e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

An inventory of tree species in Europe—An essential datainput for air pollution modelling

Carsten A. Skjøth ∗, Camilla Geels, Martin Hvidberg, Ole Hertel, Jørgen Brandt,Lise M. Frohn, Kaj M. Hansen, Gitte B. Hedegård,Jesper H. Christensen, L. MoseholmNational Environmental Research Institute, University of Aarhus, P.O. Box 358, Frederiksborgvej 399,4000 Roskilde, Denmark

a r t i c l e i n f o

Article history:

Published on line 21 July 2008

Keywords:

VOC

Pollen

Biogenic emission

EMEP

a b s t r a c t

We present a detailed tree species inventory covering Europe, parts of Africa and parts of

Asia. The inventory contains 39 groups of species that are important for biogenic VOCs

or pollen emission calculations. For example: oak (Quercus), poplar (Populus), pines (Pinus),

spruce (Picea), birch (Betula) and alder (Alnus). The inventory is based on national forest

inventories and national statistics and gives tree species distribution in percentage within

broadleaved as well as conifer forests. The inventory includes data from 799 regions and

is redistributed into the 50 km × 50 km EMEP grid. The inventory is therefore prepared for

easy implementation into atmospheric transport models by providing an extension to

Climate change already applied land use data such as the Corine Land Cover (CLC2000) or Global Land

Cover (GLC2000). The gridded version of the data set will be available on the webpage

http://www.dmu.dk/International/Air/Models/Background/Trees/.

et al., 2006; Guenther et al., 2006). Short term changes in

1. Introduction

The biosphere act as an important source of gasses and par-ticles to the atmosphere. Detailed data characterising thebiosphere is therefore needed as input for atmospheric mod-els in order to assess the impact of these emissions correctly.For some of these emissions, the distribution of trees down togenus or species level is a must. The release of volatile organiccompounds (VOCs) (Dindorf et al., 2006; Guenther et al., 1995;Karlik et al., 2002; Karlik and Winer, 2001; Simpson et al., 1999)is an important example of such emissions, since the distribu-tion of VOC species and the emission intensity varies strongly

from one type of tree to another as well as between othergroups of plants. The same picture applies e.g. to the emissionof pollen. These differences are important since emissions of

∗ Corresponding author. Tel.: +45 46301200; fax: +45 46301214.E-mail address: cas@dmu.dk (C.A. Skjøth).

0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2008.06.023

© 2008 Elsevier B.V. All rights reserved.

VOCs and pollen have great impact on both environment andhealth.

The biogenic VOCs are highly reactive, and together withNOx these compounds play an important role in the formationof tropospheric ozone and secondary organic aerosols (SOA)(Andreae and Crutzen, 1997; Atkinson, 2000; Gelencser et al.,2007; Kanakidou et al., 2005; Szidat et al., 2006; Went, 1960).The emission of biogenic VOCs has therefore far-reachingeffects in relation to air quality and effects on human health,plants as well as the global climate. Biogenic VOC emissionsdepends strongly on temperature and light variations (Dindorf

meteorological conditions may therefore increase or decreaseemissions of biogenic VOCs in one area such as southernEurope, which in turn affect formation and transport of ozone

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Uafap

e c o l o g i c a l m o d e l l i n

o northern Europe. Regional tree species distributions areherefore important with respect to biogenic VOC emissionsnd long range transported air pollutants such as ozone andOA. The large temporal variations in emissions suggest these of dynamical emission models. Such models require treepecies information as a main input parameter. It has beenhown that dynamic emission calculations of other air pol-utants such as ammonia improves the results obtained withtmospheric transport models (Ambelas Skjøth et al., 2004). Its therefore likely, that studies of air pollutants such as ozoner SOA using chemical transport models (CTMs) will benefitrom dynamic emission calculations of biogenic VOCs usingree inventories on species level.

Atmospheric transport models can be extended to handletmospheric dispersal of pollen (Helbig et al., 2004; Pasken andietrowicz, 2005; Sofiev et al., 2006). Similar to CTMs, pollenispersal models are strongly dependent on the quality ofhe pollen emissions. As for VOCs, pollen emission changeseavily with variations in meteorology such as temperaturend relative humidity (Bianchi et al., 1959; Laaidi et al., 2003;gden et al., 1969; Rempe, 1938). Similar to chemical air pol-

utants, the overall pollen concentration at a specific locationay be due to local sources (Corden et al., 2000; Skjøth et al.,

008) or long range atmospheric transport (Hjelmroos, 1991;jelmroos, 1992; Skjøth et al., 2007; Smith et al., 2008). Further-ore, pollen production from trees such as Betula is known

o vary significantly from year to year (Latalowa et al., 2002;anta et al., 2006; Sommer and Rasmussen, 2006). Tree pollenroduced one year is released the following spring. Dynamicstimates of pollen emission should therefore be based onodels that take the meteorological conditions during pollen

roduction and release into account and a specific tree inven-ory.

Thus, atmospheric transport models used for chemical airollution or pollen dispersal studies will benefit from dynami-al emission models using high resolution tree inventories onpecies level.

