Spain (Cabezudo et al. 1997 ) and Cannabis ,
Cupressus , Pinus , Platanus and Sambucus pollen were observed in Cordoba (South
Spain) exclusively during African-dust events (Cariñanos et al. 2004 ) . In addition,
the source areas of several LRT episodes in Tenerife (Canary Islands) that origi-
nated from Mediterranean region were traced to the Saharan sector and the Sahel
(Izquierdo et al. 2011 ) .
A strong association of biological particles with desert dust was also suggested
by Kellogg and Grif fi n ( 2006 ) . Dust clouds generated by storm activity over arid land
can result in mineral particles combined with viruses, bacteria (Hua et al. 2007 ; Hervàs
et al. 2009 ) , fungal spores (Grif fi n 2004, 2007 ; Grif fi n and Kellogg 2004 ; Grif fi n
et al. 2001a, 2003, 2007, 2006 ; Kellog and Grif fi n 2006 ; Wu et al. 2004 ; Garrison
et al. 2006 ; Schlesinger et al. 2006 ; Lee et al. 2007 ) and pollen being raised to altitudes
over 2 km and then transported for thousands of kilometres, i.e. at a planetary scale.
For example, viable microorganisms and fungal spores from Africa were sampled
at Barbados after being transported by African dust plumes (Prospero et al.
2005 ) .
Intercontinental mineral dust transport has been the subject of much attention
over decades (Guerzoni and Chester 1996 ; Goudie and Middleton 2001 ; Prospero
et al. 1996 ; Zhang et al. 1997 ) , but further research is needed on the biological com-
ponent associated to the dust.
Finally, the study of aerobiological long-range transport has stimulated a new research
line about the viability of pollen (Bohrerova et al. 2009 ) , bacteria (Hervàs et al. 2009 )
and fungal spores (Gorbushina et al. 2007 ) after the long distance dispersion.

138 M. So fi ev et al.
In Europe, numerous long range transport episodes have been identi fi ed in
Fennoscandia (Ranta et al. 2006 ; Oikonen et al. 2005 ) . Franzen and Hjelmroos
( 1988 ) observed pollen transport from Germany, Holland and England to the
Swedish coast, and Franzen (
1989 ) and Franzen et al. ( 1994 ) documented the arrival
of pollen grains to Fennoscandia from the Mediterranean. A strong episode of the
ragweed pollen transport from southern Europe to Finland was recorded in 2005
and traced back to the source areas in Hungary. A particularly speci fi c spring season
in Europe took place in 2006, when a strong plume of birch pollen was transported
from Russia over the whole Europe and reached Iceland. The pollen cloud was
mixed with the dense smoke from wild-land fi res, which allowed its easy identi fi cation
and follow-up by chemical observations and air quality models (e.g. Saarikoski
et al. 2007 ) .
As examples of transport episodes from East to West Europe, there are episodes
of Betula pollen coming from Poland and Germany to Denmark (Skjøth et al. 2007 )
and from Russia to Finland (Siljamo et al. 2008b ) . In the opposite direction, Betula
pollen arrived in Lithuania from Latvia, Sweden, Denmark, Belarus, Ukraine,
Moldava, Germany and Poland (Veriankaité et al. 2010 ) . Also, episodes of the pollen
of the tree species Fagus sylvatica (beech) reaching Catalonia were traced back to
central Europe (Belmonte et al. 2008 ) . Complicated large-scale patterns have been
found for Moscow by Siljamo et al. ( 2008b ) , who also reported strong bi-directional
exchange of birch pollen between Russia and Finland.
The LRT episodes are not speci fi cs of the tree pollen only. Thus, Smith et al.
( 2005 ) has registered Poaceae pollen in the UK originating from the continental
Europe. The pollen spectrum of Lithuania was also affected by long range transport
of several pollen types coming from other European regions (Sauliene et al. 2007 ) .
