Atmospheric metal depositionbased on moss analysis:Which classification and mapping methodto choose for a relevant interpretationof actual deposition and critical loads?

Les dépôts atmosphériquesde métaux baséssur les analyses de mousses:Quelle méthode de classificationet de cartographie choisirpour une interprétation pertinentedes dépôts et des charges critiques?

Sandrine GOMBERT*, Catherine RAUSCH de TRAUBENBERG*,Laurence GALSOMIÈS**, Jonathan SIGNORET***

NDLR : Article issu du Séminaire ADEME « Pollution atmosphérique et charges critiques: Bilan et perspectivesdes recherches menées en France " , 15 mai 2001, Paris (MATE).

Abstract

France has been participating in the European program "Atmospheric heavy meta l deposition in Europe based on mossana lysis" since 1996 with the aim of obtaining data on metal depos ition on a large scale . Five moss species were collectedat a national scale within the framework of two surveys : 1996 and 2000 . The complete data for France of 36 trace elementsana lysed during the 1996 biomonitoring campaign is presented here for the first time . For ten elements (As, Cd, Cr, Cu, Fe,Hg, Ni, Pb, V, Zn), a comparison betwee n France and the rest of Europe, as weil as a comparison between 22 Frenchprovi nces is done using box plots. This work also prese nts the first consideration of the potential use of "meta l accumulatedin moss" data for asce rtaining actual metal deposition and crit ical load estimations in France. The methodological approachof presenting the results, classification of data ("European" , "K-means" and "Mouvet" methods) and mapping (spot by spotand interpolation) are considered, showing the impact of these methods on the interpretation and representation of the data.It is shown that (1) interpolation maps associated with the "European " or the "K-means" classification method provid e agood tool to visualize relative atmospheric metal deposition ; (2) interpolation maps associated with the "Mouvet" classificationmethod could provide a new method for critical loads and Iimits estimation , after further verification and potentia l modificat ions.

* Muséum national d'histoire naturelle, Laboratoire de cryptogamie, Équipe Bryologie , 12, rue Buffon, 7523 1 Paris Cedex 05.** Agence de l'environnement et de la me îttise de l'Énergie, Centre Paris -Vanves, Département Air, 27, rue Louis Vicat, 7573 7Paris Cedex 15.*** Université de Metz, Équipe de phytoécologie, UPRES-EBSE, Campus Bridoux. avenue du Général Delestraint, F-57070 Metz.

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Résumé

Depuis 1996, la France participe au programme européen " Atmospheric heavy metal deposition in Europe based onmoss analysis » dont l'objectif est d'obtenir des données sur les dépôts métalliques à grande échelle. Cinq espèces demousses ont été récoltées en France lors des deux campagnes nationales de 1996 et 2000. L'ensemble complet des données(36 éléments) de la première campagne française est présenté pour la première fois. Pour 10 de ces éléments (As, Cd, Cr,Cu, Fe, Hg, Ni, Pb, V, Zn), sont comparés : les résultats français aux résultats européens et les résultats des 22 régionsadministratives françaises entre eux, en utilisant une représentation en boîtes à moustaches. Ce travail constitueégalementune première réflexion sur l'utilisation potentielle des données " métaux accumulés dans les mousses » pour une estimationexacte des dépôts métalliques et des charges critiques en France. Plusieurs aspects méthodologiques concernant laprésentation des résultats, la classification des données (méthodes " européenne » , « nuées dynamiques » et " Mouvet »)et la représentation cartographique (méthodes " point par point » et « interpolation - ) sont considérés, soulignant l'impactde ces méthodes pour l'interprétation des résultats. Ce travail montre que : 1. les cartes d'interpolation associées à laméthode de classification " européenne » ou des « nuées dynamiques » fournissent un outil pertinent pour visualiser lesdépôts relatifs de métaux ; 2. les cartes d'interpolation associées à la classification ,de « Mouvet » pourraient constituer,après vérification et adaptations, une nouvelle méthode d'estimation des charges et limites critiques.

Introduction

The progra mme "Atmospheric heavy metal depo­sition based on moss ana lysis" has been recent lyjoined to an International Cooperat ive Programmeon the effects of air pollutants on natural vegetationand crops (ICP-Vegetat ion), which is one of worki ngg roups of the Convention on Lo ng-Ran geTransboundary Air Pollution from the United Nat ions(LRTAP, UN/ECE). The ICP-Vegetation has focusedespeci ally on air pollution proble ms and using cloverand moss data for quantifying the risks posed by theatmospheric heavy metal deposition to veg etation .One of the projects of this present ICP aims to providescient ific information for the Aarhus Protocol (1998)designed to reduce emissions of heavy metals. For adecade , the critical load concept has been introducedin negot iat ion s for the revis ion or elaborat ion ofproto cols to ensure an objecti ve assessment forach ieving maximu m ben efit of emis sion red uctionsat the lowest costs .

ln order to der ive the deposition levels at whicheffects of air pollutants start to occur, the criticalload concept has been defined as ua quantitativeestimate of an exposure to one or more pollutantsbelow which significant harmful effects on specifiedsensitive elements of the environment do not occuraccording to present knowledqe" [1]. The critical loadconcept is based on the calc ulation of a critical limit,which is the maximum concentration of a pollutantsuppo rted by ecosystems. It is the concentration atwhich no relevant harmful effect on specific receptors(micro-organisms, plants, human beings) wi ll occ urover the long-term [2]. These harm ful effects may bebased on observed toxicity of soil organisms or bio­logical processes, or on toxicity tests conducted inlaboratory [3]. Based on the conclusion of the BadHazburg and Schwerin workshops [4, 5], ecotoxico­logi cal basis for the ca lculat ion of crit ical limits insoils is weak. An alternative approach was proposedusing only free metal concentration (without any toxico­logical data) in soil solution, when such information

100

exists . For example, the Dutch Target Value (DTV) isbased on background soluble and adsorbed metalconc entr ation in unpolluted soils and gro undwaters[6]. The possibl e impact of a certai n load of meta lson ecosystems can be estimated by determining thedifference between the actual deposition load andthe calculated crit ica l load . It means that the nextstep for critical load estimation is the measu rement ofactual metal de position in ecosystems in order todetermine whether the critical load is exceeded or not.

ln France, netwo rks of phys ico -chemical metalsurveys ha ve been mainly restricted to urban orindustrial areas for ambi ent air: the lead network is ata nat ional scale [7], and there are more loc alizednetworks fo r cad miu m, arsenic and nickel [8, 9]. Afe w studies fo r metal de positi on have als o be enconducted in field experimental stations. A prim arystudy was done in an open field [10] and the EMEPnetwork has planned to prepare one add itiona l site insouth -we st ern Fr an ce . Ho we v er, these d ir ectmeasureme nts of actu al metal deposition are expen­sive, time consuming and requ ire heavy equipmentpreventing them being done on a nationwide scale.Oth er ap proaches ex ist fo r metal dep os it ion esti­mation in large area in France: (1) use of snow preci­pitation [11] and ice samples [12] to estab lish heavymetal map in the Alps ; (2) use of soils and humu s inVosges [13]. However, these prev ious studies havebeen mainly restricted to local or regional areas forinvestigating deposit ion fluxes. An important prob lemfor cri tical load estimation in France is the lack ofactual deposition data on a national scale .

