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A new portable sampler to monitor pollen at street level in the environment of patients

  • Leidse instrumentmakers School

Abstract and Figures

Allergic rhinitis caused by pollen exposure is one of the most common allergic diseases. Therefore monitoring pollen levels in ambient air is an important tool in research and health care. Most European monitoring stations collect airborne pollen at rooftop levels for measurements in the larger surrounding of the sampling station, and not in the direct environment of sensitized subjects. Here we present the development and evaluation of a portable pollen sampler, called “Pollensniffer”, that was designed to collect pollen in the immediate environment of allergic subjects. Validation of the Pollensniffer against the standard volumetric pollen sampler showed for most pollen types high correlations between the number of pollen collected by those two devices (Spearman's Correlation Coefficient > 0.8); the Pollensniffer appeared to collect on average 5.8 times more pollen per hour than the static sampler. Pollen monitoring was performed using this Pollensniffer at street level at 3 different locations in the city of Leiden during 22 weeks in 2017 and 21 weeks in 2018, during three 15-min periods a day and at one day in the week. The results showed that the pollen levels for birch and grass pollen can significantly differ from location to location and per time of day. Furthermore, the Pollensniffer measurements at street level showed that birch and grass pollen grains were detected 1 1/2 and 2–3 weeks, respectively, before detection at rooftop level. The street measurements show that allergic subjects can encounter varying pollen levels throughout the city and that they can be exposed to grass and birch pollen and may experience hay fever symptoms, even before the sampler at rooftop level registers these pollen.
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A new portable sampler to monitor pollen at street level in the
environment of patients
Letty A. de Weger
,Frank Molster
, Kevin de Raat
, Jeffrey den Haan
,Johan Romein
, Willem van Leeuwen
Hans de Groot
, Marijke Mostert
,Pieter S. Hiemstra
Department of Pulmonology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands
Leidse Instrumentmakers School, Einsteinweg 61, 2333 CC Leiden, the Netherlands
University of Applied Sciences, Zernikedreef 11, 2333 CK Leiden, the Netherlands
Department of Allergology, Reinier de Graaf Gasthuis, Reinierde Graafweg5, 2625 AD Delft, the Netherlands
An easy-to-use portable pollen sampler
is developed and validated
At street level grass and birch pollen is
distributed unevenly throughout a city
Grass/birch pollen is detected weeks
earlier at street- compared to
rooftop level
abstractarticle info
Article history:
Received 27 March 2020
Received in revised form 28 May 2020
Accepted 19 June 2020
Available online 20 June 2020
Editor: Pavlos Kassomenos
Pollen monitoring
Personal sampling
Street level
Portable sampler
Hay fever
Allergic rhinitis
Allergic rhinitis caused by pollen exposure is one of the most common allergic diseases. Therefore monitoring
pollen levels in ambient air is an important tool in research and healthcare. Most European monitoring stations
collect airborne pollen at rooftop levels for measurements in the larger surrounding of the sampling station, and
not in the direct environment of sensitized subjects. Here we present the development and evaluation of a por-
table pollen sampler, called Pollensniffer, that was designed to collect pollen in the immediate environment of
allergic subjects. Validation of the Pollensniffer against the standard volumetricpollen sampler showed for most
pollen types high correlations between the number of pollen collected by those two devices (Spearman's Corre-
lation Coefcient N0.8); the Pollensniffer appeared to collect on average 5.8 times more pollen per hour than the
static sampler. Pollen monitoring was performed using this Pollensniffer at street level at 3 different locations in
the city of Leiden during 22 weeks in 2017 and 21 weeks in 2018, during three 15-min periods a day and at one
day in the week.The results showed that thepollen levels for birch and grass pollen can signicantly differ from
location tolocation and per time of day. Furthermore, the Pollensniffermeasurements at street level showed that
birch and grass pollen grains were detected 1 1/2 and 23 weeks, respectively, before detection at rooftop level.
The street measurements show that allergicsubjects can encountervarying pollen levels throughout the city and
that they canbe exposed to grass and birchpollen and may experiencehay fever symptoms, evenbefore the sam-
pler at rooftop level registers these pollen.
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://
Science of the Total Environment 741 (2020) 140404
Corresponding author at: Dr. L.A. de Weger, Department of Pulmonology, Leiden University Medical Center, P.O. Box 9600,2300 RC Leiden, the Netherlands.
E-mail address: (L.A. de Weger).
0048-9697/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (
Contents lists available at ScienceDirect
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1. Introduction
Thirty to forty percent of the Europeans suffers from the symptoms
of allergic rhinitis (Blomme et al., 2013), and although sometimes trivi-
alized, it is considered as a major health problem. The most common
symptoms of allergic rhinitis are itchy or runny nose, sneezing, blocked
nose and commonly co-existing non-nasalsymptoms such as itchy and
watery eyes; importantly, also quality of life, sleep and work productiv-
ity is affected (Bousquet et al., 2006). Although these symptoms are
bothersome, about 10% of the patients never consult their doctor for
their problems, and most patients treat their symptoms by self-
medication (Maurer and Zuberbier, 2007). Yet, it is known that inade-
quate treatment of allergic rhinitis can trigger the development of
asthma (Bousquet et al., 2008).
Besides these medical and social aspects, also the economic impact
of allergic rhinitis is considerable: the total mean of health care and in-
direct costs (absenteeism and presenteeism) amounts to 2326,70 per
allergic rhinitis patient-year (Colas et al., 2017).
Pollen exposure is a major trigger for allergic rhinitis symptoms.
For both patients and their treating physicians, it is relevant to
know when allergenic pollen is in the air. Therefore, more than 500
pollen monitor stations in Europe monitor the pollen grains that
are present in the air on a daily basis (Buters et al., 2018). The pollen
samplers are located at a height of 1030 m above ground level to
collect the different pollen types that are released by the plants pres-
ent in the region and is thus representative for the larger surround-
ings. Monitoring pollen at this height is justied since sampling at
near ground level would favour the collection of more locally pro-
duced pollen and would show more daily uctuations, while at
10 m and higher above ground level the pollen concentrations are
more stable (Galán et al., 2014;Rojo et al., 2019). However, patients
are mostly exposed at street level, and knowledge on the d istribution
of aero-allergens at this level is limited. Some studies monitored pol-
len at different height levels and compared the street level measure-
ment using a stationary sampler with the same kind of sampler
located at rooftop level. Most of these studies showed that the pollen
concentrations at rooftop level correlate well with the pollen con-
centrations monitored at street level, although in general the con-
centrations were higher at street level (Bastl et al., 2019;Rantio-
Lehtimäki et al., 1991;Rojo et al., 2019;Spieksma et al., 2000). One
studyshowedthatatsocalledstreet canyon environmentsin
urban areas, grass pollen concentrations tended to be lower (Peel
et al., 2014a). Studies on the pollen distribution at street level in cit-
ies are limited. In these studies different sampling methods have
been used, but they all indicate that highly variable pollen concen-
trations exist at different locations within cities at street level
(Ishibashi et al., 2008;Katz and Carey, 2014;Skjøth et al., 2013;
Werchan et al., 2017).