As an outcome of a series of studies, Ambrosia distribution pathways over Europe
were found to be highly irregular and episodic. Thus, the pollen registered in Poland, the
Balkans and Italy was shown to originate near the Pannonian Plain (Cecchi et al. 2006,
2007 ; Stach et al. 2007 ; Kasprzyk 2008 ; Smith et al. 2008 ; Šikoparija et al. 2009 )
and Ukraine (Kasprzyk et al. 2010 ) . In contrast, other studies recorded the transport
in the opposite direction from France, Italy and Croatia to Hungary (Makra and Pal fi
2007 ) , as well as from France to Switzerland (Clot et al. 2002 ) . There is also
evidence of Ambrosia pollen transport to Catalonia (northeastern Spain) from France
(Belmonte et al. 2000 ) . Furthermore, an increasing risk of LRT Ambrosia episodes
was detected in Scandinavia due to the rapid spread of ragweed in North-Northeast
Europe (Dahl et al. 1999 ) .
In North America, Raynor and Hayes ( 1983 ) applied the trajectory model to
cases of pollen wet deposition in Albany (New York) after long-distance transport
from the source areas located at South-Southwest. The arrival of Juniperus ashei
pollen released in southern Oklahoma and Texas to Tulsa has been reported by
Rogers and Levetin ( 1998 ) and Van de Water et al. ( 2003 ) .
In South America, extra-regional pollen of Celtis coming from the northeast and
of Nothofagus from the southwest has been found to contribute to Mar de Plata City
(Argentina) pollen records (Gassmann and Pérez 2006 ) .
In the north-western India, bioaerosols collected during dust storms sporadically
contained pollen from Himalayan species (Yadav et al. 2007 ) . The presence of pollen

139
5 Airborne Pollen Transport
grains from trees forming forests at much lower latitudes has been evidenced in the
Arctic environment (Bourgeois 2000 ; Savelieva et al. 2002 ; Rousseau et al. 2003,
2004, 2005, 2006, 2008 ) . Extra-regional pollen transport has also been found in
Antarctic (Wynn-Williams
1991 ) , Arctic (Campbell et al. 1999 ) , Australia (Salas
1983 ; Hart et al. 2007 ) , and New Zealand (Moar 1969 ) .
5.6 Release of Pollen Allergens from Grains
Probably the most important process that takes place during the pollen transport is
the release of allergen from the grains. In a dry atmosphere, pollen is very stable and
can keep its content over years (Stanley and Linskens 1974 ) . However, under
speci fi c conditions the allergen release can take place within minutes: (i) a high
relative air humidity; (ii) thunderstorms and heavy rain; (iii) high concentrations of
air pollutants. According to Behrendt et al. ( 1997 ) under humid conditions, allergens
are released from pollen grains in the process that resembles pollination. The exis-
tence of free allergen in air and its importance for the pollination season were
con fi rmed by a series of fi eld observations, such as Busse et al. ( 1972 ) for Ambrosia ,
Stewart and Holt ( 1985 ) for grass, and Rantio-Lehtimaki and Matikainen ( 2002 ) ,
Rantio-Lehtimaki ( 2002 ) , Matikainen and Rantio-Lehtimaki ( 1999 ) , and Rantio-
Lehtimaki et al. ( 1994 ) for birch.
Pollen allergens are generally glycoproteins. The majority of them are found in a
limited number of protein families. Their biological functions are presumably related
to the recognition, attachment, growth and development of the pollen tube on and
within the pistil, i.e. to hydrolysis of proteins, polysaccharides, and lipids, binding of
metal ions and lipids, and to the cytoskeleton (Radauer et al. 2008 ) . Allergen activity
can be detected, depending on the speci fi c role of the protein in question, both before
and after germination (Alché et al. 2002 ; Buters et al. 2010 ) . These proteins were
found in organelles, such as mitochondria, polysaccharide particles, starch granules,
and endoplasmatic reticulum (Behrendt and Becker 2001 ; Rodríguez-García et al.
1995 ) . Allergens are sometimes stored in the ectexine of the outer pollen wall. Rapid
elution and water solubility is considered an important prerequisite for a protein to
behave as a major allergen (Grote et al. 2001 ; Gupta et al. 1995 ) .