The utilisation of metal bioaccumul atin g plants isan indirect method for the estimation of metal depositionon a large scale . Mosses have been frequently usedto measu re metals in the env iron ment for thirty years[14] and their effic iency to acc umulate meta ls is weilknown [15]. In epige ic mosses the lack of a root andvascular system (as seen in vascular plants) mea nsthat much of the wate r and nutrient input is from dryand wet deposi tions. Some moss species are commonand widespread. Th eir sampling is easier and less

POLLUTION ATMOSPHÉRIQUE N° 173 - JANVIER-MARS2002

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expensive than using phys ico-chemical collectingequipment. Furthermore, the exposu re period ofmosses is severa l years , which is an importantadvantage for making maps of metal deposition.

A biomonito ring net work for estimating back ­ground atmosp heric metal fall-out at national scalehas been in place in France since 1996 at more than500 sites [16] . Th is French moss biomon itoringnetwork is also a part of a larger internationa l bio­monitorin g network. Studies on atmosp heric metaldeposition based on moss analysis were initiated inScandinavian countries at the end of the 1960's [1 4],bein g enlarged to Nor thern Europe with nat ionalsurv eys in the 1980' s, then in 1990 expand ed tocover the rest of Europe (about 30 countries) withthree su rv eys plann ed eve ry fi ve yea rs. Th eEuro pean programm e, or iginally propose d by theNordic Council of Ministers with a view to estimatingmetal depo sition , has since been incorpo rated, in2001, into the ICP-Vegetation programme from theUnited Nations. A review process has begun to definehow more detailed information on ecosystem riskscan be provided using the moss data.

The purpose of this present work is:

• To present the complete data set of the 1996French moss survey , including 36 analysed elements(an incomplete data set was published previously [16]);to compare the French moss data with those fromEurope and to compare French data between andwithin the 22 French administrative provinces for the10 co mmon meta ls an alyse d: ar seni c (As) ,cadmium (Cd), chromium (Cr), copper (Cu), iron(Fe), mercury (Hg), nickel (Ni), lead (Pb), vanadium(V), and zinc (Zn).

• To give a preliminary view on poss ible presen­tations and optimisat ions of these data as a base forfurth er discussions in the context of critical loads.Espec ially , on how best to gro up these data intoclusters in order to interpret the derived maps in aspatio-temporal context (European or French scale,from one survey ta another).

The aim of this paper is thus to compare threeclassification sche mes and two mapping methods.Other questions are: how could metals accumulatedin mosses give information about the atmospher icdeposit ion and fluxes of metals? How could theresu lts of this survey based on metal analysis inmosses be useful for the calculation of critical loadsvalues and critical load exceedance?

This paper is not a complete Interpretation of theFrench 1996 "moss-metal survey". It must be consi­dered as a preliminary investigation on the use ofdata provided by the "moss method" in the context ofcritical load calculation .

Materials and methods

The choice of moss species and the samplingme tho d applied in France is large ly bas ed on

POLLUTION ATMOS PHÉRIQUE N° 173 - JANVIER-MARS 2002

Scandinavian recommendations [17] that were adaptedto French ecological conditions as described below.

Recommendations before metal analysis

As numerous metal sources from air are presentin many environments (urban sites , industrial andmining sites , volcan ic sources ... ), the utmos t caremust be taken to avoid contam inations dur ing thesteps from field sampling to c1eaning in the laboratory(sampling, transportation, sorting, c1eaning and storage).Precautions to avoid contami nation must be taken:plastic gloves have to be worn, smoking is prohibited,airtight storage should be chosen (to prevent conta­mination and lost of volatile elements like mercuryand arsenic), the material should be handled in c1eanenvi ronme nts, and plast ic tools shou ld be used(previously soaked in acid solution and rinsed withde-ionised water).

Moss species

Moss species are mainly chosen based on thembeing terricolous , abundant and ubiquitous speciesthat are easy ta identify. The mosses collected inFrance during the 1996 survey are Pleurozium sen te ­beri (Brid.) Mitt. (n = 112 samples), Hylocomiumsplendens (Hedw.) B.S.G (n = 52), Hypnum cupres­siforme Hedw. (n = 200), Scleropodium purum(Hedw.) Limpr. (n = 160), and Thuidium tamariscinum(Hedw.) B.S.G.(n = 39), in order of their suitability .

Field sampling

Sites were chosen far from any local source ofpollution, being located in non-urban areas, to estimatethe background pollution as weil as the possible long­range transport of air polluti on. A regular networkcovering the whole of France (512 sites within the22 administrative provinces) was used with an averagesampl ing density of almost one moss sampie per1 000 km2 [16, 18]. The moss network has also includedsome of the forest sites from the French permanentplot network for the Monitoring of Forest Ecosystem(RENECOFOR) manag ed by the French NationalForest Office (ONF).

Sampling was done preferentially in dry conditions.Samples were mainly taken from forest clear ings ,situated at least 3 m away from trees, so they werenot directly expo sed to throughfa ll . Compos itesamples were made from one sampling point consistingof 5 to 10 subsamples collected within an area ofabout 50 x 50 m.

Sample preparation and analysis

The c1 eaning, sorting and dehydration (at 40 OC)of mosses were carried out fol lowi ng the Scandi­navian guidelines [17, 19] and are detailed in previcuspublications [16, 20].

Analysis of 36 elements for the 1996 moss surveywere performed at the Pierre Süe Laboratory (CEA­CNRS, Saclay) using two multi-element methods :

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INAA (Instrumental Neutron Activation Analysis) for32 element s, and ICP-MS (Inductiv ely Coup ledPlasma Mass Spectrometry) for 4 elements (Cd, Cu,Ni, Pb). These methods were agreed upon by ananalytical comparison study carried out by the partici­pat ing countries [21] . The analytical deta ils havebeen described in earlier papers [22-24]. Results areexpressed in l-lg of metal per 9 of moss (dry weight at40 oC).

Descript ion of methods for classification

The classification is a way to group similar datainto a number of clusters with each cluster containingthe lowest number of samples possible to facilitatethe interpretation of the data. Different algorithms ofclassification can be used according to the differenthypothesis of the work.

The "European" classification

The "European method" has been determined atthe European level and is currently used for the reali­sation of the European maps [19, 25]. For the deter­mination of different classes, no statistical test wasused but rathe r an empiric al method for gett ingappropriate maps at the European scale. The clustersused for the 1995 European survey were elaboratedfrom previous Nordic and European surveys [17, 19].The data set was divided into eight concentrationclasses of equal intervals from the minima to maximavalues for each metal. However, for Cr, Cu, Fe, Pb,Ni , V and Zn, the highest class Iimits have beenchanged (increased or reduced acco rding to theeleme nt) since the 1985 to the 1995 Europea nsurveys. These changes were linked to a decrease ofsome of the highest values obse rved between thesurveys (often in the Scandinavian countries) and toan extension of the programm e to countries fromEastern Europe where highe r concentrations wereobserved for some metals [Rühling, pers. com.].

The classification using the "K-means" algorithm

This method has not been used at present for themapping of European data. Initially, this non-hierar­chical method translates the number of componentsof a popu lation to the fi nal req uired number ofclusters , which are generated to be as different asposs ible. The final required number of clusters ischosen from the points that are mutually farthestapart. Next, each component in the population isexamined and is assigned to one of the clu stersdepending on the minimum distance. The centroid'sposition is recalculated every time a component isadded to the c1uster and this continues until ail thecomponents are grouped into the final required numberof clusters.

The classification of "Mouvet'

The "Mouvet 's method " is current ly used inFrance by the French Water Agencies to determinethe classes of metal concentrations measured in

102

aquatic mosses sampled for the estimation of watermetal pollut ion. It has been set up by Mouvet [26]and was adapted by Mersch and Claveri [27]. Thismetal biomonitoring survey using aquatic mosses inrivers is similar in many points to those set up for theair pollut ion using ter restrial mosses: both have alarge number of site s locate d in French territory(about 400 stations), successive surveys (each yearsince 1992), a large set of data , and the pollutantsanalysed are metals. These similarities allow us toadapt th is meth odolog ica l approach to terrestr ialmosses.

