Is the recent decrease in airborne Ambrosia pollen in the Milan area due to the accidental

15 Pages·2015·0.39 MB·English

by  M. Bonini

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Ambrosia
Is the recent decrease in airborne pollen
in the Milan area due to the accidental introduction
Ophraella communa
of the ragweed leaf beetle ?
M. Bonini . B. Sˇikoparija . M. Prentovic´ . G. Cislaghi .
. . . .
P. Colombo C. Testoni L. Grewling S. T. E. Lommen
H. Mu¨ller-Scha¨rer . M. Smith
Abstract This study aims to determine whether a pollendata(2000–2013)wereexaminedfortrendsover
h significantdecrease inairborne concentrations of Am- time and correlated with meteorological data. The
c brosia pollen witnessed in the north-west of the amount of Ambrosia pollen recorded annually during
. ProvinceofMilaninNorthernItalycouldbeexplained the main flowering period of Ambrosia (August–
o
by environmental factors such as meteorology, or September) was entered into linear regression models
r whetherthereisevidencetosupportthehypothesisthat withmeteorologicaldatainordertodeterminewhether
e
the decrease was related to the presence of large the amount of airborne Ambrosia pollen recorded in
r
. numbersoftheoligophagousOphraellacommunaleaf 2013waslowerthanwouldnormallybeexpectedbased
c
beetlesthatareusedasabiologicalcontrolagentagainst on the prevailing weather conditions. There were a
o
Ambrosiainotherpartsoftheworld.Airborneconcen- number of significant correlations between concentra-
d
trations of Ambrosia, Cannabaceae and Urticaceae tions of airborne Ambrosia, Cannabaceae and Urti-
/ caceae pollen, as well as between airborne pollen
/
: concentrations and daily and monthly meteorological
p
t M.Bonini(&)(cid:2)G.Cislaghi(cid:2)P.Colombo(cid:2)C.Testoni data.Thelinearregressionmodelsgreatlyoverestimat-
t DepartmentofMedicalPrevention,PublicHealth,Local edtheamountofairborneAmbrosiapollenin2013.The
h
HealthAuthorityofMilan1,Parabiago(Mi),Italy resultsoftheregressionanalysissupportthehypothesis
e-mail:[email protected]
thattheobserveddecreaseinairborneAmbrosiapollen
B.Sˇikoparija(cid:2)M.Prentovic´ mayindeedberelatedtothepresenceoflargenumbers
LaboratoryforPalynology,DepartmentofBiologyand ofO.communaintheMilanarea,asthedrasticdecrease
Ecology,FacultyofSciences,UniversityofNoviSad, inairborneAmbrosiapollenin2013cannotbeexplained
NoviSad,Serbia
bymeteorologyalone.
M.Prentovic´
DepartmentofOto-Rhino-Laryngology,ResearchGroup Keywords Aerobiology(cid:2)Ragweed(cid:2)Ophraella
AerobiologyandPolleninformation,MedicalUniversity communa(cid:2)Biocontrolagent
ofVienna,Vienna,Austria
L.Grewling(cid:2)M.Smith
LaboratoryofAeropalynology,FacultyofBiology,Adam 1 Introduction
MickiewiczUniversity,Poznan´,Poland
AmbrosiamaritimaL.istheonlyAmbrosia(ragweed)
S.T.E.Lommen(cid:2)H.Mu¨ller-Scha¨rer
species native to Europe; three other species of
DepartmentBiology,UniversityofFribourg,Fribourg,
Switzerland Ambrosia(A.psilostachya,A.tenuifoliaandA.trifida)
1
were introduced to Europe from North America in should be considered a troublesome introduction or
shipmentsofagriculturalproducts(Smithetal.2013). whether it is likely to become the first case of a
Of these introduced species, the invasive alien successful biological control of an invasive weed in
Ambrosia artemisiifolia L. (common or short rag- continentalEurope.
weed) is the most widely distributed and as such is Ophraella communa is multivoltine in that it
considered to be an import weed in agriculture and produces three or four generations per year, with an
source of highly allergenic pollen, which causes egg-to-egg generation time of about 4 weeks (Futuy-
symptoms of pollen allergy in late summer and maetal.1993).Femaleslayeggseveryfewdaysfora
autumn and reportedly induces asthma about twice month or more, with the fecundity exceeding 2700
as often as other pollen types (Dahl et al. (1999) and eggs/female(Futuymaetal.1993;Zhouetal.2010b).