In order to be able to monitor pollen at different locations, easy-to-
use, portable samplers are needed. Furthermore, such samplers can be
used to monitor pollen or other allergens in the direct environment of
the patient. Fiorina et al. (Fiorina et al., 2003)describedthataerobiolog-
ical sampling in the direct environment of two asthmatic patients, for
whom the causative allergen was difcult to identify, revealed the re-
sponsible allergen. In that pilot study, a battery operated portable sam-
pler (PARTRAP) was used (Fiorina et al., 1997). In the past decades
several other personal samplers have been described, but to our
knowledge- these devices, including the PARTAP, are not commercially
available (Peel et al., 2013;Renstrom et al., 2002;Werchan et al., 2018;
Yamamoto et al., 2015). Furthermore, the user-friendliness and the ef-
cacy of these devices varies; nasal samplers (Renstrom et al., 2002)are
placed in the nostrils of the nose and depend on breathing, and the Per-
sonal AeroAllergen Sampler (PAAS) (Yamamoto et al., 2007)relieson
passive pollen collection and is carried around the neck for a longer pe-
riod, which both may not be very comfortable. The currently available
transportable samplers (Peel et al., 2014b) are made for installation on
the oor, but not specically for portable use and furthermore has a
lower efciency than a static 7-day recording sampler.
Therefore, in this paper we developed a portable sampler, that is
ease-to-use to collect allergens at different locations, especially in the
environment of allergic patients. Although the constructed device,
called Pollensniffer, can also collect other bioaerosols like fungal spores,
in this study we focussed on pollen. The Pollensniffer was validated by
comparison to a standard Hirst type pollen sampler (Hirst, 1952), and
used to study the distribution of birch and grass pollen at street level,
since here the patients will come into contact with the pollen. In the
analysis of the data we focussed on issues that are relevant for the aller-
gic rhinitis patients such as (i) the differences in pollen levels at differ-
ent locations in the city; and (ii) the timing of exposure to the rst
pollen grains of the season at street level.
Fig. 1. a. Pollensnifferwith the conicalinlet (A) and the aluminium closingof the opening for thesample holder (B).A connector cableconnects a power bank(C) to a micro-USB connector
(D) in the Pollensnifffer.The airstream generated by the ventilator insidethe Pollensnifferleaves the Pollensniffer at openingsat the rear end (not shown)and at three backsides (e.g.E). b.
The Pollensniffer (PS1) mounted on the rain cover of the static sampler on the roof of the LUMC.
2L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
2. Materials and methods
2.1. Description of the Pollensniffer
The Pollensniffer isa portable device to monitor airborne pollen with
the following dimensions: diameter 6 cm; length 15 cm; weight 408 g
(Pollensniffer) + 205 g (power bank). The body of this low-volume
Pollensniffer (Fig. 1a) consists of four elements that were printed in a
3D printer using POM (polyoxymethylene). The technical drawings
are shown in Supplementary Fig. S1.Three elements at the back
(Fig. S1, b, d and e) were connected using 4 long thin bolts. The inlet is
placed onto the body via a bayonet mount. The inlet (Fig. S1,a) has a
conical shape and behind the inlet, a static sample holder is inserted.
This sample holder can be placed into the Pollensniffer via an opening
on the side. This opening is secured by an aluminium handle (Fig. 1a,
arrow B).
The static sample holder contains a 4 cm-long, 2.5 cm-wide carrier
glass-slide on which a Vaseline-coated Melinex strip (Burkard
Manufacturing Co. Limited, UK) is mounted to collect the pollen, that
passed the inlet. Behind thesample holder, a ventilator generates an air-
stream into the Pollensniffer. Pollen grains that are drawn in, are
trapped onto the Vaseline strip. The air stream passes through the
Pollensniffer and escapes via openings at the rear end (Fig. 1a, arrow
E). Electronics to power the ventilator are located behind the ventilator.
Using a connector cable, a power bank (Fig. 1a, C; Intenso S10000,
Germany) is connected to the Pollensniffer via a micro-USB connector
(Fig. 1a, D). The ventilator generates an airow through the inlet
(10 mm) and the pollen grains are drawn inwards and collide with
the Vaseline-coated Melinex tape and stick into the Vaseline. Although
an estimated 90% of the pollen grains are deposited on the Vaseline
tape in an approximate 12 mm circle behind the inlet, also all pollen
grains collected on the tape outside this circle are counted (see also
To measure the air ow that passed through the Pollensniffer an
electronic ow cell (Honeywell AWM5102 VN airstream sensor, North
Carolina, USA) was air-tightly connected to the conical inlet of the
Pollensniffer via a funnel. The ow appeared to be constant during the
whole operating time of the power bank, i.e. 56h.
The Pollensniffer was evaluated for its user-friendliness by partici-
pants of a local theatre and music festival in Schipborg, Netherlands
(FestiValderAa, 79 July 2017). Fifteen random, voluntary participants
used the Pollensniffer by holding it in their hand during a short walk
and completed a small questionnaire on the user-friendliness of the
2.2. Validation of the Pollensniffer
On the roof of the 6th oor of the Leiden University Medical Center
(LUMC; west part of the Netherlands), approx. 22 m above ground
level two static traps (Burkard Manufacturing Co. Limited, UK) were
present. One sampler was used for the routinely monitoring of the
daily pollen concentrations (see later), while on the other sampler the
rstly constructed Pollensniffer (PS1) was mounted on the rain-cover.
The inlets of the Pollensniffer and the static sampler were both facing
the wind guided by the wind vane of the static sampler (Fig. 1b). As
shown in Fig. S1 the inlet of the Pollensniffer has a conical shape and a
round inlet with a 10 mm diameter. The static sampler has a standard
rectangular inlet of 14 × 2 mm. Both devices collected pollen during 1
or 2 h periods from February 28th to April 4th 2017. Forty-one samples
for each of the devices were collected on warm, dry days, anticipating
high levels of pollen.