The method is base d on a referenc e concen­tration which is defined by the concentration valuewhen there is no pollut ion. According to Bonnefoyand Bourg [28], this concentration can be calculatedwhen the set of data is high in a large territory, bysuccessively eliminating the highest values until astandard deviation of 60% is reached. In this way,the distribution of values is closest to a normal distri­bution.

The reference concentration allows the limit ofclasses to be calculated and pollu tion load in theecos yst em to be def in ed . A twofold geom etri cprogression of the limit of classes is chosen, exceptfor the cluster 1 for which the upper limit is threefoldthe reference value. In this way, the five upper limitsof classes are defined as follow: 3 times the reference,unpo lluted situation (c1 uster 1); 6 times the referencevalue, suspected po llut ion (c1uster 2); 12 times therefe rence val ue, certa in po l lut ion (clu ster 3) ;24 times the reference values, important pollution(cluster 4); exceeding 24 times the reference value,exceptional pollution (cluster 5).

Desc ription of calculatio nof the po lymetallic index

The polymetallic index is based on the addit ion ofconcent rations for every metal in each site. However,as some great differences exist between elemen tconcen tra tions into mosses, the data had to bestandardised (subtracting the mean and dividing bythe standard deviation) before adding values.

Descrip tion of methods for mapping

The spo t by spot map

The spot by spot map was drawn using Arclnfoand ArcView 3.1 GIS. Each spot indicates a samplingsite and represent s a concentration range (class)which corresponds to the value of metal accumulatedin the moss species. The different sizes and coloursof spots were chosen in accordance with those usedby the European programme [25]. In the case of twomoss species per site, the greater symbol corres­ponding to the higher value was put under the lowerspot so not to mask it.

POLLUTION ATMOSPHÉRIQUE N° 173 - JANVI ER-MARS 2002

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The interpolation maps

For the European maps

Th e interpolation algorith m was Kriging with aIinear drift component added. This procedure involveda transformation of the data from the irregularly spacedsampling sites ta a regular grid. Ta switch off theinterpolation wh en the re were not enough datapoints, the followingsearch criteria were used: maximumdistance equal ta 60 km, at least one data point ineach quadrant, multiple data points in a grid cell werereplaced by the average. The overlay of geographicboundaries have been done using Arc info software[Olsson, pers. cam.].

For the French maps

Inverse dista nce weighted (IDW) interpolationdetermines cell values using a linearly weightedcombination of a set of sampie points. The weight isa function of inverse distance.

ln the case of French maps, the characteristics ofthe interpolated surface is controlled by Iimiting theinput points for calculating each interpolated point bya radius equal ta 60 km. IDW allows control of thesignificance of known points upon the interpolatedvalues , based upon their distance from the outputpoint. The interpolation can be shifted from local taglobal scales by changing the power. A higher poweris chosen because it results in less influence fromsurround ing points, i.e., nearby data will have themost influence, and the surface will have more detail(Iess smooth). A common value of 2 is chosen forthat the relative influence of more distant pointsdecreases.

Results and discussion

French results of the 1996 moss survey

The main results of the 1996 French moss surveyare presented for the 36 elements analysed but thispaper will mainly locus on the 10 metals common taail European countries: As, Cd, Cr, Cu, Fe, Hg, Ni,Pb, V, and Zn. The 26 other elements are given tamake the first information on them available, but theyare not interpreted in detail here.

Comparison with European results

The T ab le 1, p. 104 , presents Fren ch data(36 elements) compared with results (10 metals) frommore than 20 European countries participating in the1995 European moss survey [25]. For As, Cd, Cu,Fe, Hg, Ni and Zn , French medians are close tathose ca lcu lated from the rest of Europe [20]. InFrance, the medians are only slightly lower for V, butslightly higher and twice as high respectively for Pband Cr. In the case of V and Cr, the French resultsare underestimated and overestimated respectively,which is probably due ta the analytical method used[23]. Among the 10 cammon metals, France presentsneither the highest nor the lowest meta l concen -

POLLUTION ATMOSPHÉRIQUE W 173 - JANVIER-MARS 2002

tration in Europe. Although variation coefficients arerelatively low for some elements (41% for Zn, 52%for Cu) indicating homogeneity of metal exposure ofFrench sites, they are large for other elements (up ta130% for As) indicating heterogeneity between sites.

Comparison between the 22 French regions

France is divided in 22 administrative provinces(Figure 1, p. 105). Figure 2, p. 106-110, presents the1996 French moss data set for As, Cd, Cr, Cu, Fe,Hg, Ni, Pb, V and Zn, using box plots in which eachreg ion is ranked accord ing ta an increase of itsmedian value. Box plots allow us ta describe thedistribution of data within the 22 provinces of France.

For most of the metals, no important concentrationvariations are observed between the overall valuesmedian from the 22 regions. Furthermore, values areweakly scattered (25-75% values) over the mediansfor As, Cd, Cr, Fe, Pb, except for Cu, V, and Zn(Figure 2, p. 106-110). The minima from regions arecomparable for each metal but differences betweenmaxima are observed for same metals. For Cd, Cu,Pb and Zn, maxima are found in some specifieprovinces sho wing higher median val ues. Highconcentrations are observed in some areas known tabe urbanized and industrialized, mainly in the Ile-de­France area (Cd, Zn), the Nord-Pas-de-Calais (Pb),the Picardie (Cu) and the Rhône-Alpes areas (Cu,Zn), and in the agr icu ltu ral are a of Champagne­Ardenne (Cd). Some maxima are also found irregu­larly scattered within provinces where the mediansindicate a relatively low level for some metals, revealinghot spots of contamination (for example in Centre forPb, Auvergne and Rhône-Alpes for Fe, Bretagne andMidi-Pyrénées for Ni).

ln arder ta go further in the interpretation of theseresults detailed provinces by province, the box plotpresentation can be completed by a calculation of apolymetallic index. Figure 3, p. 111, presents boxplots of the "polymetallic index" (10 metals) from the22 French provinces . This pres entat ion of resultsconfirms some of earlier conc lusions: the lIe-d e­France, Picardie, Nord-Pas-de-Calais , Languedoc­Roussillon, Champagne-Ardenne and Rhône-Alpesareas appear ta be the six provinces showing themost important polymet allic concentrations withinmasses, and which are certainly exposed ta thehighest levels of metal loads. However, the box plotdoes not give a spatial representation of the scatteredvalues. Mapping of the results overcomes this problem.

Methodological approach for classif icationand mapp ing illustrated by some examples

The choice of the method of classification, as weilas the definition of class limits, have great influenceon interpretations of mapped results. The first step totransforming data into maps is to classify the dataand group them into clusters. In order to discussdifferent methods of classif ication and mapping ofdata, Cd and As are chosen as examples.

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ranc

e1.

008

.10

-416

30

.01

0.5

385

90

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202

130

.10

780.

07

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ope

0.40

18

.20

.00

10

.03

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ndar

dde

via

tion

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nce

3.17

0.0

3963

90

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519

613

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52

824

20

.58

202

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0

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nF

ran

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130

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744

0.0

72

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881

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1076

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'(%

)F

ranc

e5

213

286

5768

34

183

4983

8776

93

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(ini.1

g/g)

Pb

*R

bS

bS

cS

eS

mT

hT

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WZ

nZ

r

Num

ber

of

sam

ple

sF

ran

ce55

8(0

)55

9(1

)55

9(2

)55

9(0

)55

9(1

84)

559

(3)

529

(7)

558

(18

0)

559

(1)

55

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07)

55

9(3

)55

3(3

59)

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6823

6595

6875

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6.8

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ian

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8.88

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483

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4187

» JJ --i o r m (J)

--------------- ARTICLES

Figure 1.Location of the 22 French administrative divisions (provinces).