Forest inventories suitable for biogenic VOC emission andollen emission calculations are currently available at aational (Simpson et al., 1999) or sub-national level (Skjøtht al., 2007, 2008) for several European countries. These forestnventories are non-uniform as they are based on differentational inventory strategies. As an example: neighbouringountries account important species such as Betula sepa-ately (e.g. Sweden or Lithuania) or in groups with two or

ore species (e.g. Denmark and Poland). However, most Euro-ean countries have a forest inventory suitable for estimatesf tree species specific distributions. Therefore a GIS frame-ork may be used to harmonize data that is based onneven sampling strategies or forest inventories. This har-onized data set may be redistributed to the EMEP50 grid

http://www.emep.int/grid/griddescr.html).

.1. The overall framework of the study

nevenly distributed forest inventories and tree statistics are

ggregated on national and sub-national level using a GISramework. The inventory is redistributed into commonlypplied grid definitions such as the EMEP50 grid. The finalroduct will give a tree species distribution ready to be used

7 ( 2 0 0 8 ) 292–304 293

as input to atmospheric transport models for biogenic VOC orpollen dispersal studies.

2. Methodology

Statistical data on tree species composition was obtained fromeach country within the EMEP50 area domain. Five differentsources for data are considered appropriate: scientific papers,the national forest inventories, the national statistical offices,the European Forest Institute and the national reports used inthe Global Forest Resources Assessment (FAO, 2006). We haveselected the inventory with the largest number of species andlargest number of regions within each country (Table 1). ForSwitzerland, Poland, the Czech Republic and Hungary severalstatistical datasets were combined to produce a species spe-cific dataset. Statistical data were not found for a few areas inthe EMEP domain: Finnmark in Norway, Northern Ireland andIsle of Man, Lichtenstein, Andorra and Monaco, Svalbard, theOrkney, the Shetland and the Faroe Islands, the northern partof Murmansk and F.Y.R. Macedonia. The distribution of treespecies in these areas is based on neighbouring regions or oninterpolation between several regions. It was not possible tofind any information on Libya. Due to very limited informa-tion in the surrounding areas and the size of Libya, no speciesspecific information was added to that area.

The reported inventories are divided into two main groups:conifers and broadleaved trees on species level. The distribu-tion in percent is then calculated with respect to these twogroups.

The broadleaved species Betula and Alnus, were in Den-mark, UK, and the Balkan countries reported within a group ofremaining broadleaved species. For all Balkan countries, Betulaand Alnus are present only in small quantities – below the 10most frequent species (Angelov, 2005; Jovic, 2005; Karadumi,2005). For each Balkan country Betula and Alnus is calculatedas a fraction of the remaining broadleaved species, where thefraction is below the 10th most frequent species. In UK Alnusdensity was based on the overall national density of Alnustrees found in the small woodland survey (5%) with respectto that group of trees which is not accounted for separatelyin the Forest Inventory (Forestry Commission, 2001). In Den-mark Betula and Alnus are accounted for in a residual groupof broadleaved species, even though Betula may be the domi-nant species in selected regions (Skjøth et al., 2008). Here theresidual group is estimated to consist of mainly Betula (70%)and small quantities of Alnus (10%), Populus (10%) and otherbroadleaved unknown species (10%). For the Netherlands thegroup Populus and Salix was split into two. For Poland the groupBetula and Robinia pseudoacacia was split into two using statis-tics from the neighbouring regions indicating 95% density ofBetula in this group.

The final tree species inventory contains species on genuslevel such as Alnus sp. or Betula sp., or species level such asFagus sylvatica and Pinus sylvestris. The inventory was after-wards redistributed into the EMEP50 model grid for easy

implementation into atmospheric transport models. A sum-mary of the inventory is presented in Table 2. Table 2 includeoverall presence of each of the tree species with respect to theindividual regions with forest inventories and with respect to

i n g

294 e c o l o g i c a l m o d e l l

number of EMEP cells in the gridded inventory. A statisticalsummary based on the EMEP grid cells is given. This includesmean, standard deviation, median, 5% and 95% percentiles.

3. Results

The tree distribution inventory covers 799 regions in Europe,parts of Asia and parts of Africa. As seen on Fig. 1 the regions

range from country level to municipality level. The inventoryinclude 16 different conifer groups on genus or species level:P. sylvestris, Pinus halepensis, Pinus nigra, Pinus pinaster, Pinus sp.(undefined Pinus species), Picea abies, Picea sitchensis, Picea sp.