referencestherein). Thesurvivalrateofeggsandlarvaeishigher,andthe
TheRhoˆneValley(France),partsofNorthernItaly, development periods shorter, when temperature and
the Pannonian Plain (i.e. predominantly Hungary but humidityareincreased(Zhouetal.2010a,b;Zhuetal.
also parts of Austria, Bosnia and Herzegovina, Croa- 2012).Boththelarva andimagofeedonleavesofA.
tia, Czech Republic, Romania, Serbia, Slovakia and artemisiifolia (Wan et al. 2009). O. communa can
Slovenia) and Ukraine are considered to be the most significantly suppress plantheightandthe numberof
important sources of Ambrosia pollen in Europe branches(Guoetal.2011),andwhenbeetledensities
(Smith et al. (2013) references therein). Ambrosia are high enough, it can even kill all Ambrosia plants
h
artemisiifolia is an anemophilous (wind pollinated) (Zhou et al. 2014), thus preventing the production of
c
plant, the pollen grains of which can readily become seeds. Required densities are higher when plants are
.
o airborne when conditions are favourable. As with bigger,butevenplantsthatwereonlyexperimentally
r otherwind-pollinatedplants,suchasthosebelonging exposed to the beetles when they were already over
e
to the Cannabaceae and Urticaceae families that 90 cm tall were successfully killed prior to seed
r flower at around the same time as Ambrosia, pollen production (Zhou et al. 2014). In addition, O.
.
c releaseanddispersalisaidedbyhightemperatureand communa has high dispersal potential (Tanaka and
o low relative humidity (Dahl et al. (2013) and refer- Yamanaka 2009). The flight distance has been
d encestherein). estimated to be approximately 25 km/day, with
/ Ophraella communa LeSage 1986 (Coleoptera: potentialrangeexpansionof329 km/year(Yamamura
/
: Chrysomelidae) is an oligophagous leaf beetle that etal.2007;TanakaandYamanaka2009).Takinginto
p
feedsonvariousplantsspecies belongingtothetribe account the selective host-feeding preferences, high
t Heliantheae (Asteraceae), including A. artemisiifolia fecundity and dispersal potential, it is suggested that
t
h (FutuymaandMCCafferty1990;PalmerandGoeden O. communa may be a potential biological control
1991). The beetle originates from North America agent against A. artemisiifolia (Teshler et al. 2002)
(Futuyma and MCCafferty 1990) and has been althoughOphraellaslobodkiniwasapreferredcandi-
(accidentally) introduced to a number of other areas date species for the biological control of A. artemisi-
worldwide, including Europe, where it was first ifolia in Europe, because it exclusively feeds on
detectedin2013(Takizawaetal.1999;MengandLi Ambrosia(Gerberetal.2011).
2005; Mu¨ller-Scha¨rer et al. 2014). Despite extensive Ophraellacommunabeetleswereobservedfeeding
host-specificity tests carried out over the past two onAmbrosiaartemisiifoliaplantsinseveralregionsof
decades (reviewed by Zhou et al. (2011)), there Northern Italy, especially near Milan (Bosio et al.
remains a controversial debate mainly on whether 2014;Mu¨ller-Scha¨reretal.2014),duringthesummer
the beetle can attack and damage sunflower, of2013.Thebeetlewasfoundonallobservedragweed
HelianthusannuusL.,inthefield(cf.Mu¨ller-Scha¨rer populations. Individual A. artemisiifolia plants har-
etal.(2014)andreferences therein).Therefore,host- boureduptooverahundredbeetles,andmanyplants
specificity tests both under quarantine and open-field were completely defoliated before the end of the
conditions in Switzerland and Northern Italy and season(Mu¨ller-Scha¨reretal.2014).Atthesametime,
including various sunflower varieties and Ambrosia it wasnoticed thatroutine pollen-monitoring stations
species are presently underway (H. Mu¨ller-Scha¨rer, situated in three towns of the north-west area of the
unpublished results) to decide on whether this beetle Province of Milan were recording less Ambrosia
2
pollen than usual. This study aims to determine examined, Ambrosia, Cannabaceae and Urticaceae,
whethertheobserveddecreaseinairborneconcentra- areroutinelymonitored.