Localisation des 22 régions admin istrative s françaises.

o

Classification

Below, the three different ways of classificationchosen for our purpose are presented.

The "European" classification

Eight concentration classes are proposed by theEuropean programme [25] for each of 10 commonmetals analysed. When we apply these eight classes(ranged from the lowest to highest concentration) tothe 1996 French moss data set for Cd (Table 2,p. 111) and As (Table 3, p. 112), almost two thirdsof values are represented in the classes 2 and 3.These eight European classes used in the exampleof the French data set allow to compare French andEuropean maps, but probably do not ensure the bestpresentation of the French data for mapping at thenational scale. For this purpose, other methods ofclassifi cation ("K-means" method and "Mouvet"method) are proposed for use and detailed below.

POLLUTION ATMOSP HÉRIQUE W 173 - JANVIER-MARS 2002

GERMANY

The "tc-moens" clus tering

ln the case of Cd and As results, five classeshave been determined (Tabl es 2, p. 111 and 3,p. 112), where more than 90% of the values arerepresented in the classes 1 to 3. Using this method,the lower and upper bounds of clusters correspond tolower and upper data values of the chosen data set,and differ according to the considered area (Europeor France). Whereas this method is statist ical andnon subjective, the main inconvenience is that theclass limits will vary according to new surveys andwill have to be re-calculated each time to ensurecomparisons.

For these two methods, the different classes arenot associated to a quality or ecotoxicology scale i.e.,the highest c1ass which represents the highest valuesfor metal contamination does not necessarily indicatea high level of pollution or even a polluted situation

105

ARTICLES _

5,5

5,0

4,5

4.0

3.5

3.0

2,5

2,0

1,5

1.0

0.5

0 .0

.Arsenic . (7.50).. ....

:,

...

•

1···

1 ·

T

T•

T T 1

l:J . ~ IG~J ni ~~ 8.... - :[ ~ W l:J ~ G [J B [ ~ .~w ·~ [

.L

-r-

(17.60)

1- Europe'

<Il <Il <Il <Il i'! <Il i'! c li) li) <Il <Il <Il <Il~

li) .<Il <Il li) <Il <Il C

~C li) '5 c ,n I ii <Il c C u C <Il Ë ~ " o c gCl Ci ë 2 ·0 ë Cl c '" ~ Q. ' <Il C 'êc '" ïii 0 <Il li) '0 0 '" c ~ ln(ij '" '" <Il --' 0 i'! " «'§ o o ':; lb o ~ -o « <Il o U ." u, 0

li)E E E '" ~>

'" 0:: s, '"Ci Ci0' cl; :.J <iJ .I:: '" '" '0 '" a, --' 0 =c MaxtD « 'C o 0

'"« y c .I:: 0, " 0::z Z " ci> œ '0 o '5 =* Cl Mina, li) Cll ::, C li) .I:: C<Il »,

Cl- .9Cl <Il 0:: e ~ 0

0on 's '" '" Q. 'C 75 %on '0 Q.

4=u, <IlCll '" 0- 'E E '" 25 %œ I 0 0-

eo 0:: ClZ .I:: CMed iano œ

--'

.~ ~ : :- . :-..... . ~ - ': ';" .. " :.." : .

...L ; + L. .J

1,6

1,4

1,2

1,0

0,8

0,6

0, 4

0,2

Cadmium

• • • ~•• •••••~.. • • __ . 0 •••••• • ~ ' •• ' • ••• ~.

......;-. .;. ~..

··· ····t··· ·· ·· ··t· ········/·

. ~ .. ..:-... . : i ··· . ~ . . ; -1" . • . • • . : ••. . . ! .. • . . . . . ~ •• ·~··· ··· ···-f· .....-. ! .., ....1' ..,...

( 8.40)

Europe '

N CD CD CIl CD CIl ~ CD CD e <Il C CIl CD C CIl œ CD -œ CD CD œ-c c c œ~ œ c '5 c .2 ' (ij '5 'Iii a c o § 'E 0 c

Cl :§ ë 0 -œ '0~ c ë 8' ln ïii c il , ii '" c c'0 œ i'! o c --' œ œ '" 4= t:: on '" ~ <Il

CD ë 's ~ cO œ o ~ CIl Ü~ E .3 « ü 0 u, 'E'" > ~ il ch œ 0::'15 0' >. di '" il :.J ch a, <;: =c Maxœ « .I:: 0, « 0 V 0 C

.I::Ü Ü~ <;: œ a: Z <0

0 V œ MinCIl ::, '5 8 CIl .I::~

C

'" '" cD a: cCla 0 ~ >.

CIl 0- :; ~ <Il CJ 75 %(5 <Il CIl '0 'è lL Q.« 0- <Il œ '" E 25 %0- œ '" 0 I0:: Cl Z <Il

C .I::Median.s o

Figure 2.Box plots of the 10 metals for the 22 French admin istrative divisions. Regions are ranked according ta an increaseof their median values. On the right of the figure, maximum , minimum and median values for Europe are indicated .

Boîte à moustaches des 10 métaux pour les 22 régions administratives françaises. Les régions sont classéespar ordre croissant en fonction de leur médiane . À droite , le maximum , le minim um et la médiane sont indiqués pour l'Europe.

106 POLLUTION ATMOSPHÉRIQUE N° 173 - JANVIER-MARS 2002

- - ---- - - --- - - ARTICLES

Chrorniurn30

25 .

20 . .. .

,: :

: :.....

...............

(438)

15

i ~: :

10 r" ;...,.. .

. 1

C ID ID ~ Ul Ul.., C Ü ID ID:J Cl c ë ë ..,0 ro ro ID

~c

E "§ E o ro.~

::; CD s: »,0 q.Z oID :J '0V> .9 ~V> 'ôœ

CD Cl.

,ID ~ IDc ÜCl 'ô cQi -,J ro> .!l1 E:J dl« 0

'9 zUl .è»ro :JCl. œ

I

Europe'

ID Q) ID ID ' ID c .i ID ID Ul ID Ul IDC e C o Ë g c U ID

~ t ii c~ 0 Cl ro c c; c. "ê0 Ul 0 'ili '0 ID ~ 4: ëii'S o l? 4: o Ul 1:'

roUID U

CT :J dl :J-0

u, dl 0:: dl 0« 0 0 « dl c ...J :::c Max

CD oC 0: Y dl 'C -0 'CU U c .4? s: en Minc Ul

~ 0 ID Cl - 0: ro'C o, ro C)- D 75 %u, ID <j: c.

1:':J E 25 %Cl et ro 0c oC Zro U e Median

...J

20

15

10

Coppe r [33.1

,....

T

J,1

aa n

J ,Il a

lnV ·]t~ i·H l ~ .~. +l a . [Ja a -}k · ····· f·a c

ŒJ

(650)

_ Europe '

ID CD ~ Vl V> ID e C ID ID ID ID ID ID N V> c :$ CD Vl CD ID

~ C ID ID c 'ili Ü c Ü c o c '0; .Q c s 'ê oCl <:5 ë -œ 'ili ë El :§ Cl! 8'

« 5 c c0 s c c '0 ëii ~

ÜCl! -' ~

c t:: ID Cl! ID Cl!Vl CD <i: Cl! ~

"§ Ca ~ .3 o E E > E'5 <i: l? ID Ü Vl Ü 1:' cD

o LL

d;Cl! », :J cr :J .0 ch s ID -c a:: ID ---r-

CD s: q. ::J 0 « 0 « 0 ", cDc Max

", o z z CD o cr: s: -0 ", Min:J '0 <il Vl 8

o c s:Vl ID cD Cl! c Cl cr: ~e-, 0 ~ :; Vl a Cl. ~ Cl! 0Cl! :5 Vl 'è al LL 0. 75 %

Cl. Cl! Cl! ~Cl. I co 0 :J E 25 %et Cl Cl!Z c 6Cl! Median

-'

Figure 2 (suite).Box plots of the 10 metals for the 22 French administrative divisions. Regions are ranked accord ing ta an increaseof their median values. On the right of the figure, maximum, minimum and median values for Europe are indicated .