Table 1 – Land, number of regions in each land with forest invethe inventory

Country Regions Classes

Albania 1 7 (KaradumAlgeria 1 7 (Ghazi, 2Armenia 1 11 (GhulidjaAustria 9 26 (SchadauAzerbaijan 1 13 (FRA, 200Belarus 6 10 (SchelhaaBelgium 2 11 (LaurentBosnia Herzegovina 1 7 (BlagojevBulgaria 1 11 (Angelov,Czech Republic 1 24 (Czech FoCroatia 1 12 (OjurovicDenmark 270 11 (DanmarEstonia 15 9 (Estonia CFinland 14 8 (Peltola, 2France 93 53 (IFN, 2007Georgia 1 11 (TorchinaGermany 13 13 (Bundesm

2004)Greece 1 8 (SimpsonHungary 1 11 (Csoka anIceland 1 11 (SnorrasoIreland 26 17 (RedmonItaly 21 62 (TabacchKasakhstan 1 11 (MeirembLatvia 1 8 Central SLithuania 55 9 (LithuaniLuxembourg 1 11 (Wagner,Macedonia 1 2 (SchelhaaMoldavia 1 9 (FRA 2005Netherlands 1 13 (PaasmanNorway 6 8 (LarssonPoland 16 9 (Budna eRomania 1 11 (Moise, 2Russia 137 38 (Isaev anPortugal (MinisterSwitzerland 5 12 (Brassel aSlovakia 1 14 (Longaue

2004; StaSlovenia 1 10 (HocevarSerbia and Montenegro 1 10 (Jovic, 20Spain (MinisterSweden 21 7 (SwedishTunisia 1 8 (Rafik anTurkey 1 20 (GeneralUnited Kingdom 65 18 (ForestryUkraine 1 11 (Romano

2 1 7 ( 2 0 0 8 ) 292–304

(undefined Picea species), Pseudotsuga, Abies alba, Abies procera,Abies normanniana, Abies sp. (undefined Abies species), Larix sp.,Juniperus sp. and other conifers. The inventory also include 23broadleaved groups on genus or species level: F. sylvatica, Fagussp. (Fagus moesiaca, Fagus orientalis) Quercus sp., Quercus petraea,Quercus robur, Quercus rubra, Quercus cerris, Quercus pubescence,Quercus suber, Quercus faginea, Quercus ilex, Acer sp., Castaneasp., Fraxinus sp., Carpinus sp., Betula sp. (Betula pubescens andBetula pendula) R. pseudoacacia, Alnus sp. (Alnus glutinosa and

Alnus incana), Populus sp., Ulmus sp., Eucalyptus sp., Olea andother broadleaved.

Six groups of trees species on genus level are shown asexamples in Figs. 2 and 3: from the broadleaved group Alnus

ntories, number of classes in the inventory and source of

Source

i, 2005)005)nyan, 2005)er, 2004)5; Secretariat, 2005a)s et al., 2006)

et al., 2005)ic, 2005)2005)rest Management Institute, 2007; Schelhaas et al., 2006), 2005)ks Statistik, 2002)

entre of Forest Protection and Silviculture, 2004)006))va, 2005)inisterium für Ernährung – Landwirtsaft und Verbraucherschutz,

et al., 1999)d Mozes, 2005)n, 2005)d et al., 2007)i et al., 2007)ekov et al., 2005)tatistical Bureau of Latviaan State Forest Survey Service, 2005)2005)s et al., 2006)Secretariat, 2005b)

, 2005; Schelhaas et al., 2006)and Hylen, 2007)t al., 2005; Schelhaas et al., 2006)005)d Shvidenko, 2002)io da Agriculture, 2007)nd Brändi U.-B., 1999)r and Moravcik, 2005; Ministry of Agriculture of the Slovak Rep,tistical Office of the Slovak Republic, 2007), 2005; Slovenia Forest Service, 2006)05)io de Medio Ambiente, 2006)Forest Agency, 2007)

d Ghazi, 2005)Directorate of Forestry in Turkey, 2005)Commission, 2001)vskyy, 2005; Zyrina, 2007)

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304 295

Table 2 – Common name and latin name of tree species found in the inventory

Common name Latin name Regions Cells Mean S.D. Median 5% percentile 95% percentile

ConifersSilver fir Abies alba 73 1142 9.2 13.3 3.1 0.1 46.8Caucasian fir Abies normanniana 266 45 11.8 8.6 9.4 0.7 28.1Noble fir Abies procera 249 44 3.6 2.0 3.2 0.8 7.0Fir Abies sp. 355 1479 11.2 14.8 4.9 0.0 40.3Juniper Juniperus sp. 16 1102 3.0 4.5 3.0 0.0 11.6Larch Larix sp. 245 1869 3.4 5.8 0.4 0.0 15.5Norway spruce Picea abies 638 2364 27.8 23.0 17.8 0.7 68.1Sitka spruce Picea sitchensis 370 487 23.6 27.0 11.6 0.0 79.3Spruce Picea sp. 62 1577 23.7 24.5 11.1 0.7 68.4Aleppo pine Pinus halepensis 41 1288 44.0 29.5 36.0 0.8 81.6Black pine Pinus nigra 45 1394 18.9 13.5 16.1 0.2 33.7Maritime pine Pinus pinaster 84 1228 10.4 22.1 2.5 0.3 68.5Scots pine Pinus sylvestris 395 2991 36.0 30.2 26.7 1.1 85.6Pine Pinus sp. 595 3468 33.7 29.5 30.4 0.0 97.5Douglas fir Pseudotsuga 197 1080 4.8 8.4 1.1 0.0 24.0Other conifers . . . 359 4162 22.3 33.6 4.9 0.0 100.0

BroadleavedMaple/sycamore Acer sp. 512 2261 2.3 3.7 1.2 0.0 9.8Alder Alnus sp. (Alnus glutinosa

and Alnus incana)695 4014 5.8 7.0 2.7 0.1 21.2

Birch Betula sp. (Betula pubescensand Betula pendula)