The allergen release from pollen is responsible for bio-availability of the allergen
(Behrendt and Becker 2001 ) but it also releases into the atmosphere the new particles
with entirely different transport features. Indeed, using the formulations of So fi ev
et al. ( 2006a ) and applying them to the allergen size distribution observed by Taylor
et al. ( 2004 ) or Miguel et al. ( 2006 ) , one obtains the sedimentation velocity for
allergen to be 0.01–0.1 mm sec
−1 , compared to 12 mm sec
−1 for pollen. Allergens
can stay in air much longer than pollens and thus be transported over much longer
distances. In contrast to pollen grains, which due to their size do not penetrate into
the lower human airways, the allergen aerosols are respirable and can provoke
stronger immune response than pollen itself (Motta et al. 2006 ) .
The allergen liberation is well reproduced in laboratories and can occur within a
few tens of minutes in favourable conditions (Behrendt et al. 1997 ; Behrendt and

140 M. So fi ev et al.
Becker 2001 ; Taylor et al. 2002, 2004, 2007 ) . The release can take place through the
pollen walls or through the pollen tube wall. The liberation is pH- and temperature
dependent (Behrendt et al. 1997 ) . It is yet unclear how frequent this phenomenon is
present in the real atmosphere because the fi eld measurement of allergen are
extremely complicated and often give semi-qualitative results based on the indirect
indicators (Matikainen and Rantio-Lehtimaki
1999 ; Buters et al. 2010 ).
Schäppi et al. ( 1997 ) found that atmospheric concentrations of birch pollen allergen
are correlated with birch pollen counts, but also that concentration of fi ne particles
associated to Bet v 1 dramatically increase with light rainfall. They suggested that
deposited pollen was stimulated to germinate, and that these particles were liberated
as pollen tubes dried out when the rain was over. In Derby, UK, the pollen levels
affected the number of emergency visits for asthma during days with light rainfall,
but not during dry days (Lewis et al. 2000 ) .
During thunderstorms, outbreaks of allergenic reactions have been registered
and a high fraction of the patients were sensitive to both grass pollen and fungal
spores (Pulimood et al. 2007 ) . It was suggested that pollen grains, spores and other
bioaerosols are carried into a cloud base, where they burst. Cold out fl ows transport
the debris downwards where it can cause the observed asthma exacerbations (Marks
et al. 2001 ) .
In contrast to results from other studies, Buters et al. ( 2010 ) in Munich did not
fi nd any Bet v 1 in the fraction 0.12–2.5 m m. A hypothesis was suggested that either
no allergen was present in the particles of this size range or the allergen was absorbed
to diesel soot particles and became invisible for the technique used. Diesel soot
particles bound to the grass allergen Lol p 1 under in vitro conditions were previ-
ously found by Knox et al. ( 1997 ) .
An indirect way to detect the allergen in the air is to monitor the long-range
transport plumes. Due to sharply different features of the particles, pollen grains are
deposited faster than allergen and, after some travel time, the only aerosols present
in air will be the allergen. The other option is to use size-segregated observations,
thus separating pollens and allergens in different fi lters. That way, however, is
dif fi cult because of: (i) non-ideal fi ltering of coarse particles during sampling, which
results in contamination of the fi ne-particle fi lter with pollen grains, (ii) very low
allergen concentrations in ambient air – a few nanograms m
−3 which are dif fi cult to
measure, (iii) addiction of allergen to the black carbon aerosols originating from
diesel exhaust, which can make the detection of the allergens complicated or
impossible.
5.7 Inter-Action Between Pollen and Chemical Pollutants
At all spatial and temporal scales, pollen, spores and, if present in a free form in the
air, allergens are subjected to chemical and physical interactions with other atmo-
spheric constituents. Chemical pollution can stress both the pollinating plants and
the pollen grain, which cause qualitative and quantitative changes of the pollen

141
5 Airborne Pollen Transport
content. The impact starts already during the pollen formation. For example, Aina
et al. ( 2010 ) found an increased amount of the allergenic proteins in the grass Poa
annua if the plant is grown in soil contaminated with cadmium. Similarly, the pollen
vitality of Parietaria judaica was found maximal in soils enriched with heavy metals
(Fotiou et al.
2010 ) .