Following manufacturing of the rst Pollensniffer (PS1), another
four Pollensniffers (PS2, PS3, PS4, and PS5) were constructed. The per-
formance of these Pollensniffers was compared among each other. To
this end, the ve Pollensniffers were mounted on a rack (15 cm height)
at a distance of 20 cm from each other on the roof of the LUMC. They
were allowed to collect pollen for 30 min periods on June 13th and
19th, 2017, resulting in 8 different samples for each of the ve
Pollensniffers. In this period mainly Poaceae and Urticaceae pollen
were present and used for the analysis. All Poaceae and Urticaceae pol-
len presenton the slides were counted.
2.3. Pollen monitoring at street- and rooftop level
Rooftop level pollen samples were collected with one of the static 7-
day volumetric spore trap continuously. At street level, pollen were col-
lected by the Pollensniffer from April 6th to September 5th in 2017 and
February 7th to June 29th in 2018. Three locations in the city of Leiden
were selected (Fig. 2): (i) in a main shopping street in Leiden (=City
Center) without any plants or trees, except for Linden trees (Tilia)in
the side way; (ii) on an open space in a park within the city canal
boundaries (Huigpark) with a stretch of grass and a diversity of trees,
like Aesculus, Salix, Betula, Platanus, Populus, Sorbus, and Taxodium
distichum; and (iii) on an open space in a park more at the border of
the city (Kweeklust) with a small grass area and several trees like
Platanus, Populus, Salix, Taxodium distichum, and bushes like Corylus.
The birch tree species in the surroundings of the monitoring locations
are mapped and shown in Supplementary Fig. S2. An overview of the
birch species present in an approximately 500 m circle around the mon-
itor locations is as follows: for the City Center location only a few Betula
species are present in a 500 m circle, i.e. B. pendula (8 trees),
B. papyfrifera (6 trees) and B. ermanii (6 trees). For the Huigpark location
hardly any Betula species are present at the south side of the park, but at
the north side several B. pendula species are found e.g. B. pendula (60
trees), B. papyrifera (15 trees), B. ermanii (6 trees), B. nigra (30 trees)
and B. utilis (17 trees). In the surroundings of Kweeklust (within a
500 m circle) at the north side of the city, hardly any Betula trees are
present but in the streets on the other sides different Betula species
can be found, i.e. B. pendula (43 trees), B. ermanii (4 trees), B. papyrifera
(5 trees), B. utilis (2 trees), B. pubescens (8 trees) and B. nigra (3 trees).
When focussing on grass in the surroundings of the monitoring loca-
tions it is clear that for the City Center very little grass is present except
for some well-maintained grass areas at approx. 400 m from the loca-
tion. Also at the locations in the parks, the grass areas are well main-
tained. Only close to Kweeklust there are some grass banks that are
mowed only twice a year (early July and half September).
At each location, pollen were sampled for 15 min in the morning
(910.30 h), in the early afternoon (12.3014.00 h) and early evening
(16.3017.00 h) during one day in the week. The Pollensniffer was
hand-held by a standing person, resulting in a measuring height of ap-
proximately 1 to 1.20 m. For the street level measurements Pollensniffer
PS1 was used. The day varied depending on the weather; especially dry,
sunny days were selected for measurements unless this was not possi-
ble. At each location weather parameters like temperature, wind
speed and rain were recorded.
In the static sampler as well as in the Pollensniffer, the pollen grains
were collected on a Melinex strip covered with Vaseline. The strip was
stainedbysafranin(0.002%w/v) solution and mounted on a micro-
scopic slide (Galán et al., 2014).
For the daily pollen concentrations the microscopic slides from the
static sampler (rooftop level counts) were scanned in three longitudinal
bands corresponding to 1 m
collected air in 24 h, to obtain daily pollen
concentrations (grains/m
,24h)(de Weger et al., 2013;Galán et al.,
2017). On the strips of the Pollensniffer, all pollen collected were micro-
scopically differentially counted.
For the validation experiments, the start and the end of each mea-
surement with the static sampler was marked on the Vaseline strip. In
contrast to the analysis of daily pollen concentrations where dened
areas are being counted, in this analysis all pollen present on the strip
between these marks were counted. So in the validation experiment
all pollen collected on the tape in the static sampler were compared to
all pollen collected on the tape in the Pollensniffer. For the validation
3L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
experiments, the results obtained from the Pollensniffer samples and
the static sampler were expressed as pollen counts and not as pollen
concentrations, since the counts were not related to the sample volume.
The samplevolume is dependent on the ow rate of the device and we
could not use the same device to measure the ow rate in the static
sampler and the Pollensniffer since the inlets are different. Furthermore,
Fig. 2. a. Map of the city of Leiden showing the location ofthe roof top level static sampler and the locations of the street level measurements. b. The three locations of the street level
measurements, City Center, Huigpark and Kweeklust.
4L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
Oteros et al. (Oteros et al., 2017) showed that there is a large variation in
ow rates measured in routinely used Hirst type samplers. Since we
used different devices and comparing ow rates was not possible due
to technical restrictions, we carefully standardized sample collection
by using a strict protocol concerning duration and time of collection,
height at which pollen were collected, and position of collection by
directing the inlet of the Pollensniffer facing the current wind direction.
Subsequently all pollen present in the sample were microscopically
counted and compared.
An overview of the different experiments is provided in Table S1.
2.4. Analysis of data
For the analysis of the validation experiments a correlation analysis
was performed to assess the strength of the relationship between the
pollen collected by the Pollensniffer and the static pollen sampler.
Since a Shapiro-Wilk test showed that neither the original nor the log-
transformed pollen data of both samplers were normally distributed,
Spearman's correlation coefcient was used. Differences and correla-
tions were considered statistically signicant at pb.05. For the calcula-
tion of the average difference between the pollen collected by the
Pollensniffer and the static sampler, a regression analysis was per-
formed using the counts of all individual pollen types.
For the comparison of the 5 individual Pollensniffers the mean and
standard deviations of the 8 measurements were calculated. A one
way analysis of variance (ANOVA) was performed on the log trans-
formed data, since the log transformed data of this experiment were
normally distributed according to the Shapiro-Wilk test.
For the comparison between rooftop level and street level measure-
ments, the daily concentrations of the static sampler were compared to
the sum of the street level measurements of the three locations in the
morning, the early afternoon and the early evening (the sum of 9 mea-
surements per day). In order to compare the timing of the rst seasonal
pollen collected by the roof top static sampler and of the rst seasonal
pollen collected by Pollensniffer at street level, we compared the day
at which more than 3 grains/m
were collected by the static sampler
and when more than 3 grains were collected by the Pollensniffer. This
is an arbitrary threshold with the rationale that 1 or 2 pollen can be co-
incidental and a count of 4 pollen or more is more robust.