tions of Ambrosia pollen can be explained by The amount of Ambrosia pollen recorded annually
environmental factors such as meteorology that are duringthemainfloweringperiodofAmbrosia(August–
knowntostronglyaffectpollenreleaseanddispersal, September) is presented (Annual Ambrosia Pollen—
orwhetherthereisevidencetosupportthehypothesis AAP).ThemeanAAPofallthreesitescombinedwas
that the decrease was related to the presence of O. alsoexaminedandtermed‘‘Milanarea’’.Inaddition,the
communainthearea. amountsofCannabaceaepollen(AnnualCannabaceae
Pollen—ACP) and Urticaceae pollen (Annual Urti-
caceaePollen—AUP)recordedduringAugust–Septem-
2 Materialsandmethods beratLegnanowereincludedinthestudyasthesetaxa
containspeciesthatflowerinAugust–September.Asa
2.1 Airbornepollendata result,theamountofthesepollentypesintheaircanbe
assumed to be influenced by similar meteorological
Dailyaveragepollenconcentrationswerecollectedat factorsasAmbrosia.Wethereforehypothesisedthatif
three sites in the north-west area of the Province of airborne Cannabaceae and Urticaceae pollen concen-
Milan, Northern Italy, from 2000 to 2013 (Fig. 1). trationsdonotfollowasimilartrendasAmbrosiapollen,
Pollendatawerecollectedbyvolumetricsporetrapof i.e.theACPandAUPdidnotdecreaseinthesameway
h
the Hirst design (Hirst 1952). The pollen-monitoring as AAP, then weather conditions during the period of
c
sites were the towns of Legnano (ITMIL6), Magenta pollenreleaseareunlikelytobethemainfactoraffecting
.
o (ITMAGE)andRho(ITRHO1).Legnanoistheclosest airborne Ambrosia pollen concentrations. This would
r pollen-monitoring site to the meteorological stations, provide support for the hypothesis that the observed
e
andsopollendatafromthissitewereusedthroughout decreaseinAAPwasduetothepresenceofO.communa
r theanalysis.Legnanoisalsotheonlypollen-monitor- inthearea,ratherthanmeteorology.Ontheotherhand,
.
c ing site in this study where all three pollen types concurrent decreases in all three pollen types would
o
d
/
/
:
p
t
t
h
Fig.1 Positionofthepollen-monitoringsitesandmeteorologystationsintheMilanregion(Italy)
3
suggest that either conditions during August and applied; where data were not normally distributed,
September2013 weregenerally not conducive for the nonparametric statistics were used (Sterling et al.
release and dispersal of anemophilous pollen or that 1999).
otherfactorswhichcanaffecttheamountofpolleninthe Annual variations in the amount ofairborne pollen
airfromthesethreepollentypesshouldbeconsidered. recordedduringAugustandSeptemberwerenormally
Withthisinmind,thefollowingshouldbenoted:(1) distributed.Analysisofvariance(ANOVA)wasusedto
Cannabaceae and Urticaceae species tend to grow in determinewhethertherewasasignificantdifferencein
similarhabitatsasAmbrosia(e.g.Cannabisonruderal themeanAAPrecordedatthethreesites(2000–2013).
habitatsandagriculturalland,andUrtica/Parietariaon Also, Pearson’s correlation analysis was carried out
ruderalhabitats);(2)Allhost-rangetestsconductedso betweenAAPrecordedatallthreesitesincludedinthe
far indicate that O. communa is specific to the Aster- study,aswellasbetweenAAP,ACPandAUPrecorded
aceae tribe Heliantheae (Palmer and Goeden 1991; at Legnano. Trends in the amount of Ambrosia,
Gerber et al. 2011). As a result, Cannabaceae and Cannabaceae and Urticaceae pollen recorded in three
UrticaceaepopulationscouldbeaffectedbyAmbrosia periods(2000–2011,2000–2012and2000–2013)were
eradicationmeasuresintheMilanarea,andconcurrent examinedusing simplelinearregression analysis.The
decreases in airborne Cannabaceae and Urticaceae followingstatisticsarepresented:theslopeofthesimple
pollenareunlikelytobecausedbybeetleattacks. linear regression over time, standard error of the
Dailyaveragepollenconcentrationsareexpressedas regressionslope(SE)andprobabilitylevel(p).