706 3743 31.8 34.9 10.8 0.1 95.1

Hornbeam Carpinus sp. 181 1956 4.5 7.3 2.1 0.2 19.4Chestnut Castanea sp. 216 1689 3.9 6.2 1.6 0.1 19.3Eucalyptus Eucalyptus sp. 29 483 5.6 15.6 0.9 0.3 35.7European beech Fagus sylvatica 560 2096 23.0 19.6 14.1 1.0 64.1Beech Fagus sp. (Fagus moesiaca,

Fagus orientalis)12 913 24.2 15.6 26.6 0.7 54.3

Ash Fraxinus sp. 658 3094 4.3 5.5 2.7 0.1 17.5Olive Olea sp. 7 177 3.1 4.3 1.6 0.0 16.0Poplar Populus sp. 717 4183 5.8 7.4 2.7 0.1 22.9Turkey oak Quercus cerris 27 242 7.2 5.5 7.8 0.0 16.3Portuguese oak Quercus faginea 12 277 9.8 8.9 7.0 0.3 34.4Evergreen oak Quercus ilex 16 324 40.4 24.1 45.0 0.2 80.1Sessile oak Quercus petraea 55 646 7.4 6.1 5.9 0.5 17.9Downy oak Quercus pubescens 60 618 13.8 18.0 7.4 0.1 40.2Pedunculate oak Quercus robur 418 1443 13.5 15.8 4.4 0.2 42.2Red oak Quercus rubra 79 282 0.9 1.2 0.4 0.0 4.5Cork oak Quercus suber 35 519 20.8 21.3 13.7 0.2 57.0Oak Quercus sp. 338 3566 33.1 25.5 30.1 0.3 76.0Black locust Robinia pseudoacacia 95 688 1.7 3.4 0.6 0.0 12.6Elm Ulmus sp. 116 1274 3.3 7.9 0.9 0.0 10.7Other broadleaved . . . 777 5353 20.8 29.8 9.5 0.1 100.0

statefers

sssrg

EGcnc

u

Regions and cells represent presence of the actual tree species in thestandard deviation (S.D.), median, 5% percentile and 95% percentile r

p. (2a), Betula sp. (2b), Fagus sp. (2c) and Quercus sp. (2d) arehown, while from the conifer group all Pinus sp. (3a) and Piceap. (3b) are shown. In these figures the inventories have beenedistributed into a 50 km × 50 km grid based on the EMEP50rid definition.

Alnus sp. (Fig. 2a) is found in almost all regions within theMEP50 domain. Highest percentages are found in parts ofermany, Poland, the southern part of Scandinavia, the Balticountries and Belarus. Trees belonging to the genus Alnus are

ot dominant species on the large scale, as the largest per-entages rarely exceed 20%.

Betula sp. (Fig. 2b) is found in most of Europe with densitiesp to 100%. Trees belonging to the genus Betula are the dom-

istical regions and the EMEP 50 km × 50 km cells, respectively. Mean,to the percentages given in the EMEP cells.

inant broadleaved species in Russia, Scandinavia and Balticcountries, but have also significant prevalence in large partsof Poland, Germany and the United Kingdom. The southernlimit is the northern part of Spain and Portugal, the Balkancountries as well as parts of Italy.

Fagus sp. (Fig. 2c) shows the sum of two groups F. sylvat-ica and Fagus sp. (F. moesiaca, F. orientalis) in the inventory.Trees belonging to the genus Fagus have a northern limitin the southern part of Sweden, Poland and Ukraine and

a southern limit in the central part of Spain and Portugal.Highest densities from 15% to 50% are found in Denmark, Ger-many, parts of France as well as in central Europe, Balkan andTurkey.

296 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304

isticfor d

Fig. 1 – Regions contribution to tree species distribution statareas in Europe within the GLC2000 land cover (see Table 1

Quercus sp. (Fig. 2d) shows the sum of the nine groups Quer-cus sp., Q. petraea, Q. robur, Q. rubra, Q. cerris, Q. pubescence, Q.suber, Q. ilex and Q. faginea in the inventory. Trees belonging tothe genus Quercus have a northern limit in Scandinavia, Rus-sia and Baltic countries, but have no southern limit within theEMEP50 domain. Densities between 50% and 100% are foundin the countries south of the Scandinavian and Baltic areas,except for the Alp region such as Switzerland, Austria andnorthern Italy where the density is between 0% and 10%.

Pinus sp. (Fig. 3a) shows the sum of the five groups P.sylvestris, P. halepensis, P. nigra, P. pinaster and Pinus sp. in theinventory. Trees belonging to the genus Pinus are present innearly all regions with conifer trees Pinus are dominant speciesin most of southern Europe, but they also constitute a largefraction in Poland, parts of Russia and in Finland. Low densi-ties are found throughout central Europe, most of the UnitedKingdom and Ireland.