All statistical analyses were performed using the statistical software
package STATA 14.2 (StataCorp, TX, USA).
3. Results
3.1. General use of the Pollensniffer
The air ow through the Pollensniffers appeared to be very stable
during the operating time of the power bank (56 h). Using an elec-
tronic air ow sensor, the air ow was measured in 5 individual
Pollensniffers (Supplement Table S2.) The air ow appeared to depend
on the shape of sample holder used. The sample holder was slightly
modied in Pollensniffers PS2-PS5 resulting in an increase in air ow
through the device from 7.5 l/min to 8.39.2 l/min.
The user-friendliness of the Pollensniffer was evaluated by a panel of
volunteers in the northern part of the Netherlands. None of participants
considered the Pollensniffer was complicated; 81% said it was easy to
learn how to use the Pollensniffer and 50% indicated they would like
to use the Pollensniffer more often. The major concerns on the
Pollensniffer were related to the noise produced during operation (too
much; 50% of participants), the size (too large; 14%) and the weight
(too heavy; 28%).
3.2. Validation of the Pollensniffer in comparison to the static sampler
When the Pollensniffer PS1 was mounted on top of the rain cover of
the static sampler, the number of pollen grains collected by the
Fig. 3. Scatterplotsof the pollen counts per hourof different pollenspecies from the staticsampler and thePollensniffer.For presentationpurposes the countswere log transformed.In each
plot Spearman's correlation coefcient (SCC) between the counts of different pollen types collected by the two samplers are shown. All correlation coefcients are signicant (pb.05).
5L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
Pollensniffer and the static sampler correlated signicantly for all pollen
types analysed (Fig. 3). For most types the Spearman's correlation coef-
cient was strong (N0.8) but for Cupressaceae, Salix and Corylus, this
was moderate (0.640.75). This experiment showed that for all types
the Pollensniffer collected more pollen grains than the static sampler.
To assess the magnitude of the difference between the two samplers,
a regression analysis between the pollen counts by the Pollensniffer
(PS) and by the static sampler (BU) was performed. This resulted in
the equation: PS = 5.766 BU + 2.721 (R
= 0.788). This regression
equation indicates that the counts of the Pollensniffer are on average
5.8 times per hour higher than those in the static sampler.
Validation of the 5 individual Pollensniffers.
Five individual Pollensniffers (PS1-PS5) were placed on the same
roof as the static sampler and were set up to collect eight 1 h-samples,
each at the same time. The individual Pollensniffers (PS2-PS5) were
comparedto the validated Pollensniffer PS1. The Spearman's correlation
coefcients between the pollen counts of the Pollensniffers PS2-PS5 and
Pollensniffer PS1 were all signicant (pb.05) and higher than 0.80.
(Supplement Fig. S3). The percentage standard deviation of the mean
values of the measurements with these ve Pollensniffers varied be-
tween 9.3 and 27.4% resulting in a 15.5% average variability (Supple-
ment Table S3). A one way analysis of variance (ANOVA) showed that
the pollen collected by the different Pollensniffers did not signicantly
differ[(F55,47) = 0.93, p=.601)].
Grass and birch pollen collected at different locations.
At three locations in the city of Leiden pollen were collected for
15 min using the Pollensniffer in the morning, the early afternoon and
the early evening. Concerning the variation in pollen counts (dynamics)
during the day and the variation in number of pollen counts at large,
similar results were observed for the three location on most days, al-
though there are some notable differences. The peak values for birch
pollen at the three locations were reached on the same monitoring
day, but the number of pollen collected at these three locations varied
notably on that day (Fig. 4a and b) i.e. in week 15, 2017 102 birch pollen
grains (square rooted value [RV] 10.1) in Kweeklust, 28 pollen grains
(RV 5.3) in the City Center and 39 pollen grains (RV 6.2) in Huigpark.
In 2018, these values were much higher; e.g. on April 18th (week 16)
2338 pollen grains (RV 48.4) in Huigpark and 556 (RV 23.6) in
Kweeklust and 312 (RV 17.6) in the City Center. This large difference
in peak values between the years was also reected in the annual pollen
integral of the rooftop birch pollen, which amounted to 4398 pollen
in 2018 compared to only 995 in 2017. Strikingly, in the City
Center without any birch trees in the street or close by, the pollen counts
were in the same range as collected in the parks, although as mentioned
before, the highest numbers of pollen were collected in the parks.
Also for grass pollen (Fig. 4c and d) the levels were similar in the City
Center to those in the parks although again the peak values were
reached in the parks: 174 in 2017 in the Huigpark week 22 (June 2nd)
and 209 in 2018 in Kweeklust week 23 (June 6th).
For grass pollen counts, the variations between the three locations
were observed more frequently than for birch pollen; e.g. in 2017,
week 22, a high number of grass pollen was collected in the Huigpark
(174) in the morning with low numbers in Kweeklust (33). In the
early evening it was the other way around: high numbers in Kweeklust
(105) and low numbers in Huigpark (39). In contrast to birch pollen,
peak values for grasses were not reached in the same week on the var-
ious locations: the Huigpark reached its peak value (178) in week 22
(June 1st) and Kweeklust (peak value 209) in week 23 (June 6th),
which was 5 days apart.
During rain periods the number of birch or grass pollen that were
collected at street level were usually low (see arrows in Fig. 4).
Comparison between street level and rooftop level pollen counts
in time.
For a comparison between the rooftop level and street level pollen,
we compared the daily pollen concentrations measured by the static
sampler on the roof of the LUMC (as pollen/m
) with the sum of the
pollen collected at three 15-min periods of the day and from three loca-
tions in the city (sum of 9 measurements). Fig. 5 shows that the pollen
counts at street level follow at large the rooftop level pollen concentra-
tions: on days with high pollen concentrations measured at rooftop
level also the street level pollen counts are high and vice versa. Never-
theless, exceptions can be found: e.g. April 12th 2017 (week 15)
shows high street level birchpollen counts while the rooftop concentra-
tions were low (Figs. 4aand5a).
We studied the timing of the rst pollen grains to be collected at
rooftop level and street level for birch pollen and grass pollen. In 2017,
this could not be studied for birch since the birch pollen season was al-
ready started before our series of measurements took off. In 2018, the
rst signicant (4) number of birch pollen grains was collected on
March 26th (19 birch pollen, see asterisk in Fig. 5b), while at rooftop
the pollen concentration reached a signicant level (4 pollen grains/
) on April 6th (6 pollen grains/m
). So at street level birch pollen
are detected 11 days earlier than on rooftop level (Fig. 5b).