Boîte à moustaches des 10 métaux pour les 22 régions administratives françaises . Les régions sont classéespar ordre croissant en fonction de leur médiane. À droite, le maximum , le minimum et la médiane sont indiqués pour l'Europe .

POLLUTION ATMOSPHÉRIQUE N" 173 · JANVIER-MARS 2002 107

ARTICLES _

6000

5000

4000

3000

2000

1000

o

Iron i

,n~ 4. ~ o ~ Q [ [ [0 I t l 1

0 o .. .~ . . .~.10

~ 0 [0- J ':t

(18600)

Europe'

c 0) 0) 0) 0) 0)~

0)~ '" 0) '" ' 0) 0) '" 0) 0) c 0)

~ '" 0),;; TI TI c c o c 2 c 0)Ë c 0) c o g E " ~

:J :§ C» ro C C» ,5 c E' ' 0) .~ 0- C C (ij 0c c ro '" 0) 0 -' c 0 « 0)~

,;; ro u l Max0 ro œ ':; 0; « E' .!!l ~ 0)'~ o Ci -o '" o 0) U oE E E o >

'" 4:u, :J 0::

'"CT cD :J ro :J », ID -' ID ;Q::::; ID s: c 0 Min0 0 -c 0 o -c 0;- .<: '0 '" -o 0:: U -o

Z Z al u o in

'" :, TI .<: c~ U inb b c 0:: ro 0 75 %», s ~ ~

C» 0 0) 0-'" S ro ro u 0-

"è'" ro 0- '0 u, 0- 0) <j: 25 %roI 0- E :J 0al co Cl

.<: c 0:: zMediano ro

-'

0,35

0,30

0 ,25

0, 20

0,15

0,10

0 ,05

0 ,00

Mercur v -. .....,... ....

T

-

l

IJ 101 [0 "1 !" ~e

i ~ lU rJ I ~ r~ . ~c c

D .~."

in 'nI ~0

...........T ··tt· 1 lo 1 1

l''33J

Europe '

ID 0) ID -ID ~ '" ~ 0) ID ID 0) 0) 0) c Ul 0) ID C 0) Ul N Ul~ c o § ID C TI ï5 c c o ,;; ID C c ..Q 'Ë a <{ '1,0 'ê ro ë ' ID '0 Dl C c 8' c c

15 ë E' '§ 1? '0 (ijÜ

Ul ID C -' '" '" '"0)

~ e 0) '" :;:;: o:;:;: o o .~~ '§ E' "E E 'S Ul o 0)0 E E u,

'" > CT 15 0:: '" cD-' cD >- cD al 15 "f 0) ::::; s: :J c <ë5 l Maxs: 0;- 0 00) '9 Ü -c « a: '0 t.f uu Z Z al

'" Mino TI en c~ :, 8 .<:: Ulc cD cD Dl a: '"~ ~

c-, 0 a 0-'" S Ul '" E u

"è c:::::::J 75 %u, 0- '"Ul o, œ <j:I '" E 0- :J 25 %al 0'" Dl 0: z6 c

Median'"-'

Figure 2 (suite).Box plots of the 10 metals for the 22 French adrriinistrative divisions. Regions are ranked according ta an increaseof their median values. On the right of the figure, maximum, minimum and median values for Europe are indicated.

Boîte à moustaches des 10 métaux pour les 22 régions administratives françaises. Les régions sont classéespar ordre croissant en fonction de leur médiane. À droite, le maximum, le minimum et la médiane sont indiqués pour l'Europe.

108 POLLUTION ATMOSPHÉRIQUE N° 173 - JANVIER-MARS 2002

- - - -------- ARTICLES

20 ~" " h h " Nickel ! ' + +" ' ; 'T " h" ') "'hh":" ' : ' .. :

:, : ; ; ; ; ; i ~

(235)

12 [ ' ! ih 'hhhhi "+h 'ihhh' i-h.. 'hhh.! . j .[. . .;. h.hh;h . : .I.'hhh..;. L. h.'h..h.h!hhhh-!-. ! ;" h "hhh" : . .1

8 >h" ' ; hi ' h' " 'h~hhT"""" .! +.:··· ·! l! L !.hhh. i ll '! h'hhi ' lh" hh" + 'hhh':' hhh" "!hh" hhh" :hhTh';'" 1

6 ~

i ~T 1

1 T T

.•~.4

' ~2r r .r JI r r' nll i~ i8

p..~. I l

G ~ ~ ~ ~ 'i ~ L: ~ 1 T0

cr Europe'

0) 0)~ en c: :g ~

0) en 0) 0) 0) c .:;! e 0) 0) .., ene c: 0) 'ii; c: 0) o., en 0)

Cl ,5 ë ë 'ë c: .Q~

c 'ë Ë., s ' 0 o

0 ::l .~ Cl .0) CIl~

ÜCIl --' ~ 0 0) 0 c: en ffi 'ii; 'a E' c Cl. Cii c

"ê S E a o E' .~ ~ '5- en CIl 0) CIl 0 ~ 0)~

CIl c: 0) o > E o

" "E '-?CD cU <3 ::i --' ::l c-, c: 15 ;0 il: ::l " « 0)

u,

0 q. 0 « « 0 c: cU =r.::: Max"0 CD Z 0:: Ü or; -0 6 "0 "0en ::, 'ë cU U en z o or; §, in

~Min

», .9 ~ 0 ., cU c 0:: CIlCIl '0

en "0 Cl. "5 ~ CIl 0.. Cl0.. en 75 %0.. CIl '" 1'- CIl u, Cl. "è

CD ::l I E 25 %Cl 0: CIl 0c: or; ZCIl Ü

, Median--'

80 f " h;hhh' ;"h'h"!' " h!.hhh.' . hhh; ;.hhhh'h h.h.' ;..hhh. ihh··· ·;· · I ····;··········I j ,..•.. ···f··· ! { , j .•.•. ...

Europe

(443)

=r.::: MaxMin

Cl 75 %25 %

c Median

., I . • . • . •-.j ; , .

Lead

0) li) 0) li) ~ N c: 0) 0) 0) 0) 0)~ ' 0) 0) 0) 0) li) c: 0) li) 0)

c: 0) c: 0) « 'iii c c: ~ .S ïJ g o c: ïJ a .Q~

'c;; o

2 ë œ -œ 'e'a s 8' E' 0 ê c: ë CIl c: c: 1J5 Cii

c:

e CIl c: --' 0) o CIl0) li) 0)

CIl « CIl ~'s ~ ~ ~ .!!l E E' > 0 E o o « 'E ê "

li) o y u,rr CIl s il:

or; CD»,

ci> -0 ::i :::J :::J --' " -c c: 0) dl-cÜ q. Ü 0 « 0 or; "

0 -0 CI: ~ "01J CD Z o z

:J '6 li> in c: or; S '*dl c: œ ci> CI: CIl

.9 ~ >- a li) ~ CIl "5 0..