h
particlespercubicmetreofair(P m-3)(Comtois1998). Conversely, daily variations in airborne pollen and
c
The amount of pollen recorded during August and variations in meteorological data (daily and monthly)
.
o September(AAP,ACPandAUP)ispresentedasgrains. were often not normally distributed. As a result,
r Spearman’s rank correlation analysis was used to
e
2.2 Meteorologicaldata examinerelationshipsbetweenthefollowing:(1)daily
r average concentrations of airborne Ambrosia pollen
.
c Meteorological data were collected at Malpensa with daily average concentrations of airborne Canna-
o airport and accessed via http://www.tutiempo.net. baceae and Urticaceae pollen; (2) daily average con-
d The following daily meteorological variables were centrations of airborne Ambrosia, Cannabaceae and
/ examined:T,meantemperature((cid:2)C);T ,maximum Urticaceae pollen with all of the daily meteorological
max
:/ temperature((cid:2)C);Tmin,minimumtemperature((cid:2)C);H, data listed in Sect. 2.2 (not wind direction); (3) AAP
p
mean humidity (%); PP, precipitation amount (mm); with monthly meteorological data recorded from Jan-
t V,meanwindspeed(Km/h);VM,maximumsustained uary through to September in the year of pollination.
t
h wind speed (Km/h); RN, indicator for occurrence of The latter was used to determine whether there was a
rainordrizzle;SN,indicatorforoccurrenceofsnowor relationshipbetweentheamountofairborneAmbrosia
ice Pellets; FG, indicator for occurrence of fog. Me- pollenrecordedinAugustandSeptemberandmeteoro-
teorological data for Malpensa airport were not logical variables likely to influence the production,
availableforanalysis:2002—26–31.05.2002;2002— releaseanddispersalofAmbrosiapollen.Themonthly
18–21.06.2002; 2005—13–30.11.2005; 2007— meteorologicaldatawereasfollows:monthlyaverages
28.8.2007; 2008—18.8.2008; 2013—1–31.12.2013. of daily temperature (daily minimum, mean and
In addition, daily average wind direction data were maximum temperatures), daily average relative hu-
obtained from Busto Arsizio meteorological station, midityanddailyprecipitation;themonthlysumofdaily
which is situated close to Legnano (ITMIL6) pollen- precipitation; the number of days with rain, fog and
monitoring station and the meteorological station at drizzle recorded in each month. In addition, two
Malpensaairport(Fig. 1). monthly sumsand averagesofprecipitation, humidity
and temperature were also entered into correlation
2.3 Statisticalanalysis analysis with AAP. Data from 2013 were not entered
into Spearman’s rank correlation analysis, as this was
Data were examined for normality using the Kol- theyearunderinvestigation(2000–2012only).
mogorov–Smirnovtest.Wheredatawerefoundtobe Simplelinearandmultiplelinearregressionmodels
normally distributed, parametric statistics were were constructed in order to predict AAP in ‘‘below
4
average’’,‘‘aboveaverage’’and‘‘lowintensity’’years using data that were characteristic of both low and
(Sect. 2.4) recorded at Legnano, which is the site high seasons (but not the years with the lowest and
closest to the meteorological station at Malpensa highestAAPinthedataset).Forthisreason,theAAP
airport(Fig. 1).Thejustificationforthisextraanalysis in each year was categorised as being ‘‘below aver-
is that if the models were unable to satisfactorily age’’ or ‘‘above average’’. The years with the mean
predicttheAAPin2013,thenthiswouldaddweightto AAP in each group (e.g. the mean of all ‘‘below
the argument that the observed decrease in airborne average’’ years) were selected for validation. The
concentrations of Ambrosia pollen cannot be ex- 2 years selected were 2010 (mean of all ‘‘below
plained by meteorology and add support to the average’’ years) and 2009 (mean of all ‘‘above
hypothesis that the decrease was related to the average’’ years). The regression models were then
presence of O. communa in the area. Independent validated using the AAP from selected validation
variables that were not normally distributed were years(2009and2010),aswellas2013.