For grass pollen at rooftop level (week 18) 7 pollen/m
were re-
corded on April 30th 2017, while at street level 18 days earlier: 15 pol-
len were collected on April 12th (week 15) (see asterisk in Fig. 5c). In
2018, the rst signicant number of pollen were collected at rooftop
level on May 2nd (4 pollen/ m
, 24 h), while 19 days earlier (13th
April, week 15) 5 pollen were collected at street level (see the asterisk
in Fig. 5d). This indicates that birch pollen and grass pollen can be de-
tected at street level 1 1/2 weeks and 23 weeks, respectively, earlier
than at rooftop level.
4. Discussion
Here we describe the developmentand validation of a portable sam-
pler, called Pollensniffer, which can be used to collect pollen in the di-
rect environment of patients. In this study we have used the
Pollensniffer to monitor grass and birch pollen grains present at street
level in the city of Leiden and we show large differences in quantities
between the three locations on different days and during the day. We
also show that birch pollen and grass pollen are present 1 1/
23 weeks earlier at street level (approximately 1 to 1.20 m)compared
to rooftop level (22 m), which is the monitoring height for daily pollen
monitoring with the static sampler.
First the Pollensniffer was validated in an experimental set up where
the Pollensniffer was mounted on top of the rain cover of the static sam-
pler. We showed that for most pollen types, the number collected by the
Pollensniffer and the static sampler correlated signicantly. The
Pollensniffer appeared to be very efcient, since it collected on average
5.8 times more pollen per hour than the static sampler. An explanation
for the high efciency of the Pollensniffer compared to the static sam-
pler might be found in the shape of the inlet. A pollen grain passing in
an air streamline just above the inlet of the static sampler may not be
drawn into the static sampler, while the wider conical inlet of the
Pollensniffer may still collect this pollen grain.
Several devices for personalized allergen measurements have been
described (Fiorina et al., 1997;Peel et al., 2014b;Renstrom et al.,
2002;Werchan et al., 2018;Yamamoto et al., 2007)buttheuser-
friendliness and the efcacy of these devices varies. Therefore, we de-
veloped the portable Pollensniffer in such a way that it can be carried,
and, if required, can also be mounted onto the steering wheel of a bicy-
cle, which will be described in another paper. Furthermore, the easily
replaceable power bank generates a constant ow which can be main-
tained for 56 h. The measurements were performed with theinlet fac-
ing the current wind direction. This is a relevant instruction since this
will help to generate a good ow into the sampler.
We asked 15 volunteers to evaluate our Pollensniffers, and the ma-
jority (81%) thought it was an easy-to-use device, which they would
like to use more often (50%). The test panel suggested some improve-
ments: (i) to reduce the noise during use (50%); (ii) to reduce the size
6L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
(14%); and (iii) to reduce the weight (28%). These suggestions will be
used when a next generation of Pollensniffers will be produced.
The pollen collected by the Pollensniffer and the static sampler at the
same location correlate signicantly and for most types the correlation
is strong (SCC N0.8). For Corylus, Cupressaceae and Salix this correlation
was moderate (SCC =0.64, 0.74, resp.). For Corylus, the pollen counts in
the static sampler were very low (b07 pollen) (Fig. 3) which will affect
the correlation. Molina et al. (Molina et al., 2010) found in a similar ex-
perimentalso a low correlation for Cupressaceae pollenbetween a per-
sonal sampler and a continuous sampler (both from Burkard). The
lower correlation for Cupressaceae and Salix might be caused by the
fact that both these grains form clumps (Cupressaceae especially
when they are broken) more often than other pollen types. These
clumps may introduce a bias in the counts.
Comparison of ve individual Pollensniffers shows that the number
of pollen collected bythe 5 devices are strongly correlated (Supplement
Fig. S3). Furthermore, the average standard deviation between the mea-
surements with the ve Pollensniffers was 15.5%, which is comparable
to the variation reported in 3 Hirst-type samplers placed 5 m apart, i.e.
20% of the pollen count (Buters et al., 2015). This shows that, although
the measurements of the ow through the devices may slightly differ
among the Pollensniffers, their performance in pollen collection is
The rst use of the Pollensniffers was to study the distribution of
grass and birch pollen in the environment of the patients and we choose
three locations at street level in the city of Leiden. One location in the
shopping street (City Center), one in a park within the boundaries
formed by the canals (Huigpark) and one more at the border of the
city (Kweeklust). The total daily pollen counts for birch and grasses fol-
low the pollen concentrations measured by the static sampler at rooftop
level, although exceptions occur e.g. April 12th 2017 (week 15) with
high pollen counts at street level and low pollen concentrations at roof-
top level. Higher birchpollen counts at street level may be caused by the
fact that in a 500 m circle around all three locations birch trees are pres-
ent. When these treesare blooming their pollen may be dispersed better
at street level and collected less at rooftop level.
The peak values for both birch and grass pollen were recorded in the
parks, but on most days the pollen counts were in the same order of
magnitude for the City Center and the parks, which may be striking
for the City Center location since in this shopping street no grasses or
birch trees were present. Only a few birch trees or well-maintained
grass areas were present within a circle of 500 m. This indicates that
birch and grass pollen can disperse very well within a city.
For birch trees, the temporal differences between the three locations
are very similar, although the number of pollen collected at the various
sites can differ markedly (Figs. 4a and b). The most striking differences
Fig. 4. Pollencounts in 2017 and 2018 at the different locations in the city of Leiden: City Center, Huigparkand Kweeklust. The numbers 123 indicatemorning (1), early afternoon (2) and
early evening (3). The numbers 1426 indicate the week number of the measurement. Arrows indicate the incidence of rain during the pollen collection. For presentation purposes the
birch pollen counts are rooted. Note that scale of the vertical axes is different. Figs. 4a and b represent birch pollen counts and Figs. 4c and d grass pollen.
7L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
are found between the parks; there are days with very high pollen
counts in one park and much lower pollen counts at the other 2 loca-
tions (Fig. 4a, week 15, 2017 and Fig. 4b, week 16, 2018). In 2017,
highest values of birch pollen were collected in Kweeklust, while in
2018 the highest values were collected in Huigpark. This may be due
to the fact that we only collected pollen one day in the week. Maybe
in 2017, most birch trees in Kweeklust were ready to bloom on the col-
lection day, while in 2018 the day of collection may have been the
blooming day of most birches near Huigpark.