'ôCIl li) U. 0. -g 120... <!= CIl E

CIl

0... al I :::J 0

0:CIl œ

<5 c: zCIl--'

f ' .+.hhh.!.. hhh; h!.hhh.+.hh.' -l-..hh.i.... . !hh.h !. hhh 'h ' .. ; j - ~ ; j' ~ .

60 f " '+hh' +' hhh" h' h"hhh'i-'hh"!hh"" hhh"!' h h '.hhh h" 'hhh; . .. ., . j .' .. . '.h .! . . .. ']' . . . , . . .,...

100

Figure 2 (sui te).Box plots of the 10 metals for the 22 French administrative divisions. Regions are ranked acco rding to an increaseof thei r median values. On the right of the figure, maximu m, minimum and median values for Europe are indicated.

Boîte à moustaches des 10 métaux pour les 22 régions administrat ives françaises . Les régions sont classéespar ordre croissant en fonct ion de leur médiane. À droi te, le maximu m, le minimum et la médiane sont indiqués pour l'Europe.

POLLUT ION ATMOSPHÉRIQUE N° 173 - JANVIER-MA RS 2002 109

ARTICLES _

18

16

14

12

10

8

6

4

2

a

Vanad ium

........ :..........: . .. ......

.. .. ..~ ... ····1··· ........:-...

,,.. ..,

.... .....,

: : : ....~....

..i : 1

H"

....:.... .

u] '[1 ' [00• • • • • • • • ~• • • • •• • •• "1- ' _. • . _. ( .•

,J G: : :

··8 H ô Lo

~ 'W

i ~ j '·8,.8- I ~ :8é;·o'" 0..,.. 01 .1.

...

j ~ ~ 1 j

(54 .2)

Europe '

c Q) <n e Q) Q) Q) Q) Q) Q) e <n Q) Q) Q) <n . Q) Q) <n~

Q) cën c Q) c c c c 'ë c Q) c o 'ë Q)

Ë <n ëli 'ë g::l CIl E ë El ~

Cl Cl c 1~ë5 ' Q) c c Ct;

o, a ro u c ën0 <n~

Q)Q) CIl 0 CIl ....J C Q)

~ <ë 0 o CIl<ë o '" a; El a dl . ~ -c U U U <nE CIl > E ~u, 0:: Q, dJ

Q) E ::l::l 0' cD ::l ....J d; », dl Q, ;Q l Max::} oC~ ~ 0 0 0,- C "tJ 0 0u CD 1= "tJ Q, "tJ -0 s: .n o z 0::

Min:, J, 'ë c~

s: o .n <0c CIl dls Q) >- ~Cl 0::

~ q. Q) 0<J)CIl CIl C. S "tJ D 75 %'s <J)0- c, u, t'

~CIl Q)CIl E 25%0- CD 0 I ::l

CIl z 0: CloC cU CIl Median

....J

Zinc,...• . ...: (850)

100 e···.. ;··· .. ··· , ·.. ·, ..· ·;.. · ; , , ; ; ; ; , -r- .. ;.. ; ; ...; , . ··,· ··;··1··' · ;··1

80

60 T .. ...... l -.. C... 1" ;

l i: ~l

1 J!ti . ~ [J a

40 r

J [.1 Hl a

o i8 ~ [~ a ~ [~ [J ~ . TLL ... ..20 :1 1 .t l l

: i Europe ·0

ID <J) N ID C ID <J) C e ID ID ~(J) ID ID ID (J) -œ ID ID (J) ID

!'! ID C ,g C ID ën c ïJ a 'ë o c Q)

Ë ::e c ' ëij o« ë <5 c c0 -œ'0 8' ~ ë s E' c c CIl . ~ Dl roc ' ijj

~ID CIl ....J <ë CIl

(J) c 0 CIl CIl ~U (J) '" ID dl <ë ID Ü U~ y~ ID E'

i3 CIl E o > E cD E .3 'E iL LL>- <5 0' :::J di cD ID cD l Max0,- s -c s: ::} -c 0 c 0 -c CD '0U a: U <0 s: V'ë ci> CD

8 :J z ~ s: Z cD o ci>~

MincD a: cD c c CIl~ a 0 (J) 0>-

S Dl ~ 0- D 75 %'0 '5 (J) CIl CIl LL 'é~ ID CIl 0- CIl a. 25 %:::J 0- CD I E 0n: Dl zc CIlMedianCIl s:

....J Ü

Figure 2 (fin).Box plots of the 10 metals for the 22 French administrative divisions. Regions are ranked according to an increaseof their median values. On the right of the figure , maximum, minimum and median values for Europe are indicated .

Boîte à moustaches des 10 métaux pour les 22 régions administratives françaises. Les régions sont classéespar ordre croissant en fonction de leur médiane. À droite, le maximum, le minimum et la médiane sont indiqués pour l'Europe.

110 POLLUTION ATMOSPHÉRIQUE N" 173 - JANVIER-MARS 2002

- ----- - - - --- -- ARTICLES

3

2

o

-1

Poly metallicinde x ,. .......

:..

1

~ ..~.. i ~ r0 0 0

~~ [ ~T T

[e) ..~T .~ . 1.

Q~ ~ ~ ~ , ~

[:J ~ r l l

e ID <J) ID ID ID ID ~ c ID <Il N ID ID -œ ID <J) ID C <J) ID ID'0 C ID C C c '6 ïD ~ ID « 0 '6 Ë c ID C g ï'ii 'ë 0œ C 1§ œ El c C :J 0 -œ

'0CIl c . ~ C>- C c;; C

--' <Il e 0 ID 0 C <J) 0 <t' ID '00 CIl ~.!il ~'S El ID CIl

Ü Ü ~ <t' CIl0 E <J) ÜCIl > E E ID E o ch 0 LLcr :J IDch co ..c :J :J :.:::J >- ;0 ch --' « 0 if: ch ::::r Ma x

Ü-c 0 « 0 a, o 0 ..c ,5 ch ""0"Ç co z z 0 cr: cl> ""0 Min<J) ::, ch '6 en ID

..c c 8 ~>- 0 ~ IDC cr: Ol CIl

CIl 'i5<J) C>- :; ~ CIl ""0

D- O 75 %<J) C>- -èD- CIl ~ CIl LL IDD- co I E :J 0 25 %

0: CIl œ..c c Z

MedianÜ CIl--'

Figure 3.Box plots of the polymetallic index (sum of the 10 metals values of each site) for the 22 French administrative divisions.

Boîte à moustaches de l'indice polymétallique (somme des valeurs des 10 métaux de chaq ue site)pour les 22 régions admin istratives françaises.

Table 2.Classification of the cadmium data according to the three different methods :

"European method", "Kmeans" method and "Mouvet's method".

Class ification du cadmium selon les trois méthodes de classification :Méthode européenne , méthode des nuées dynamiques, méthode de Mouvet.

Clusters

1 2 3 4 5 6 7 8

Limits of classes < 0.1 [0.1-0.2[ [0.2-0.3[ [0.3-0.4[ [0.4-0.S[ [0.5-0.6[ [0.6-0.8[ ~ 0.8

Europeanclassification

% of values 5.9 33.6 29.6 16.9 6.9 2.2 2.4 2.4

Limits of classes < 0.17 [0.17-0.29[ [0.29-0.46[ [0.46-0.88[ [0.88-1.7[

"K-means" method

% of values 28.8 39.0 23.8 6.7 1.7

Limits of classes < 0.63 (0.63-1 .26[ [1.26-2.52[ [2.52-5.04[ ~ 5.04

"Mouvet's method"

% of values 96.6 3.0 0.40 0 0

POLLUTION ATMOS PHÉRIQUE N° 173 - JANVIER-MARS 2002 111

ARTICLES _

Table 3.Class ification of the arsenic data according to the three different methods:

European method , "K-means" method and "Mouvet's method".