normalised using the LOG10 function (Stach et al. The models were evaluated by examining: (a) the
2008). Spearman’s correlation analysis was then adjustedcoefficientofdetermination(Adj.R2),which
repeated, in order to ensure that normalisation had isconsidered more appropriate for evaluatingregres-
not notably altered the strength and direction of the sion models developed on small samples (Pallant
relationship between dependent and independent 2001); (b) the Kolmogorov–Smirnov test (normality
variables. of the residuals); (c) the Koenker–Basset test for
h
Tests were considered significant with probability heteroscedasticity,forviolationofhomogeneityofthe
c
levels\0.05. All calculations and statistical analysis residuals variance (Bickel 2012); (d) collinearity di-
.
o ofthedatawereconductedusingMicrosoftExceland agnostics were carried out by checking the variance
r SPSS12.0softwarepackages. inflationfactor(VIF)andcomputingtheMahalanobis
e
distance(Pallant2001).
r 2.4 Modelforpredictingannualvariations
.
c inAmbrosiapollen(AAP) 2.5 ExamininglandusechangeintheMilanarea
o
d Theindependentvariableswiththestrongestrelation- Common ragweed is a pioneer plant that is known to
/ ship with AAP were entered into simple linear and preferopen,disturbedsites(Essletal.2009)andtendsto
/
: multiple linear regression analysis. The regression grow in suitable habitats if two conditions are fulfilled
p
models were primarily constructed in order to inves- (Skjøth et al. 2010): (1) available seeds; (2) soil
t tigatewhethertheAAPin2013waslowerthanwould disturbance. For this reason, major construction works
t
h normallybeexpectedbasedontheprevailingweather intheMilanarea2000–2013wereexaminedinorderto
conditions.Theregressionmodelswerealsovalidated determinewhetherchangesinlandusecouldberelatedto
using data deemed to be characteristic of ‘‘below the decrease in airborne concentrations of Ambrosia
average’’(2010)and‘‘aboveaverage’’(2009)magni- pollenin2013.Informationaboutconstructionsiteswere
tude AAP years (note that 2013 recorded the lowest accessed via the Local Health Authority Milan 1,
AAP in the 2000–2013 data set and was therefore Department of Medical Prevention, UOC SPreSAL,
deemedtobea‘‘lowintensity’’year). Internal Archive. The ‘‘UOC SPreSAL’’ is the service
The regression models were calibrated using thatcontrolsconstructionintheMilanarea.Geographical
11 years of Ambrosia pollen and environmental data coordinateswerefoundbyusingGoogleMaps.
(2000–2008, 2011 and 2012). These years were not
included in the validation process. The models were
validated(tested)using2 yearsofdatanotincludedin 3 Results
model construction. Following the method described
byPrentovic´etal.(2014),itwasdecidednottotestthe 3.1 Trendsandrelationshipsintheamount
performance of the model using the two most recent ofairbornepollen
years of data (i.e. 2011 and 2012). This was because
both of these years were representative of low AAP There is great deal of variation in AAP recorded at
years. Instead, it was decided to validate the model stations situated in the north-west of the Province of
5
Milan (Table 1). The highest AAP was recorded in 3.2 Theinfluenceofmeteorologicaldata
2000, 2004 and 2009 in the Milan area (the highest onairbornepollenconcentrations
was2000atLegnanoandRhoand2004atMagenta).
The lowest AAP was recorded in 2013 at all three Daily average concentrations of airborne Ambrosia,
sites.NotethatthesecondlowestAAPwasrecordedin Cannabaceae and Urticaceae pollen were entered in
2012,showinganotabledecreaseinrecentyears.The Spearman’s correlation analysis with daily meteoro-
results of ANOVA showed that there was not a logicaldata(2000–2013).Theresultsshowthatthere
significant difference (p[0.05) between the AAP were a number of significant correlations between
recorded at the three sites, and Pearson’s correlation daily average concentrations of airborne pollen and
analysis showed that AAP at these sites was also dailymeteorologicaldatarecordedduringAugustand
significantly (positively) related. The amount of September(Table 3).Dailyaveragepollenconcentra-
airborneUrticaceaepollenrecordedannuallyshowed tionswerenegativelycorrelatedwithprecipitationand
a similar pattern, with the highest AUP in 2000 and humidity and positively correlated with temperature.