Processes taking place in the air during pollen transport are very poorly studied
and one can only guess the type and intensity of the involved reactions. In an attempt to
classify these processes, one can consider the physical transformations (rupture of
grains, phase transformations of allergen particles, coagulation with other aerosols,
etc.); chemical transformations (oxidation and nitration), and biological transforma-
tions of the particles (loss of viability, germination). Some of these processes can
lead to substantial changes in the atmospheric features of the particles affecting
their life time and dispersion features, and some are of importance from medical
and biological points of view. In this sub chapter, we will consider only changes that
are induced by chemical and physical transformations and result in alterations of
the atmospheric features of the particles. The other aspects of the interactions are
considered in other chapters of this book.
During transport, the water content of the grains is adapted to ambient humidity
via water exchange through the pollen walls (Traidl-Hoffmann et al.
2003 ) . In a
very humid environment, a process resembling germination (so-called pseudoger-
mination) can be triggered resulting in an abrupt release of the pollen content into
the air (Motta et al. 2006 ; Traidl-Hoffmann et al. 2003 ; Grote et al. 2000, 2001,
2003 ) . Together with water, pollen also absorbs dissolved heavy metals, nitrate and
sulphur (WHO 2003 ) .
The other process revealed in laboratory conditions refers to damage of pollen by
aggressive chemicals, fi rst of all ozone and nitrogen oxides. The related chemical
processes are oxidation and nitration. According to Motta et al. ( 2006 ) , treatment
of pollen grains by ozone leads to a substantial increase of the fraction of damaged
pollen already for the ozone concentrations of 100 ppb, which can easily be observed
in the real atmosphere. For NO
2 , the levels of a few ppm needed for the non-negligible
impact were much too high to represent any real-life case, which questions the role
of nitrogen oxides in the pollen rupture. Apart from NO
2 and O
3 , oxidation agents,
such as hydroxyl radicals, can damage pollen or oxidise allergen but, to our know-
ledge, no experiments were performed with such species.
Chemical pollution can cause morphological changes of the pollen grains. Among
the effects are the collapse and thinning of the exine (Shahali et al. 2009 ) , which
increases a bioavailability of the content. Stronger leaching was one of the explana-
tions of observed decreased allergen detection in grass pollen exposed to car exhausts
(Peltre et al.
1991 ) , as well as to O
3 , SO 2 and NO
2 (Rogerieux et al. 2007 ) .
The reaction between the pollen allergens and air pollutants is nitration (Franze
et al. 2005 ) , which may augment the allergenic potential of the allergen.
Interactions of pollen with fi ne aerosols of anthropogenic origin are con fi rmed
by observations (Behrendt and Becker 2001 ) , who showed that small carbon
particles stick to the surface of pollen grains. This process provokes pseudo-
germination and pollen rupture. However, comparatively fast depositing pollens

142 M. So fi ev et al.
can serve as a scavenging agent for small particles. The latter process, however,
can be non-negligible only for the episodes with extremely high pollen concen-
trations in the air.
Interaction of allergen and diesel exhaust probably rests on the coagulation
mechanism. Knox et al. (
1997 ) showed that grass allergen molecules tend to bind to
micrometre-size aggregates formed by black carbon particles. Since the resulting
aggregate is in the respirable range and is much less soluble than the allergen itself,
these particles tend to be transported even further than allergen and are capable of
penetrating deep into human airways.
Apart from allergens, pollen grains also contain an array of non-allergenic but
pro-in fl ammatory pollen-associated lipid mediators (PALMs) and enzymes that are
suggested to be involved in the pathogenesis of allergic diseases (Gunawan et al.
2008 ; Gilles et al. 2010 ) . Pollen collected in streets with a heavy traf fi c released
signi fi cantly more PALMs than pollen collected in rural surroundings (Risse et al.
2000 ) . A similar effect was found by Behrendt et al. ( 1999 ) for grains treated with
volatile organic compounds.
5.8 Modelling the Pollen Dispersion
Modelling studies of pollen dispersion in the atmosphere are not numerous and
concentrate on just two main directions. The most widely used model-based
approach is an inverse-modelling analysis of the observational results and determi-
nation of the source regions affecting the speci fi c monitoring site. This direction is
dominated by simple backwards trajectory analysis.