Picea sp. (Fig. 3b) shows the sum of the three groups P. abies,P. sitchensis and Picea sp. in the inventory. Trees from the genus

Picea are dominant species in northern Europe, most of the UKand Ireland and central Europe including the Alp region. Theyhave a southern limit in France, Italy and Turkey, where thedensity is below 5% of the conifer trees.

s and major conifer (green) and broadleaved (blue) forestedetails).

4. Uncertainty and comparison with otherresults

Accuracy and hence uncertainty of this dataset varies withineach of the 799 regions and within the countries. The uncer-tainty can be grouped into three categories where the first isrelated to the applied inventory method, the second to thenumber of species categories (including the individual speciesand species not accounted for) and the third to the geograph-ical size of each of the 799 regions and regions with limited orno statistics.

4.1. Uncertainty with respect to inventory method

In general two different inventory methods have been applied:sampling methods such as in France (IFN, 2007) and the UnitedKingdom (Forestry Commission, 2001) or complete invento-ries based on questionnaires such as Denmark (Danmarks

Statistik, 2002). In general complete inventories are consideredto have the highest accuracy. However, the sampling methodmay also be considered very accurate having a standard errorbetween 0% and 1% for the most common species (Forestry

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304 297

(c) Fa

Ct

4

Itao

Fig. 2 – Gridded density of (a) Alnus sp., (b) Betula sp.,

ommission, 2001). Uncertainties of the inventories are unfor-unately not always documented.

.2. Uncertainty with respect to tree species

nventories of Fagus sp. and Quercus sp. are considered to havehe highest accuracy as these species are accounted for sep-rately in nearly all countries. Fagus sp. primarily consistsf F. sylvatica for nearly all European countries. Quercus sp.

gus sp. and (d) Quercus sp. among broadleaved trees.

consists of nine different Quercus species each accounted forseparately.

Betula sp. and Alnus sp. are accounted for in nearly all coun-tries either on genus level as Betula sp. and Alnus sp. or onspecies level as B. pubescens, B. pendula, A. glutinosa or A. incana.

Denmark, UK and the Balkan countries do not make a surveyfor Betula or Alnus separately. In the Balkan area the only infor-mation is that the abundance of Betula and Alnus is below the10 most occurring species, which in general is below 2% of

298 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304

sp. a

Fig. 3 – Gridded density of (a) Pinus

the broadleaved species, thus setting an upper limit for theuncertainty. In Denmark Betula and Alnus are accounted for ina residual group of other broadleaved trees including Betulasp., Alnus sp., Populus sp. and Salix sp. The exact composi-tion of this group is not known. Betula and Alnus densities aretherefore more uncertain than other species in Denmark. Fur-thermore, it has been shown, that this group in some regionsof Denmark can be the dominant group of broadleaved trees(Skjøth et al., 2008). In addition, the same investigation alsopointed out, that the areas with very high density of otherbroadleaved trees are dominated by Betula. Similarly for sev-eral UK regions, the group of other broadleaved is trees thedominant broadleaved group of broadleaved trees. In the UKthis group include Alder. The 5% Alder density assumption isuncertain and there is a risk especially of underestimationin some regions of the UK. In Poland the broadleaved groupBetula and R. pseudoacacia introduce uncertainty. This groupis divided with a 95% and 5% split between Betula sp. and R.pseudoacacia, which introduce a risk for overestimation of theBetula density in Poland.

Pinus and/or Picea species are accounted for in all regionswith forest statistics. The main uncertainty is related to thenational identification methods. Some countries such as Slo-vakia identify Pinus or Picea species on genus level; such asPinus sp. or Picea sp. Other countries such as Italy and Spainidentify Pinus species to species level; such as P. nigra, P.halepensis, P. sylvestris and P. pinaster.

Another main uncertainty is associated with the groups“other conifers” and “other broadleaved”. Most countries have

two tree classes covering the conifers and broadleaved treespecies without a specific class. Several countries surround-ing the Baltic Sea such as Sweden, Estonia and Latvia didnot report a group with other conifer trees. However, infor-

nd (b) Picea sp. among conifer trees.

mation from the corresponding national forest assessmentreports support that the amount of other conifer trees isinsignificant for these countries (FAO, 2006). Greece has notproduced a national forest inventory (FAO, 2006). Greece isin our data set based on statistics given in Simpson et al.(1999). All broadleaved trees in Greece are distributed amongthe genus Castanea, Fagus and Quercus. Neighbouring regionssuch as Bulgaria, Albania or Italy report a much larger num-ber of broadleaved species (up to 13 species or genera in Italy).Therefore statistics from Greece should be considered prelim-inary.

4.3. Uncertainty with respect to the geographicalregion

Finally, there is uncertainty with respect to the geographicalsize of each of the 799 individual regions. The statistics refer tothe geographical region. This means that the grid based esti-mate in general will have larger uncertainty in areas wherethe underlying inventoried region is large such as in Ukraineand low where the underlying region is small such as in Den-mark. In principle, the uncertainty will decrease for Denmarkusing a higher grid resolution similar to the 16.67 km emissioninventory by Hertel et al. (2002) applied by the DEHM model(Frohn et al., 2002), as the statistical regions in Denmark has ageographical area very similar to the 16.67 km grid definition.Few areas in the EMEP domain are not covered by the statis-tical data base and the interpolation from the neighbouringregions, therefore, also introduces uncertainty.