lowest AUP in 2013. The lowest amount of airborne Theonlysignificantcorrelationswithwindspeedwere
Cannabaceaepollenwasalsorecordedin2013,butthe seen with airborne Urticaceae pollen at Legnano
highestACPwasin2009. (significant negative correlations). Wind direction
Therewereanumberofsignificantnegativetrends was also examined. Wind roses (Fig. 2a–f) for daily
inAAPduringtheperiod2000–2013(Table 2).These averagewinddirectionduringAugustandSeptember
h
significanttrendsinAAPwerenolongerevidentwhen showthattherewasahigherthanaveragefrequencyof
c
Ambrosia pollen data from 2013 and 2012 were windsfromtheSoutheastin2012and2013(theyears
.
o removedfromtheanalysis.Theonlysitewherethere withthelowestAAP).Conversely,therewasahigher
r werenosignificanttrendsinAAPwasMagenta,where frequency of winds from the South and Southwest in
e the peak AAP was recorded in 2004. The significant 2000 and 2004 (years with high AAP), although it
r negative trend was still apparent when data from all shouldbenotedthattherewasalsoahighfrequencyof
.
c three sites were combined. There were no significant Southeasterly winds in 2009 when the AAP also
o trends in ACP, but there were significant negative exceeded 6000 Ambrosia pollen grains (Fig. 2c;
d trends in AUP in all three periods (2000–2013, Table 1).
/ 2000–2012and2000–2011). AAP recorded in Legnano was entered into Spear-
/
: Cannabaceae and Urticaceae plants also flower in man’s rank correlation analysis with monthly and two
p
latesummer,andtheirpollenisintheairatthesame monthly averages of meteorological data recorded
t timeasAmbrosia.TheresultsofSpearman’scorrela- beforeAugust(examiningpollenproduction)andduring
t
h tion analysis showed that there were significant August and September (related to the release and
positive relationships between daily average concen- dispersal of pollen from the plant) in the year of
trations of airborne Ambrosia pollen and airborne pollination.Theonlystatisticallysignificantcorrelations
Cannabaceae pollen (r = 0.5582, p\0.001) and recordedwerebetweenAAPandthefollowing(Fig. 3):
Urticaceae pollen (r = 0.648, p\0.001) recorded
• Before the season—March (r = -0.702,
during August and September at the sites. There was
p = 0.008) and June (r = -0.634, p = 0.020)
also a significant positive correlation between daily
averages of daily minimum temperatures, as well
average concentrations of airborne Cannabaceae pol-
as June averages of daily relative humidity (r =
len and airborne Urticaceae pollen (r = 0.714,
-0.676,p = 0.011);
p\0.001).
• During the season—September number of days
Pearson’s correlation analysis showed that there
withrain(r = -0.656,p = 0.015).
was a significant relationship between AAP and
ACP (r = 0.560, p = 0.037) but not between AAP ClimatedatarecordedatMalpensaairport(Table 4)
and AUP (r = 0.490, p = 0.075) recorded at Leg- show that in 2013 mean minimum temperatures in
nano. Similarly, correlations were not significant MarchandJune,meanrelativehumidityinJuneandthe
between ACP and AUP at this site (r = 0.176, number of days with rain inSeptember 2013 were all
p = 0.547). belowaverage(lowerthanthe1981–2010mean).