The second application area – forward pollen dispersion – aims at simulation of
the actual pollen life cycle: production, release into the air, transport, deposition,
and sometimes, also the health impact. The outcome of such studies is a set of con-
centration and deposition maps of the particular pollen type. This task normally
requires comprehensive systems, detailed input information of various kinds, and
substantial computational resources. Compromising approaches can be applied in
this area too but they are quite rare.
5.8.1 Inverse Studies and Analysis of Observational Results
The inverse modelling studies usually pursue one of the following objectives:
(i) to fi nd out the reasons of the observed peculiar behaviour of pollen concentra-
tions, (ii) to outline the source areas most frequently and signi fi cantly affecting
certain region. The fi rst task normally covers a short period of time, up to a few days
that immediately precede the considered episode, while the second task requires the
analysis of a period of a few years to accumulate suf fi cient information for the
statistical analysis.

143
5 Airborne Pollen Transport
The most common and simple method for back-tracing the origin of the observed
pollen is the backward trajectories computed using a Lagrangian trajectory model
(the Lagrangian trajectory approach). Within this method, a few trajectories directed
backwards in time are started from the location of the pollen monitor and evaluated
a few hours or days back in time. The resulting trajectories roughly show the direction
from where the air masses arrived at the observation place during the observation
time period. Since the method is qualitative, extra measures can be taken to increase
the value of the outcome: clusters or ensembles of trajectories can be started from
different points (e.g. Smith et al.
2005 ; Stach et al. 2007 ) , trajectories can also be
started at different heights, a random shift of start time can be applied, etc.
The backward trajectory methodology has been used for studying the origin of
the observed pollen by Gassmann and Pérez ( 2006 ) for Celtis and Nothofagus pollen
in Argentina, by Stach et al. ( 2007 ) and Smith et al. ( 2008 ) for Ambrosia in Poland,
by Skjøth et al. ( 2007 ) for birch in Poland, by Šaulienė and Veriankaitė ( 2006 ) and
Veriankaité et al. ( 2010 ) for birch in Lithuania, by Cecchi et al. ( 2007 ) for Ambrosia
in Central Italy, by Mahura et al. ( 2007 ) and Skjøth et al. ( 2007, 2008b ) for birch in
Denmark, by Skjøth et al. ( 2009 ) for birch in London, etc. The bulk of the works
were based on inverse trajectories computed by the NOAA HYSPLIT model
(Draxler and Hess 2010 ) , but also by own systems, such as THOR (Skjøth et al.
2002 ) in Denmark. A manual construction of trajectories via summing-up the wind
vectors was used by Giner et al. ( 1999 ) for evaluation of the Artemisia source areas
affecting the station at the eastern coast of Spain.
Apart from the above regional-scale assessments, trajectories were also used for
back-tracking the transoceanic transport. In particular, many studies quoted in
Sect. 5.5 (Long-Range Transport) have used trajectories to understand the origin of
the exotic pollen (e.g. Rousseau et al. 2003, 2004, 2006 ) .
A next step in the complexity of the methodology is consideration of a large
number of Lagrangian particles – from a few thousands and up to a few millions.
Each particle is transported with wind, thus drawing a trajectory, and is randomly
relocated at every model time step, thus re fl ecting the impact of atmospheric diffusion.
This approach is called as the “Lagrangian particle random-walk” method. In com-
parison with few trajectories, the large number of particles allows reproducing the
actual shape of the clouds and makes it possible to include linear transformation
processes, dry, and wet deposition. Most of the presently used Lagrangian models
follow that paradigm: FLEXPART (Stohl et al. 2005 ) , SILAM-L (So fi ev et al.
2006b ) , DERMA (Sorensen 1998 ) , SNAP (Saltbones et al. 2001 ) .
The statistical analysis of a great number of back trajectories from receptor sites
has turned out to be an ef fi cient tool to identify sources and sinks of atmospheric
trace substances or to reconstruct their average spatial distribution (Ashbaugh
1983 ;
Seibert et al. 1994 ). A trajectory statistical method was successfully applied to iden-
tify source areas for beech pollen recorded in Catalonia (Belmonte et al. 2008 ) and
for pollen of various species recorded on Tenerife (Izquierdo et al. 2011 ) .