The overall uncertainty related to the final data set istherefore expected to be lowest for the Danish area, as thegeographical regions are smaller than the EMEP50 grid reso-lution and because the statistical data are based on complete

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 292–304 299

Table 3 – Broad leaved and conifer forest areas in the case study using the GLC land cover data set by assuming 100% treecover in wooded areas and the similar values given in Table 1 by Simpson et al. (1999)

Country This study Simpson et al. (1999)

Broadleaved forest area Conifer forest area Total forest area Total forest area

Albania 2490 2104 4594 10460Algeria 0 21348 21348 N/AAndorra 256 65 321 N/AArmenia 4039 2223 6261 N/AAustria 35317 23307 58625 32270Azerbaijan 6489 4993 11481 N/ABelarus 74209 41817 116026 73834Belgium 3170 3069 6238 6170Bosnia & Herzegovina 5500 14235 19735 23724Bulgaria 9159 26021 35180 38710Croatia 3475 17879 21354 20776Czech Republic 23721 7700 31421 26421Denmark 3139 1697 4835 4930Estonia 16377 8954 25332 18692Faroe Islands 219 87 307 N/AFinland 211613 53632 265245 232220France 53049 92972 146021 148500Georgia 15036 25892 40927 N/AGermany 67909 43730 111639 104030Gibraltar 0 0 0 N/AGreece 20551 9908 30459 26200Guernsey 14 1 14 N/AHungary 1350 12890 14240 17010Iceland 0 0 0 1200Iran 2 1 3 N/AIreland 2058 1066 3124 3450Isle of Man 79 54 134 N/AItaly 36078 80464 116541 67520Jan Mayen 0 0 0 N/AJersey 12 0 12 N/AKazakhstan 114 104 218 N/ALatvia 19844 10811 30655 28032Libya 0 1113 1113 N/ALiechtenstein 93 78 171 N/ALithuania 14944 6143 21087 19677Luxembourg 142 650 792 886F.Y.R. Macedonia 1442 6658 8101 10790Malta 0 0 0 N/AMoldova 583 1211 1794 3570Monaco 0 0 0 N/AMorocco 0 1507 1507 N/ANetherlands 1833 503 2336 3000Norway 106924 48783 155707 83300Poland 70829 29315 100144 87810Portugal 29253 17612 46865 29680Romania 24463 60671 85134 66900Russia 635696 526188 1161884 1446420San Marino 2 2 3 N/ASerbia & Montenegro 5197 23018 28215 41196Slovakia 9146 16105 25250 19400Slovenia 3440 12052 15492 10140Spain 84785 80010 164795 158580Svalbard 0 0 0 N/ASweden 283749 57663 341412 280200Switzerland 8074 15859 23932 10520Tunisia 0 4686 4686 N/ATurkey 110869 44961 155830 N/AUkraine 76439 53573 130013 92930United Kingdom 7731 2694 10425 24100

All values are given in km2 and N/A indicates not available. Only areas within the EMEP50 domain are considered.

i n g

300 e c o l o g i c a l m o d e l l

inventories and not sampling. Highest uncertainty is expectedfor the very large geographical regions such as Ukraine and forareas with uncertain statistical data such as Greece.

4.4. Comparison with other results

Several European scale inventories have previously been madeincluding Simpson et al., (Köble et al., 2001; Simpson et al.,1995; Simpson et al., 1999; Sofiev et al., 2006): we discuss thefollowing inventories: (1) “Inventorying emissions from naturein Europe” (Simpson et al., 1999), (2) “Novel maps for forest treespecies in Europe” (Köble et al., 2001) and (3) “Towards numer-ical forecasting of long range air transport of birch pollen:theoretical considerations and a feasibility study” (Sofiev etal., 2006)

Simpson et al. (1999) used statistics on national scale suchas France, UK, Germany, Spain or Russia to create their inven-tory. In our study sub-national regions was used when everpossible. The largest relative difference is found in Denmark,where Simpson et al. (1999) use one region and the presentstudy uses 270. With respect to tree species, Alnus has beenreported in Estonia, Lithuania, Latvia, Norway, Russia andFrance and Betula in Estonia, Lithuania, Latvia, Netherland,Norway, Poland, Sweden, Ukraine, Finland and France. Themaps in our study indicate a much larger geographical cover-age. Betula sp. and Alnus sp. are present in nearly all Europeancountries with varying density between the regions. Withrespect to Fagus and Quercus both indicate presence in mostEuropean countries south of Scandinavia. Simpson et al. (1999)mainly identify Fagus as Fagus sp. and most of the Quercusinventories as Quercus sp. The maps in our study have for sim-plicity been aggregated to Fagus sp. and Quercus sp., but mostof the statistical data are on species level such as F. sylvatica,Q. robur and Q. suber. Both inventories indicate Pinus sp. andPicea sp. species throughout Europe, with P. sylvestris as themain Pinus species in the north and several Pinus species inthe south.