Also for grasses the temporal differences are similar for the different
locations, althoughthe three locations showmore variation in time than
the birch pollen. Pollen counts can be high in Kweeklust and low in
Huigpark, but the other week it can be the other way around (Fig. 4d,
week 22 and 23). Or pollen counts are increasing during the day in
Kweeklust and decreasing in Huigpark during the day (e.g. Fig. 4c,
week 22). These marked differences in pollen counts between the loca-
tions may be caused by the fact that different grass species are present
near the Huigpark and Kweeklust, which may cause different dynamics
in pollen production. Another explanation may be that the mowing re-
gime differs among the different regions. Large differences in grass pol-
len distribution within a city has also been described by others (Hjort
et al., 2016;Skjøth et al., 2013). The grass pollen will be produced by
local urban sources, but since in the surroundings of Leiden also rural
and recreation areas are present, it cannot be excluded that also these
areas could be a source for the grass pollen detected in the city. How-
ever, several recent studies on street level measurements indicate that
mainly local sources affect the pollen levels in the city domain and
that during intense owering the grass pollen level is a local scale phe-
nomenon (Skjøth et al., 2013;Werchan et al., 2017).
These differences in pollen levels within a city may explain in part
the diversity in symptom severity among allergic patients and it will
hamper the development of accurate local p ollen forecasts. It will be im-
possible with currenttechniques to take these local differences into ac-
count for pollen forecasts, but knowledge about the uneven dispersion
of pollen in a city is relevant (Werchan et al., 2017).
The daily pollen monitoring takes place at rooftop level which is jus-
tied since the measurements should give an overview of the airborne
pollen types within a larger area. These rooftop level measurements
are used in most countries to inform the patients on the current pollen
levels. We donot question the relevance of these data, however it is also
important to know how these rooftop pollen concentrations relate to
the pollen counts at street level. This study shows that at street level
birch pollen was collected 1 1/2 week and grass pollen 23 weeks be-
fore those pollen types were collected at rooftop level.
In 1991, Rantio-Lethimäki was the rst to describe that detection of
grass pollen at street level occurs 2 weeks earlier than rooftop level
(Rantio-Lehtimäki et al., 1991). Bastl et al. found a longer duration of
the grass pollen season at street level compared to rooftop level (29
and 34 days in 2015 and 2016, respectively; (Bastl et al., 2019)). These
results indicate that patients may be affected by the pollenbefore pollen
are detected by the samplers at rooftop level. The pollen collected at the
street level locations in the early season were quite low (see results),
Fig. 5. At rooftoplevel daily pollen concentrationswere monitored duringthe whole year (blue line).At street level the pollen weremonitored once a week on variable days and theyare
shown as the sum of the collected pollen at the tree locations during the morning, early afternoon and early evening (red bars). Green triangles indicate the day at which a street level
measurement was performed. Asterisks indicate days with more than 4 pollen collected at streetlevel before pollen are detected at rooftop levelby the static sampler. Figs. 5a and b
represent birch pollen in 2017 and 2018 respectively. The street level counts of 2018 are rooted for presentation purposes. Figs. 5c and d represent grass pollen in 2017 and 2018
respectively. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
8L.A. de Weger et al. / Science of the Total Environment 741 (2020) 140404
but we have to realize that pollen were collected during only 15 min at
each location. Nevertheless, patients for instance going by bike or on
foot to work or doing exercise in the parksthat stay out for a longer pe-
riod, may inhale sufcient pollen to develop symptoms. Allergic pa-
tients report to their physician or allergist that they experience
symptoms before the pollen are reported by the daily pollen concentra-
tions (Pfaar et al., 2017)
5. Conclusions
A new portable pollen sampler, called Pollensniffer, has been devel-
oped, validated against the static pollen sampler and which is approxi-
mately 56 times more efcient in collecting pollen. This Pollensniffer
has been used to monitor birch and grass pollen at street level. The pol-
len levels correlate well with the pollen levels at rooftop, but there can
be large differences in pollen levels in a city between different locations
and different 15-minute periods during the day. Furthermore, at the
start of the birch and the grass pollen season, pollen were collected 1
1/2 to 23 weeks respectively earlier at street level compared to rooftop
level, explaining why patients can have symptoms before the pollen
season ofcially has started as dened by pollen counts in the rooftop
CRediT authorship contribution statement
Letty A. de Weger:Conceptualization, Methodology, Formal analysis,
Writing - original draft, Writing - review & editing.Frank Molster:Writ-
ing - review & editing, Conceptualization. Kevin de Raat:Investigation,
Writing - review & editing.Jeffrey den Haan:Investigation, Writing - re-
view & editing.Johan Romein:Writing - review & editing, Conceptuali-
zation. Willem van Leeuwen:Conceptualization, Supervision, Writing
- review & editing.Hans de Groot:Conceptualization, Writing - review
& editing.Marijke Mostert:Conceptualization, Supervision, Project ad-
ministration, Writing - review & editing.Pieter S. Hiemstra:Conceptual-
ization, Supervision, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inu-
ence the work reported in this paper.
The study was supported in part by a grant from Generade (grant
2016002D12) and from Taskforce for Applied Research (RAAK
PUB03.045), and the testing of the Pollensniffers on Festivalderaa was
supported by an Innofest grant. Wewould like to acknowledge the help
of Dennis Hengst, Martijn Tenge,en Stefan Elfering for their work on the
development of the Pollensniffer, Sam Luijben and Raheel Gill for tech-
nical assistance and pollen sampling. Roel Schellandfor his support dur-
ing the grant application, and Kenji Miki from Kyoto University, Kyoto
for advising us on the aerobiological properties of the Pollensniffer.
We thank Emiel Hilgersom from the Leiden Municipality for the infor-
mation on the birch tree species and the maintenance of roadsides
and meadows.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
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... Although the city is situated in the most populated part of the Netherlands, to the east the surroundings consist of polderlands. The streets in the city are lined with alder, birch, ash, plane, willow, maple, horse chestnut, oak, or poplar (31). From 1991 to 2000 close to the sampler a stretch of waste land was present where old hospital buildings had been demolished. ...