6
individual eITMIL6 Maximum 463 413 376 132 167 391 356 206 304 254 223 141 63 134 258.79 125.44
o2013at Urticacea AUP 7574 5494 7428 2155 2982 3954 4255 2711 4814 4597 3078 2735 1483 1447 3907.64 1944.00
t
00 m
0 u
2 m 3 7
from eae Maxi 88 55 43 88 75 97 73 93 54 126 32 105 86 34 74.9 27.9
st–September(ITRHO1) CannabacITMIL6 ACP 1089 815 618 990 757 1144 758 758 927 1350 540 1264 820 449 877.07 265.83
uo
gh
uR
A
d
uringE)an MIL6,
dG
h yA IT m
c.rero.c pollenrecordedannuallTMIL6),Magenta(ITM Milanarea(ITMAGE,mean) Maximu 394 322 526 296 380 380 427 630 258 281 329 387 220 48 348.43 137.52
/do ae(AUP)Legnano(I AmbrosiaITRHO1 AAP 7086 3938 4656 5134 6392 4939 5609 5823 4866 6266 4500 4778 3136 685 4843.43 1575.67
http:/ andUrticaceceofMilan: aITRHO1 Maximum 528 247 585 208 452 273 522 527 241 324 409 256 191 84 346.21 155.82 erlined
AmbrosiaAmountof(AAP),Cannabaceae(ACP)ogicalstationssituatedintheNorthwestoftheProvin AmbrosiaAmbrosiaAmbrosiITMIL6ITMAGE AAPMaximumAAPMaximumAAP 706244767533707442 487246137952573147 354531053716835053 492829259996554476 623233773584975585 446743357285834623 536573750424616421 4837288704011255593 394225565114004146 616230872123315424 331929665707673610 3922253675310013659 279119436034133013 6466068034865 4435.00332.795595.79543.434499.64 1621.43157.351858.82286.051646.14 estvalueiswrittenbold,andthehighestvalueisund
Table1aerobiol Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Mean SD Thelow
7
Table2 Trends in the amount of airborne Ambrosia (AAP), regression over time (slope), standard error of the regression
Cannabaceae (ACP) and Urticaceae (AUP) pollen recorded (SE) and probability level (p). Legnano (ITMIL6), Magenta
annuallyinAugust–September intheMilan areaduringthree (ITMAGE)andRho(ITRHO1)
periods(2000–2011,2000–2012and2000–2013):slopeofthe
Site 2000–2013 2000–2012 2000–2011
Slope SE p Slope SE p Slope SE p
AAP—ITMIL6 -256.312 83.932 0.01 -174.659 81.091 0.054 -134.318 91.905 0.175
AAP—ITMAGE -136.200 122.096 0.286 21.665 94.871 0.824 135.601 80.886 0.125
AAP—ITRHO1 -240.556 89.897 0.02 -158.516 89.223 0.103 -121.248 102.455 0.264
AAP—Milanarea -211.023 90.065 0.037 -103.837 76.415 0.201 -39.988 79.200 0.625
ACP—ITMIL6 -0.069 18.225 0.699 7.434 18.849 0.701 13.552 21.907 0.550
AUP—ITMIL6 -330.631 94.266 0.004 -318.648 109.782 0.014 -286.738 128.054 0.049
Boldvaluesaresignifytheprobabilitylevel
Table3 Results of Spearman’s rank correlation analysis be- temperatureandthenumberofdayswithprecipitation
tween daily average Ambrosia, Cannabaceae and Urticaceae in September and (2) June averages of daily relative
pollen concentrations (August–September) and daily me-
h teorological data. T mean temperature ((cid:2)C), T maximum humidityandthenumberofdayswithprecipitationin
max
c temperature ((cid:2)C), Tmin minimum temperature ((cid:2)C), H mean September.Ingeneral,actualandpredictedvaluesfor
humidity (%), PP precipitation amount (mm), V mean wind AAP were reasonably close in both 2009 and 2010.
.
o speed(Km/h),VMmaximumsustainedwindspeed(Km/h) The main exception is the simple linear regression
r AmbrosiaMilanarea Cannabaceae Urticaceae model that included the number of days with pre-
e
(ITMAGE,ITMIL6, ITMIL6 ITMIL6 cipitationinSeptember,whichoverestimatedAAPin
r ITRHO1mean)
2010. There was a close agreement between the
.
c PP -0.093** -0.183** -0.164** models in 2013, but all three models greatly overes-
o H -0.140** -0.256** -0.125** timatedAAPinthisyear(Fig. 4).