Lagrangian particle models, being more realistic than Lagrangian trajectory ones,
still inherit some weaknesses of the Lagrangian approach – fi rst of all, limited spatial
representativeness of a single Lagrangian particle. As a result, even for regional studies

144 M. So fi ev et al.
an astronomical number of particles have to be evaluated (Veriankaité et al. 2010 ;
Siljamo, et al. 2008b ; So fi ev et al. 2006a ) . It was also shown that the trajectory models
show dif fi culties in mountain regions, where air fl ow patterns are known to be complex
(Pérez-Landa et al.
2007a, b ) and hard to explore with few trajectories. It has there-
fore been suggested that more sophisticated (Eulerian) approaches could be applied
for such environments (Smith et al.
2008 ; Šikoparija et al. 2009 ) .
Compared with the back-trajectories approaches, adjoint dispersion modelling
offers a more rigorous instrument to identify the sources (Marchuk 1982 ) . The idea
of the method is to explicitly compute the sensitivity of the observed values to
emission fl uxes, chemical transformations and meteorological processes, which can
affect the particular observation. Adjoint methods (both explicitly identi fi ed in the
studies and implicitly included into the numerical systems) are extensively used in
observational analysis at a local scale (Kuparinen 2006 ; Kuparinen et al. 2007 ; Rannik
et al. 2003 ) , in the source apportionment of greenhouse gas emissions (Bergamaschi
et al. 2005 ) , analysis of regional air quality (e.g. Saarikoski et al. 2007 ; Prank et al.
2008, 2010 ) , and for the regional-scale source apportionment of the observed pollen
(Veriankaité et al. 2010 ; So fi ev et al. 2006a, b ; Siljamo et al. 2008b ) .
An example of comparison of the three methodologies is a study of Veriankaité
et al. ( 2010 ) , where the Lagrangian trajectory model HYSPLIT was used in combi-
nation with the SILAM model, which was run in two setups: as a back-tracking
Lagrangian particle model and as an Eulerian model in adjoint mode. Despite quali-
tatively similar conclusions of all three analyses, a general recommendation was
favouring the most comprehensive and least subjective approach of Eulerian adjoint
modelling. It was noted that the results of trajectory analysis suffered from limited
representativeness of a single trajectory and depended on subjective selection of the
trajectory setup. The problems can become acute in meteorologically complicated
situations, such as passage of a frontal line, strong cyclonic activity, etc.
As a post-processing of the model output for quantitative source identi fi cation, a
number of analytical inversion techniques have been developed and applied in air
pollution research (Wotawa et al. 2003 ; Seibert and Frank 2004 ; Stohl et al. 2009 ) .
Such methods have not yet been applied to the problem of identifying pollen sources.
The same is true for variational methods of source apportionment (So fi ev and
Atlaskin 2007 ) .
5.8.2 Forward Simulations and Forecasting
of the Pollen Seasons
Forward model simulations aim at predicting the pollen emission from plants,
dispersion in the atmosphere and computing on its basis the concentrations of the
grains at any distance from the sources. Similar to the inverse modelling, both
Lagrangian and Eulerian approaches have been developed for these tasks.
A series of works addressed the local-scale dispersion of pollen from genetically
modi fi ed plants: see review of Kuparinen ( 2006 ) , applications for maize by Arritt

145
5 Airborne Pollen Transport
et al. ( 2007 ) , Jarosz et al. ( 2004 ) , and Aylor et al. ( 2006 ) , theoretical considerations
of Novotny and Perdang ( 2003 ) and Fraile et al. ( 2006 ) , meso-scale Lagrangian
model of Kuparinen et al. ( 2007 ) , etc.
In the 1980s, the signi fi cance of atmospheric conditions for the regional pollen
dispersion was considered and the trajectory model applied for prediction of the
transport direction (Davis and Main
1984 , 1986 ). Another work was connected with
the transport of marihuana pollen from Northern Africa to Europe (Cabezudo et al.