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Airborne pollen is a major cause of allergic rhinitis, affecting between 10 and 30% of the population in Belgium, the Netherlands, and Luxembourg (Benelux). Allergenic pollen is produced by wind pollinating plants and released in relatively low to massive amounts. Current climate changes, in combination with increasing urbanization, are likely to affect the presence of airborne allergenic pollen with respect to exposure intensity, timing as well as duration. Detailed analysis of long-term temporal trends at supranational scale may provide more comprehensive insight into these phenomena. To this end, the Spearman correlation was used to statistically compare the temporal trends in airborne pollen concentration monitored at the aerobiological stations which gathered the longest time-series (30–44 years) in the Benelux with a focus on the allergenic pollen taxa: Alnus, Corylus, Betula, Fraxinus, Quercus, Platanus, Poaceae, and Artemisia. Most arboreal species showed an overall trend toward an increase in the annual pollen integral and peak values and an overall trend toward an earlier start and end of the pollen season, which for Betula resulted in a significant decrease in season length. For the herbaceous species (Poaceae and Artemisia), the annual pollen integral and peak values showed a decreasing trend. The season timing of Poaceae showed a trend toward earlier starts and longer seasons in all locations. In all, these results show that temporal variations in pollen levels almost always follow a common trend in the Benelux, suggesting a similar force of climate change-driven factors, especially for Betula where a clear positive correlation was found between changes in temperature and pollen release over time. However, some trends were more local-specific indicating the influence of other environmental factors, e.g., the increasing urbanization in the surroundings of these monitoring locations. The dynamics in the observed trends can impact allergic patients by increasing the severity of symptoms, upsetting the habit of timing of the season, complicating diagnosis due to overlapping pollen seasons and the emergence of new symptoms due allergens that were weak at first.
Airborne birch pollen may elicit allergies and affect the public health badly. Timely spatially distributed information on current and forecasted pollen levels may help people with pollen allergies to take preventive measures. This requires a modelling approach. Here we reconstruct multi-decadal (1982–2019) daily spatially distributed airborne birch pollen levels by ingesting seasonal dynamic birch pollen emission source maps into the pollen transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) in a bottom-up approach. We introduce seasonal variations in the birch pollen emission maps by combining a forest inventory based areal birch fraction map with four decades of spaceborne Normalized Difference Vegetation Index (NDVI) in a Random Forest statistical framework. The approach of combining the transport model with NDVI based pollen emission maps is applied and evaluated with birch pollen observations by Hirst method from the Belgian aerobiological surveillance network that go back to 1982. Transport in SILAM is driven by ECMWF ERA5 meteorological data. The mean seasonal R² values between modelled and observed time series of airborne birch pollen levels in the period 1982–2019 range between 0.35 and 0.63, but can go up to 0.86 for individual seasons, indicating good performance of SILAM for Belgium. Here we show that the predicted amount of birch pollen in the air in Belgium has been increasing on average by 13.1% per decade based on the Sen slopes computed on the Seasonal Pollen Integral for the period 1982-2019. Analysis of the SILAM runs shows that this increase over time is mainly climate-induced (8.2% per decade), but it is amplified by the spatiotemporal variations of the birch pollen emission sources with 4.9% per decade.
Purpose of review: The development and progression of chronic respiratory diseases are impacted by a complex interplay between genetic, microbial, and environmental factors. Here we specifically summarize the effects of environmental exposure on asthma, allergic rhinitis, and chronic rhinosinusitis. We furthermore discuss how digital health technology may aid in the assessment of the environmental exposure of patients and how it may be of added value for them. Recent findings: It is well established that one gets allergic symptoms if sensitized and exposed to the same allergen. Viruses, bacteria, pollutants, irritants, and lifestyle-related factors modify the risk of getting sensitized and develop symptoms or may induce symptoms themselves. Understanding these processes and how the various factors interact with each other and the human body require big data and advanced statistics. Mobile health technology enables integration of multiple sources of data of the patients' exposome and link these to patient outcomes. Such technologies may contribute to the increased understanding of the development of chronic respiratory disease. Summary: Implementation of digital technologies in clinical practice may in future guide the development of preventive strategies to tackle chronic respiratory diseases and eventually improve outcomes of the patient.
Here we describe an automated and compact pollen detection system that integrates enrichment, in-situ detection and self-cleaning modules. The system can achieve continuous capture and enrichment of pollen grains in air samples by electrostatic adsorption. The captured pollen grains are imaged with a digital camera, and an automated image analysis based on machine vision is performed, which enables a quantification of the number of pollen particles as well as a preliminary classification into two types of pollen grains. In order to optimize and evaluate the system performance, we developed a testing approach that utilizes an airflow containing a precisely metered amount of pollen particles surrounded by a sheath flow to achieve the generation and lossless transmission of standard gas samples. We studied various factors affecting the pollen capture efficiency, including the applied voltage, air flow rate and humidity. Under optimized conditions, the system was successfully used in the measurement of airborne pollen particles within a wide range of concentrations, spanning 3 orders of magnitude.
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Background: It is recommended to position pollen monitoring stations on rooftop level to assure a large catchment area and to gain data that are representative for a regional scale. Herein, an investigation of the representativeness of pollen concentrations was performed for 20 pollen types in the pollen seasons 2015-2016 in Vienna for rooftop and ground level and was compared with weather data and for the first time with symptom data. Methods: The complete data set was analyzed with various statistical methods including Spearmen correlation, ANOVA, Kolmogorov-Smirnov test and logistic regression calculation: Odds ratio and Yule's Q values. Computational intelligence methods, namely Self Organizing Maps (SOMs) were employed that are capable of describing similarities and interdependencies in an effective way taking into account the U-matrix as well. The Random Forest algorithm was selected for modeling symptom data. Results: The investigation of the representativeness of pollen concentrations on rooftop and ground level concerns the progress of the season, the peak occurrences and absolute quantities. Most taxa examined showed similar patterns (e.g. Betula), while others showed differences in pollen concentrations exposure on different heights (e.g. the Poaceae family). Maximum temperature, mean temperature and humidity showed the highest influence among the weather parameters and daily pollen concentrations for the majority of taxa in both traps. Conclusion: The rooftop trap was identified as the more adequate one when compared with the local symptom data. Results show that symptom data correlate more with pollen concentrations measured on rooftop than with those measured on ground level.
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Abstract Background Ambient air quality monitoring is a governmental duty that is widely carried out in order to detect non-biological (“chemical”) components in ambient air, such as particles of 500). The prevalent monitoring method is based on the Hirst principle (> 600 stations). The inventory is visualised as an interactive and on-line map. It can be searched, its appearance can be adjusted to the users’ needs and it is updated regularly, as new stations or changes to those that already exist can be submitted online. Conclusions The map shows the current situation of pollen and spore monitoring and facilitates collaboration among those individuals who are interested in pollen and spore counts. It might also help to improve the monitoring of biological particles up to the current level employed for non-biological components.