The tree inventory must be used in combination with landuse maps. Table 3 gives an example using the GLC2000 dataset. The example is obtained by assuming maximum forestcover (i.e. 100% cover in forest land use categories) and noforest cover in land use classes such as cropland and urbanareas. This example is compared with the overall tree den-sity given in Table 1 from Simpson et al. (1999), which againare based on data from EUROSTAT and Food and Agricul-tural Organization. Forest densities using GLC2000 data setare not identical to Simpson et al. (1999). For many countriesthe difference is within a range of 20%, but countries withmany mountains such as Switzerland, Norway, Austria andItaly give about a factor of two higher forest cover. Contrarydo the GLC2000 dataset give much lower tree density in F.Y.R.Macedonia, Serbia and Montenegro, Moldova, Albania and theUnited Kingdom.

Köble et al. (2001) used ICP Forest Level 1 data to calcu-late forest tree species diversity on European scale. 5513 plots(∼0.5 ha) distributed between 30 European countries was used

to produce maps showing the distribution of 115 different treespecies. The method used by Köble et al. (2001) shows thattheir method gave similar results as the NFI results for somecountries like the Czech Republic (Quercus, Fagus, Betula, Picea,

2 1 7 ( 2 0 0 8 ) 292–304

Abies, Pinus, Larix). Their method, however, deviates from theNFI for other countries such as the UK. In the UK species orgenus like Fraxinus, Betula, Larix, Pseudotsuga and several Pinusspecies are not recognized, despite frequent in the NFI (Köbleet al., 2001). Alnus is not very frequent in the study by Köble etal. (2001). They report that A. glutinosa covers less than 0.5% ofthe European forest area. Our study indicates that Alnus sp. ispresent in most of Europe with a maximum between 10% and50% of all broadleaved trees in Poland, the Baltic countries,Belarus and southern part of Scandinavia.

The map by Sofiev et al. (2006) only considers Betula sp.The map is based on the map of Köble et al. (2001) for west-ern Europe and a data set from the European Forest Institute(EFI), where the latter shows broadleaved, conifer forest andmixed forest areas. The EFI data were assumed to have aspecific Betula tree density at selected latitudes from 100%Betula sp. north of 65 ◦N and down to 5% Betula sp. between52 ◦N and 47 ◦N. Overall the Betula map in our study matchesthe assumption by Sofiev et al. (2006), as there is a generalnorth–south decrease in Betula percentages (Fig. 2b). For thecountries west of the Baltic countries, Belarus and Ukraine,the Betula density is mainly based on the dataset by Köble etal. (2001). This region seems scattered with respect to Betuladensity: it contains many Betula free regions and small iso-lated areas with densities up to about 30%, which only partlyagrees with the forest inventories used in our study. In the for-est inventories Betula is reported in nearly all regions north ofcentral Spain and northern Italy.

5. Discussion and conclusion

The unevenly distributed forest inventories and tree statisticsfrom 799 geographical regions are used as the basis for a newdetailed tree species inventory. We have combined and redis-tributed the data into the EMEP50 grid. The gridded datasetis ready to be used for input to atmospheric transport mod-els for biogenic emissions calculations such as VOCs or pollendispersal studies.

The tree species distribution includes important speciessuch as Betula sp. and Alnus sp. relevant for pollen dispersionsmodels (D’amato and Spieksma, 1992; WHO, 2003) and speciesrelevant for biogenic VOC calculations (Simpson et al., 1999)such as Quercus sp., Fagus sp., Populus sp., Picea sp. and Pinussp., totalling 39 groups on species or genus level. The inven-tory is the first to present Alnus sp. density on European scaleand the distribution also matches the overall distribution ofFagus sp., Quercus sp., Pinus sp. and Picea sp. for the EuropeanUnion Countries reported by Köble et al. (2001). The distribu-tion uses the 50 km × 50 km resolution EMEP50 grid definitionfor easy implementation into atmospheric transport modelsand the results are consistent with national forest inventoriesthroughout the model domain.

The statistical coverage is almost complete for the entireEMEP50 domain. The missing areas are generally of relativelysmall geographical extent or contain large regions without

trees (Libya and Finnmark). Interpolation into these areas istherefore considered acceptable. The results are based on themost extensive ground based data set compared to previousinventories as the number of regions is much larger than in

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Neither does it include city sources which in some countries

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impson et al. (1999) and as the statistical background mate-ial for single countries such as Denmark in general exceedshe total amount of material used by Köble et al. (2001). Thisew inventory is therefore considered to have the most accu-ate distribution for most of the species included, comparedo the other inventories.

The new inventory varies in accuracy depending on theegion and the inventoried species. Highest accuracy is ineneral considered for the Denmark and lowest accuracy forkraine and Greece. Furthermore, the results are based on thessumption that the overall tree species distribution does nothange in an area within few years, as the national inven-ories are performed within a range of years mainly between000 and 2005. Finally the inventory only includes forest areas,ainly defined as areas above 0.5 ha. In countries with low

orest density such as Denmark and United Kingdom smalloodlands or urban areas may be important. These areasay have another tree species distribution than high forests

Forestry Commission, 2001) and thus affect local levels of airollutants such as Betula pollen (Skjøth et al., 2008). However,s this inventory is intended for regional scale atmosphericransport models this uncertainty is considered of minormportance.