d
T 0.119** 0.439** 0.351**
min
/
/ Tmax 0.176** 0.556** 0.429** 3.4 Landusechange
:
T 0.184** 0.567** 0.470**
p
V -0.008 0.030 -0.124** Examinationofdata fromthe UOCSPreSALInternal
t
t VM -0.024 0.000 -0.135** Archiveshowsthatthereisnorelationshipbetweenthe
h
numberoflargeconstructionworks(i.e.theMilanFair,
*Correlationissignificantatthe0.05level(twotailed)
roadandrailnetworksandbuildingconstructionsuchas
**Correlationissignificantatthe0.01level(twotailed)
hospitals and residential buildings) and the significant
decreasewitnessedinAAPintheMilanareasince2000
(Table 6).Nomajorconstructionworkswerelistedas
3.3 Regressionanalysis
beingundertakenduring2000and2001,whenairborne
concentrations of Ambrosia pollen were high. On the
Simplelinearandstandardmultipleregressionmodels
otherhand,anumberofmajorconstructionworkshave
were constructed for predicting the AAP in Legnano
been in operation in recent years, such as residential
during2009and2010(thevalidationyears)aswellas
buildingaswellasroadandraillinks.
2013 (the year under investigation), using me-
teorology parameters that were significantly correlat-
ed with AAP. The final models are presented in
Table 5. The only models that were statistically 4 Discussion
significant were the simple linear regression model
using the number of days with precipitation in This study investigates potential drivers of the
September and standard multiple regression models significant decrease witnessed in Ambrosia pollen
using: (1) March averages of daily minimum counts recorded in the Milan area in recent years.
8
Fig.2 WindRosesfor
selectedyears,showingthe
frequencydifferentdaily
averagewinddirections
recordedatBustoArsizio
meteorologicalstation
duringAugustand
Septemberin2000–2013:
a2000;b2004;c2009;
d2012;e2013;f2000–2012
mean
h
c
.
o
r
e
r
.
c
o
d
/
/
:
p
t
t
h
ConcentrationsofairborneAmbrosiapollenrecorded Pearson’s correlation analysis). This information,
at the three pollen-monitoring sites included in this combined with the fact that this significant decrease
studyarecloselyrelated(determinedbyANOVAand is repeated at all three sites, suggests that this is a
9
h
c
.
o
r
e
Fig.3 Relationship between AAP and monthly average relationshipwiththemonthlyvariablesmeansthatlowvalues
r
meteorological data with significant correlations: a March would normally result in high AAP. Many of the values are
.
c averages of daily minimum temperature, b June averages of below average (e.g. T in June is the third lowest in the
min
o daily relative humidity, c June averages of daily minimum 2000–2013dataset),buttheAAPin2013isextremelylow.Tmin
temperatures,(D)numberofdayswithraininSeptember.Data minimumtemperature((cid:2)C),Hmeanhumidity(%),RNindicator
d
from 2013 are marked as ‘‘X’’. Note that the negative foroccurrenceofrainordrizzle
/
/
:
p Table4 Climate data recorded at Malpensa airport and ac- of large numbers of O. communa leaf beetles in the
t cessed via http://www.tutiempo.net, showing 2013 values in area?
t relationtolong-termmeanfortheperiod1981–2010
h Airborne concentrations of Ambrosia pollen
2013 1981–2010 recorded in the Milan area are influenced by weather
conditions before and during the main flowering
MeanannualT 6.50 5.93
min
periodofAmbrosia(August–September).Theweather
MeanannualRN 150.00 127.14
before the start of the season influences pollen
T (March) 0.30 0.58
min production, and significant correlations were found
T (June) 13.50 13.60
min between AAP and March and June averages of daily
H(June) 60.00 67.64
minimum temperatures, as well as June averages of
RN(September) 7.00 10.50
daily relative humidity. The negative relationships
Tmin minimum temperature ((cid:2)C), H mean humidity (%), RN between AAP and these variables suggest that lower
indicatorforoccurrenceofrainordrizzle
temperatures in March and lower temperatures and
lower rainfall in June favour the production of
Ambrosia pollen. It should therefore be noted that
regional scale phenomenon. The question that this minimum temperatures in March and June as well as
studyhasattemptedtoansweris:Canthisdecreasebe relative humidity in June were all below average in
explained by environmental factors such as me- 2013(comparedwiththe1981–2010mean)(Table 4).
teorology, or is there evidence to support the hy- The negative association with minimum tem-
pothesisthatthedecreasewasrelatedtothepresence peraturesinMarchcouldbecausedbytherelationship
10