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Aerobiology is an interdisciplinary science where researchers with different backgrounds are involved in different topics related to microorganisms, airborne biological particles, e.g. pollen and spores, and phenology. Some concepts, words or expressions used in aerobiology have a clear definition, but are, however, frequently misused. Therefore, the working group ‘‘Quality Control’’ of the European Aerobiology Society (EAS) and the International Association of Aerobiology (IAA) would like to clarify some of them, their use and presentation.
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For nearly a decade, the majority of the world’s population has been living in cities, including a considerable percentage of people suffering from pollen allergy. The increasing concentration of people in cities results in larger populations being exposed to allergenic pollen at the same time. There is almost no information about spatial distribution of pollen within cities as well as a lack of information about the possible impact to human health. To obtain this increasing need for pollen exposure studies on an intra-urban scale, a novelty screening network of 14 weekly changed pollen traps was established within a large metropolitan area—Berlin, Germany. Gravimetric pollen traps were placed at a uniform street-level height from March until October 2014. Three important allergenic pollen types for Central Europe—birch (Betula), grasses (Poaceae), and mugwort (Artemisia)—were monitored. Remarkable spatial and temporal variations of pollen sedimentation within the city and the influences by urban local sources are shown. The observed differences between the trap with the overall highest and the trap with the overall lowest amount of pollen sedimentation were in the case of birch pollen 245%, grass pollen 306%, and mugwort pollen 1962%. Differences of this magnitude can probably lead to different health impacts on allergy sufferers in one city. Therefore, pollen should be monitored preferably in two or more appropriate locations within large cities and as a part of natural air quality regulations.
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Standardisation of methods of pollen monitoring networks is vital for data quality. In pollen monitoring networks in Europe, the Hirst-type trap is standard. Hirst traps are calibrated with handheld rotameters. We detected a systematic error in the flow rate calibrated by these standard handheld rotameters. We measured the flow rate of 19 Hirst traps from three commercial brands during calibration but also during monitoring. We used three different rotameters supplied by the manufacturers of the traps, respectively. The actual air flow rate was measured using an electronic heat anemometer with negligible air flow resistance. After calibration to 10 l/min, the rotameter was removed, which led to a significant increase in the flow rate in the range of 10.5–17.2 l/min, a systematic error between 5 and 72%. No significant difference was found between the different commercial trap brands. The analysis revealed that the error depended on the type of the rotameter and the individual trap. The error may be explained by the additional air flow resistance of each rotameter. The total resistance of the system—trap plus rotameter—is higher during calibration when the rotameter is held on the inlet compared to the routine monitoring without the rotameter. Depending on the characteristic curve of the suction pump in the trap (fan), the air flow rate increases to values considerably higher than 10 l/min. Thus, monitoring is done under a higher flow rate than that was calibrated. In order to obtain comparable data within a monitoring network, a solution for correction of this systematic error seems advisable, preferably in cooperation with the manufacturers.
The effect of height on pollen concentration is not well documented and little is known about the near-ground vertical profile of airborne pollen. This is important as most measuring stations are on roofs, but patient exposure is at ground level. Our study used a big data approach to estimate the near-ground vertical profile of pollen concentrations based on a global study of paired stations located at different heights. We analyzed paired sampling stations located at different heights between 1.5 and 50 m above ground level (AGL). This provided pollen data from 59 Hirst-type volumetric traps from 25 different areas, mainly in Europe, but also covering North America and Australia, resulting in about 2,000,000 daily pollen concentrations analyzed. The daily ratio of the amounts of pollen from different heights per location was used, and the values of the lower station were divided by the higher station. The lower station of paired traps recorded more pollen than the higher trap. However, while the effect of height on pollen concentration was clear, it was also limited (average ratio 1.3, range 0.7-2.2). The standard deviation of the pollen ratio was highly variable when the lower station was located close to the ground level (below 10 m AGL). We show that pollen concentrations measured at >10 m are representative for background near-ground levels.
Background: Despite the socioeconomic importance of allergic rhinitis (AR), very few prospective studies have been performed under conditions of routine clinical practice and with a sufficiently long observation period outside the clinical trial scenario. We prospectively estimated the direct and indirect costs of AR in patients attending specialized clinics in Spain. Methods: Patients were recruited at random from allergy outpatient clinics in 101 health centers throughout Spain over 12 months. We performed a multicenter, observational, prospective study under conditions of routine clinical practice. We analyzed direct costs from a funder perspective (health care costs) and from a societal perspective (health care and non-health care costs). Indirect costs (absenteeism and presenteeism [productivity lost in the workplace]) were also calculated. The cost of treating conjunctivitis was evaluated alongside that of AR. Results: The total mean cost of AR per patient-year (n=498) was €2,326.70 (direct, €553.80; indirect, €1,772.90). Direct costs were significantly higher in women (€600.34 vs. €484.46, P=0.02). Total costs for intermittent AR were significantly lower than for persistent AR (€1,484.98 vs €2,655.86, P<0.001). Total indirect costs reached €1,772.90 (presenteeism, €1,682.71; absenteeism, €90.19). The direct costs of AR in patients with intermittent asthma (€507.35) were lower than in patients with mild-persistent asthma (€719.07) and moderate-persistent asthma (€798.71) (P=0.006). Conclusions: The total cost of AR for society is considerable. Greater frequency of symptoms and more severe AR are associated with higher costs. Indirect costs are almost 3-fold direct costs, especially in presenteeism. A reduction in presenteeism would generate considerable savings for society. This article is protected by copyright. All rights reserved.
Background: Clinical efficacy of pollen allergen immunotherapy has been broadly documented in randomized controlled trials. The underlying clinical endpoints are analysed in seasonal time periods pre-defined on the basis of the background pollen concentration. However, any validated or generally accepted definition from academia or regulatory authorities for this relevant pollen-exposure intensity or period of time (season) is currently not available. Therefore, this Task Force initiative of the European Academy of Allergy and Clinical Immunology (EAACI) aimed to propose definitions based on expert-consensus. Methods: A Task Force of the Immunotherapy and Aerobiology and Pollution Interest Groups of the EAACI reviewed the literature on pollen-exposure in the context of defining relevant time intervals for evaluation of efficacy in allergen immunotherapy trials. Underlying principles in measuring pollen exposure and associated methodological problems and limitations were considered in order to achieve a consensus. Results: The Task Force achieved a comprehensive position in defining pollen exposure times for different pollen types. Definitions are presented for 'pollen season', 'high pollen season' (or 'Peak pollen period') and 'high pollen days'. Conclusion: This EAACI position paper provides definitions of pollen exposures for different pollen types for use in Allergen Immunotherapy trials. Their validity as standards remains to be tested in future studies. This article is protected by copyright. All rights reserved.