Classif ication de l'arsenic selon les trois méthodes de classification :Méthode euro péenne , méthode des nuées dynamiques, méthode de Mouve!.

Clusters

1 2 3 4 5 6 7 8

Limits of classes < 0.2 [0.2-0A[ [OA-0.6[ [0.6-0.8[ [0.8-1.0[ [1.0-1.2[ [1.2-1A[ ~ l A

European classification

% of values 14.5 48.9 19.8 7.3 3.5 2.0 0.9 3.1

Limits of classes < 0.286 [0.286-0.580( [0.580-1.300( [1.300-3.500[ [3.500-7.500[

"K-means" method

% of values 41.6 41.3 13.8 204 0.9

Limits of classes < 0.9 [O.9-1.8[ [1.8-3.6[ [3.6-7.2[ ~ 7.2

"Mouvet's method"

% of values 93.9 4.1 1.1 0.7 0.2

that is damaging ecosystems . In contrast , the"Mouvet's classification" permits the assessment of apollution load in an ecosystem.

The classificatio n of Mou vet

Five classes have been determined for Cd and Asand their Iimits are shown respectively in Table 2,p. 111 and Table 3. For Cd, the 5 highest valueshave been eliminated to reach a standard deviationof 57.6% (Iower than 60%) and the reference valueobtained is 0.2 1 ~g/g . Standard deviat ion of thewhole data set is 64% (Table 1, p. 104). For As, the22 highest values have been eliminated to obtain astandard deviation of 59.7% and the reference valueis 0.30 ~g/g . Standard deviation of the whole data setis 130% (Table 1, p. 104).

Table 2, p . 111 .. sho ws that 96 .6% of Cdconcentration values is distributed in c1uster 1 (unpol­luted situation) and no values are included in c1usters4 and 5 (important or exceptional pollution). Table 3shows that 93.9% of As concentration values is distri­buted in c1uster 1, and 0.2% of the values (i.e., onevalue) is located in c1uster 5 (exceptional pollution).According to this classification method, Cd concen­tration distribution shows no important nor exceptionalpollution, and most of the values are considered asreflecting a backg round leve l. The As distribut ionshow s a few exceptional and important pollu tionlevels (clusters 4 and 5). The example of Cd pointsout a problem. The Cd reference value is calculatedon the hypothesis of an homogeneous set of data(showing a standard deviation close to 60%) reflecting

112

an unpolluted situation. For Cd, the standard deviationis 64% (Table 1, p. 104). In the case of a diffuse andhomogeneous level of pollution, the reference valuewou ld be overest imated , and the calculation ofclasses would be biased. The consequence would beto mask important or exceptional pollution levels. Tosolve this problem, it is necessary to calculate thereference value with a higher data set at a Europeanor intern ational level , including remote region s farfrom polluted areas. Another pro blem is that thec1ass limits calcul ation (using a twofold geometricprogression) should perhaps be adapted to terrestrialmosses after analysing the data set of whole metals,and after considering the variation of distribution ofvalues using different geometric progressions.

ln conclus ion, for each of the three methods ofclassification, a majority of data is locate d in thelower classes (Iow metal concentrations) whereason ly a few data in the upper one s (hig h met alconcentrations). Tab le 4 presents the advantagesand disadvantages of these methods. The choice ofthe method depends on the objectives: (1) to comparethe French data to the European ones, the "Europeanclass if ication" will be chosen ; (2) to present theFrench data in the best way in space and time, the"K-means" method will be preferred; (3) the "Mouvet'smethod" , which paramete rs might be adapted toterrestrial mosses will not be appropriated to showthe fine French spat ial differe nces between areas .The general idea of the "Mouvet's classification" is totransform quantitative data of concentrations inmosses to qualitative data associated to a level of

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Table 4.Advantages and disadvantages of the three ways of classification.

European method, "K-means" method and "Mouvet's" method.

Avantages et inconvénients des trois méthodes de classification.Méthode européenne, méthode des "nuées dynamiques", méthode de "Mouvet".

Classification meth ods Advantages Disadvantages

"European" • Homogenous cartographie representation • Non statistical method.throughout Europe.

• Can vary according to new surveys and with• Comparisons between European countries some additional countries.possible.

• As it is elaborated with a large dataset, theclustering may be inappropriate for one singlecountry.

"K-means" • Statistical method grouping data into clusters • Changes of the clustering according to newas different as possible. data (comparisons in space and time) are difficult.

• Can vary according to new surveys and withsome additional countries.

"M ouvet' • Clustering based on reference value (unpol- • Non statistical method.luted situation). • Appl ied from a class ification for aquatic• Mathematical method. mosses : the coefficients may be inappropriate

• Standardized method. and should probably be adapted to terrestrial

• Quality notion assoc iated to each clustermosses.

(from not to exceptionally polluted). • Does not show small differences between

• Lower and upper bounds not similar to lowerareas.

and upper values of the considered dataset.

• Comparisons between European countriespossible.

contamination. This method of classification will thus befavoured to give a qualitative contamination notion tothe sites and to point out important or exceptionalpollution.

Mapping

The second step to present data after the clusteringis the mapping of the different classes. The aim is tovisualize the distribution of data within a large area,making possible comparisons in space and time. Thechoice of number of c1usters mapped, colours, symbolsor interpo lat ion algorithms used can provide suchdifferent maps which can lead to strongly differentInterpretations of results.

Two Cd maps using diffe rent methods (spot byspo t, in terpolation) are presented and discussedbelow. Then, the three classification methods describedabove are compared using the interpolation map forCd and As.

Spot by sp ot map

The French spot by spot map for Cd that is realisedwith the "European classifi cation" , is presented inFigure 4, p. 114. In the provinces where density ofsites is not too high, like in the Rhône-Alpes and theMidi-Pyrénées provinces, several "hot spots" arec1early visualized. Indeed, sorne sites present a highlocal concentration in mosses whereas the next closest

POLLUTION ATMOSPHÉRIQUE N° 173 - JANVIER-MARS 2002

sites to these indicate much lower metal levels. lt isnot the case in provinces where a higher density ofsites (example of the Ile-de-France province) maymask information at a national scale. So, the maindisadvantage of this method is that results are consi­dered as scattered points, where an information maybe masked in the case of high density of sites or, atthe opposite, where the general information cannotbe seen obviously in the case of a low densi ty ofsites (with an higher visual impact of hot spots thanothe r sites). However, the great advan tage of thispresentation is that it gives compl ete informations(site location, moss species) and contributes to preciseinterpretation .

Interpolation maps

The Cd interpolated European map realised withthe "European cla ssi f icat ion" (Figu re 5, p. 115)allows to see a general tendency throughout Europe.We can see for examp le, higher levels in EasternEurope (Romania, Ukraine, Siovakia, and Poland),and also in other co untr ies (Por tuga l and Th eNetherlands), lower levels in Northern Europe, or sornelarge areas showing intermediary levels of Cd in mosses(throughout south-western Germany, Luxembourg,Belgium and north-eastern France). So, this kind ofprese ntation allows a global inte rpre tation of thedistribution of data with large scale variations in the

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.... · 3' ·2' · 1' l' 2' 3' 4' 5 ' 6' 7' 8 '

45'

47 '

46 '

48 '

50'

51'CADMIUM1996 survey

Kilanè tns

a 50 100

,..-.;;;;

Mosses

oHypnum cupressiforme (198)»Scleropodiumpurum (158)oPleuronu m schreberi (111)"Hylocomium splendens (52). Thuidium tamariscinum (39)

Over 0.8 (13)

Minirra 0.0Maxirra 1,7e

~ean 0,2Median 0,23

ISD o.u42'

I nncertaiDty 13... ' ·3 ' ·2' ·1' O' l ' 2 ' 3' 4' 5' 6' 9'

45'

43 '

44 '

S la tistic538 values

(l' glg Moss DW)

46 '

47 '

0.6 ra 0.8 (15)

50'0.5 ra 0.6 (12)

0.4 ra 0.5 (40)

0.3 ra 0.4 (9 1)0.2 ra 0.3 (167)

o 0.1 ra 0.2 (169)• BeiowO .1 (50)

49 '

Cadmium concentration51' (l'glg dr)"moss we lg bt )

Figure 4.French spot map of the cadmium concentrations into mosses according to the clustering of the "European " method.