1997 ) . The transport of Junipeerus Ashei pollen towards the city of Tulsa, USA, was
studied by Van de Water et al. ( 2003 ) and Van de Water and Levetin ( 2001 ) , who used
a separately estimated release of pollen in combination with trajectory and weather
pattern analysis. The result was a qualitative forecast of the threat to the downwind
regions, covering a territory of about 1,000 km
2 . The outcome was encouraging:
during the two seasons of 1998–1999, only a single occurrence of “high” or “very
high” pollen concentrations in the city of Tulsa was not directly linked to “moderate”
or “severe” forecast threat predictions. Other applications include the study of Pasken
and Pietrowicz ( 2005 ) for oak, and the study by Kawashima and Takahashi ( 1999 )
for Japanese cedar (both emission and dispersion were predicted).
The results of trajectory model applications in 1990s showed a principal possi-
bility to predict the pollen distribution by means of dispersion modelling. However,
actual forecasting for large areas, such as Europe, could be approached only by
integrated modelling systems, which incorporate descriptions of all the parts of the
pollen cycle: production, release, transport, transformation, and deposition (Helbig
et al. 2004 ; So fi ev et al. 2006a, 2009 ; Siljamo et al. 2004a, b, 2006, 2007, 2008a, b, c ;
Vogel et al. 2008 ) . The corresponding biological, mathematical and physical
sub-models are then driven only by historical, actual and forecasted meteorological
information.
The input information for the pollen dispersion models consists of: (i) map of
distribution of the source plants (see maps in So fi ev et al. 2006a ; Skjøth et al. 2008a,
2010 ) , (ii) information for the phenological model covering the whole region of com-
putations and predicting both start and end of the fl owering season (Helbig et al.
2004 ; Linkosalo et al. 2010 ; Siljamo et al. 2007, 2008c ; Efstathiou et al. 2011 ) , (iii)
information on the pollen season severity obtained from observations or predicted
from historical meteorological and pollen data (Ranta and Satri 2007 ) , (iv) meteoro-
logical data produced by numerical weather prediction models. One has to warn
against the climatology-based approaches, when the model is driven by long-term
averaged parameters, such as the fl owering date expressed as Julian date of the year:
such approaches are bound to fail in every speci fi c situation (So fi ev et al. 2006a ) .
Observational information on pollen distribution, to our knowledge, is not used
in any of the presently existing systems. The same is true for the remote-sensing
observations, despite showing that they provide valuable information about the phe-
nological stages of some plants (Høgda et al.
2002 ) .
The pollen emission and dispersion computations are usually split into two
stages: prior to the season and during the season. Before the season, the only
important process is the accumulation of the heat sum and other relevant param-
eters, which control the pollen development. Upon ful fi lling some condition

146 M. So fi ev et al.
(e.g., reaching the prescribed heat sum threshold), the actual pollination starts.
During that stage, the model follows the propagation of the fl owering season but
also responds to the short-term meteorological stress, such as cold weather, diurnal
cycle of temperature and humidity, precipitation, etc. These variations can be
parameterised as a function of daytime (e.g. Mahura et al.
2009 ) or predicted
dynamically from the basic meteorological parameters (Linkosalo et al.
2010 ;
Helbig et al. 2004 ; Siljamo et al. 2008b ) .
5.9 Discussion
In this section we will brie fl y touch on a few aspects of the pollen and allergen
dispersion in the atmosphere, which bring together the above sections and illustrate
interconnections between them. The topics are by no means exhaustive and the
reader is referred to the cited literature sources for a more comprehensive analysis.
5.9.1 Quanti fi cation of Pollen Loads at Different Scales
As shown above, the pollen dispersion in the atmosphere is relevant at a very wide
range of scales (Fig. 5.1 ) – from micro-scale up to continental. There are very few
pollutants, which distribution can be relevant for so large variety of spatial and
temporal scales. The main reason for this is a large variety of processes where the
pollen impact is important – especially, biological and medical.
For successful fertilization, the density of the pollen cloud must be thousands
of grains per cubic metre, though this level varies greatly for different species.