Measured pollen data may also be used as an indirect vali-ation of the distribution maps. Some of these measurementsre displayed on the homepage http://www.polleninfo.org, theublic part of the European Pollen Monitoring network. Theseaps (copyrighted) are intended as a service for vaccina-

ion planning of sensitive individuals. If it is assumed, thathe overall pollen load within a large region is mainly deter-

ined by local sources, then the pollen load maps may besed to indicate source distribution. In general the most aller-enic pollen such as Betula sp. and Alnus sp. are measuredn all of Europe. Therefore, the Alnus and Betula distributionbtained in this study is compared with the distribution mapsound at http://www.polleninfo.org. The map for Alnus pollenresented at http://www.polleninfo.org, is the southern geo-raphical limit central Spain and Portugal, while the northernimit is in Scandinavia and Finland. Highest concentrations areound in parts of Poland, Ukraine, Belarus and the Baltic coun-ries. Overall this distribution matches well the calculatedlnus distribution in our study (Fig. 2a). The map for Betularesented at http://www.polleninfo.org, is the geographicaloverage almost the same as for Alnus when looking at theeasured pollen concentrations. Highest concentrations are

ound in southern and central Scandinavia, Poland, the Balticountries, Belarus, Russia and Finland. This distribution againatches the overall calculated Betula density (Fig. 2b).Inventories are in general discussed (e.g. emission inven-

ories Dragosits et al., 1998; Olivier et al., 1998; Simpson etl., 1999). Inventories may be validated indirectly using atmo-pheric transport models similar to the approach suggestedy Sofiev et al. (2006) for pollen dispersion. For pollen, suchprocedure should in principle include dynamical emissionodels, which are currently not available. Another approach

s the methodology used by Skjøth et al. (2007, 2008) using

ack-trajectory calculations, forest statistics presented herend land use information. This methodology relates possi-le source areas along the trajectory path with variationsn measured Betula pollen concentrations. However, individ-

7 ( 2 0 0 8 ) 292–304 301

ual atmospheric transport models in general have limitationsand specific advantages compared to other models. A strat-egy using only one selected atmospheric transport model willtherefore reflect strengths and weaknesses of that particularmodel. A detailed validation strategy using several atmo-spheric transport models is, however, outside the scope of thisarticle. Therefore, we have aimed at a more traditional proce-dure by discussing the data and comparing with other similarinventories as well as using an independent dataset based onpollen recordings.

Satellites using remote sensing instruments are wellsuited for finding forest trees and matches overall nationaltree cover based on forest inventories (Schuck et al., 2003).Current state-of-the-art forest cover estimates based onsatellite remote sensing include the global land cover prod-uct GLC2000 (Bartalev et al., 2003; Fritz et al., 2003) andthe Corine Land Cover 2000 (European Commission, 2005).These land cover products are based on the SPOT Vegetationand Landsat ETM+ instruments, which may be used fordetecting broad leaved and conifer forest areas. Therefore,additional information is needed to produce tree speciestree inventories. The present inventory is prepared as anoverlay for existing satellite derived land cover data suchas GLC2000 or CLC2000, which separates forest into conifer,broadleaved and mixed forests, thereby extending the useof these land cover data sets. A dataset gridded into theEMEP50 grid based on the GLC2000 data set is suppliedthis dataset and available for download at the web page:http://www.dmu.dk/International/Air/Models/Background/Trees/. However, Table 3 shows that overall tree density andtherefore also tree location will vary depending on the usedland use data set. The differences may be due to differences inobservation year, but for some geographical regions relativelylarge differences may be seen between different remotesensing instruments such as on the SPOT Vegetation andLandsat ETM+ instruments (Fritz et al., 2003). This meansthat transport models that dynamically calculate fluxes withthe biosphere such as emissions and depositions from and totrees are likely to give undesirable results if the land use dataare not synchronised, such as emissions based on the GLC2000data set and depositions based on the CLC2000 data set.

6. Outlook and perspectives

The results presented here address one of the main sci-entific challenges described in the COST Action ES0603(EUPOL to take place during the period 2007–2010)(http://www.cost.esf.org/index.php?id=1080). EUPOL isdivided into three work packages (WP), where WP 1 con-cerns pollen production and release. In WP 1 a main taskis the mapping of the distribution areas of the importantallergenic pollen types. This inventory considers tree speciesdistribution including important allergenic tree species suchas Alnus and Betula. The inventory does not include all thetrees relevant to seasonal allergic rhinitis such as Platanus.

are believed to be significant (Corden et al., 2000). This inven-tory is therefore not considered a solution but a contributionto this COST action.

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A recent review by Kanakidou et al. (2005) identify thecalculation of biogenic emissions as one of the main uncer-tainties in SOA calculations using CTMs. This uncertaintyincludes tree species distribution on the regional scale andgenera specific emission rates (Kanakidou et al., 2005). Thisinventory may provide further information for air quality cal-culations using CTM models.

Acknowledgements

This work was partly funded by the Copenhagen GlobalChange Initiative (www.cogci.dk) and Centre for Energy, Envi-ronment and Health (CEEH) (ref. 2104-06-0027) funded by theDanish Research Academy.

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