At lower concentrations, probability of fertilization becomes too small to be bio-
logically important (Gleaves 1973 ) . Such concentrations usually can be obtained
only in the source vicinity, i.e. biologically relevant distances hardly exceed tens or
hundreds of metres for herbaceous species and, possibly, a few kilometres for wind-
pollinating trees.
With regard to human allergy, concentrations much lower than those near the
source are important (see the chapter on the pollen allergy threshold). In some
cases, a few tens of grains per cubic metre can be suf fi cient to trigger the fi rst
symptoms. As demonstrated by the above-quoted studies, such concentrations can
easily be observed at hundreds and, possibly, thousands of kilometres distance from
the sources.
Finally, the inter-continental transported pollen, though existing (see the LRT
section), can hardly play any practical role due to extremely low concentrations at
the receptor point. One can, however, use exotic pollen as a natural tracer, which can
reveal the pathway and frequency of the inter-continental exchange. Pollen, therefore,
can serve as a marker of distribution of other species, whose observations may be
not possible or very complicated.

147
5 Airborne Pollen Transport
A very wide range of relevant scales poses serious challenges to pollen monitoring
and modelling. To date, the most widely used methodology of pollen monitoring is
the Hirst ( 1952 ) trap. This is a low-volume device (about 0.01 m
3 min −1 ), which is
prone to signi fi cant uncertainties. The problem exacerbates further due to sharp
gradients of pollen concentrations occurring in the vicinity of the sources: moving
the trap higher or lower or repositioning it may lead to strong changes.
From the modelling point of view, the major challenge is also the near-source
distribution and a fraction of pollen that reaches the regional scale of transport.
All regional models currently ignore the complexity of the micro-scale processes
replacing them with some variations of the “escape fraction” of Gregory (
1961 ) .
The micro-scale models, to the contrary, concentrate of reproducing the patterns in
the nearest vicinity of the source while the regional tails of the distributions are
considered unimportant for the applications.
These de fi ciencies in both current monitoring and modelling techniques are to be
addressed in future research in order to end up with comprehensive descriptions of
pollen dispersion at all relevant spatial and temporal scales.
5.9.2 Variety of Modelling Approaches: Any Chance
for Harmonization?
A variety of model types used for the pollen-related simulations is largely caused by
historical reasons and conditions when the availability and simplicity of the systems
were the primary selection criterion. However, the capabilities of the Lagrangian
trajectory models are still suf fi cient for some tasks, fi rst of all, for analysis of some
speci fi c measurements obtained at a single station. As long as a simple direction of
transport is enough, these models are the reasonable choice. For more sophisticated
observation analysis one has to rely on Lagrangian particle or Eulerian systems.
Concerning the prediction of the pollen concentrations (the forward modelling
tasks), the de fi ciencies of Lagrangian approaches ( fi rst of all, limited representativeness
of a single particle) become impossible to overcome, so for these tasks the compre-
hensive Eulerian models are inevitable.
A speci fi c problem of Lagrangian trajectory systems is selection of the type of
trajectories. For about half a century it has been known that, for example, isobaric
trajectories should not be used because they can produce entirely wrong results
(Danielsen 1961 ) . The isentropic trajectories are somewhat better but can still deviate
from the true pathways, which can be drawn only by fully 3D trajectories. In that
sense, the Lagrangian particle models, usually based on 3D trajectories, are preferable.
Considering the comprehensive systems for the pollen forecasts, one has to pay
special attention to the phenological models as a part of the emission sub-system.
There have been numerous phenological models developed for many species (see the
Chap.
3 , “Pollen season”, and Sect. 4.3.2 “Process based phenological models”, this
issue). However, practically all of these models have been developed and evaluated

148 M. So fi ev et al.
within a limited territory. An attempt to use these models outside their native region
leads to unpredictable results. The experience of SILAM system (presently, the only
European-scale pollen forecasting system) shows that none of the existing pheno-
logical models can satisfactorily describe the birch phenology over the whole conti-
nent. The thermal-sum model used in SILAM had to be reparameterised for each
sub-region independently (Sect.
4.3.3.1 “Application to large areas”). Understanding
the differences between the regions and the reasons why the same taxa require dif-
ferent accumulated heat in different regions is a challenge for the future.
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