Map realised by the ADEME (B. Charré, 2000) from the 1996 French data survey [16].

Carte française de la distribution des concentrations en cadmium dans les mousses représentée point par pointen fonction des classes établies d'après la méthode "européenne". Carte réalisée par l'ADEME (B. Charré, 2000)

d'après les données de la campagne française 1996 [16].

114 POLLUTION ATMOSPHÉRIQUE W 173 - JANVIER-MARS 2002

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Figure 5.European map of the cadm ium concentrations into mosses. Map realized by L. Olsson , Lund University, Sweden (2001 ),

from the 1995/1996 European survey data [25], with add itional complementary French data(Adapted with permi ssion from Rühling A, Steinnes E. Atmospheric heavy metal deposition in Europe 1995-1996.

Nordic Council of Ministers (ed) vol 15, Nord, 1998, 1998 : 66 p.Carte europ éenne de la distr ibution des conce ntrations en cadmi um dans les mousses. Carte réalisée par L. Olsson,

Unive rsité de Lund, Suède (2001) d'après les donn ées de la campagne européenne 1995/ 1996 [25]avec des données françaises complémentaires

(Adapté, avec autorisation, de Rühling A, Steinnes E. Atmospheric heavy metal deposition in Europe 1995-1996.Nord ic Council of Ministers (ed) vol 15, Nord , 1998, 1998 : 66 p.

"('\

regional distribution of pollutant deposition shownc1early. However, as the location of sites is not plottedat European scale, in the case of an area using lowdensity of weak ly contaminated sites this method ofmapping may give higher relative importance for a"hot spot" than it is in real ity , certa inly leading tomisinterpretations. This is the main disadvantage ofthis method.

Comparison of the three mapsfor cadmium and arsenic accordingto the three classification methods(Europea n, "K-means " and Mouvet methods)

Considering the advantages and disadvantagesof each mapping method, we have chosen to show

POL LUTION ATMOSPHÉRIQUE N° 173 - JANVIER-MARS 2002

Cadmium

Over 0.8

0.6 - 0.8

0,5 - 0.6

0.4 - 0.5

0.3 - 0.4

the Cd and As maps for France by the interpolationmethod, after removing border sites. The Cd and Asmaps are presented according to the three methodsof classification (see Figures 6a, 6b and 6c, p. 116and Figures 7a, 7b and 7c, p. 117).

For Cd and As, maps of the "European classifi­cation" and of the "K-means " methods (Figures 6aand 6b , p. 116 for Cd and Figures 7a an d 7b ,p. 117 for As) show th e same gene ral tendencythroughout France even if the calculation of classesis differ ent. Th e first maps prese nts 8 classes(Figures 6a and 7a) whe rea s th e seco nd maps5 classes (Figures 6b and 7b) for each of the twometals. For Cd, the lIe-de-France and Champagne­Ardenne provinces show the highest levels of metal

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ARTICLES · _

o 100 km....-

Figure 6a

I-Ig/g dry moss weighl• <0.1

0.1 - 0.20 0.2 - 0.30 0.3 - 0.40 0.4 - 0.50 0.5 - 0.60 0.6 - 0.8_ :>0.8

Out the external outl ine

Figure 6b

Figure 6e

119/g dry moss weighl-o.i z

bJI 0.17 - 0.290 0.29 - 0.460 0.46 - 0.88

0.88 - 1 .70Oul lhe exlernal oulline

119/9 dry moss weight<0.63

00.63 - 1.2601.26 - 2.520 2.52 - 5.04

>5.04Oul lhe exlernal outline

Figures 6.Interpolat ion rnaps of the cadmium concentrations into mosses according to the three methods of classificat ion:

"European classification", "K-means" method and "Mouvet's method". Map realised by J. Signoret (ESSE).6a: Map realised according to the c1ustering of the "European" method .6b: Map realised according to the clustering of the "K-means" method .6c: Map realised according to the clustering of the "Mouvet method" .

Cartes d'interpolation de la distribution des concentrations en cadmium dans les mousses en fonction des classes établiesd'après les trois méthodes de classification: méthode "européenne", "méthode des nuées dynamiques"

et "méthode de Mouvet". Données de la campagne française 1996. Carte réalisée par J. Signoret (ESSE).6a: Carte établie d'après la classification " européenne » ,

6b: Carte établie d'après la classification " nuées dynamiques » .

6c : Carte établie d'après la " classification de Mouvet » .

116 POLLUTION ATMOSPHÉRIQUE W 173 - JANVIER -MARS 2002

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o 100 km~

Figu re la

1-19/g dry mess weight

. <0.20.2 - DAo OA-0.6

0 0.6 - 0.80 0.8 - 1.00 1.0 - 1.2

1.2- 1A>lA

_ OUt the external eutline

Figure l b

Figure le

1-19/g dry mess weight<0.286

D 0.286 - 0.580o 0.580 - 1.300o 1.300 - 3.500

3.500 - 7.500Out the extemal eulline

1-19/g dry mess weighl<0.9

0 0.9 - 1.80 1.8 - 3.60 3.6 - 1 .2

>1.2Out the extemal eulline

Figure 1 .Interpolation maps of the arsenic concentrations into mosses according to the three methods of classifica tion:

"Europea n class ification" , "K-means" method and "Mouvet's method". Map realised by J. Signoret (ESSE).la : Map realised according to the clustering of the "European" method .lb: Map realised according to the clustering of the "K-means " method .lc : Map realised accord ing to the cluster ing of the "Mouvet method".

Cartes d'interpolation de la distribution des concentrations en arsenic dans les mousses en fonction des classes établiesd'ap rès les trois méthodes de classification : méthode "européenne", "méthode des nuées dynamiques"

et "méthode de Mouvet". Données de la campagne française 1996. Carte réalisée par J. Signoret (ESSE).la : Carte établie d'après la classif ication " européen ne ".

l b : Carte établie d'après la classification " nuées dynamiques » .

Zc : Carte établie d'après la « classification de Mouvet » .

POLLUTION ATMOS PHÉRIQUE W 113 - JANVI ER-MARS 2002 111

ARTICLES _

analysed in mosses. The provinces located from thenorth-east and east of France to the south and south­east of France also show high levels of metal conta­mi nat ion . Central parts of Fran ce show lowe rconcentrat ions than previous provinces and westernFrance (and furthes t south -east) show the lowe stconcen trations of meta ls in mosses. For As, thehighest va lues are found in the south of Franc e(south of Auvergne, Languedoc-Roussillon and Midi­Pyrénées provinces) and scattered hot spots are alsofound in Rhône -Alpes , Poitou-Charentes, Franche­Comté, Champagne-Ardenne and in the south-west ofthe lIe-de-France area. The north-west, south-westand center of France show lower concentrationsof metal in mosses than those of the previous provinces.

The results for Cd and As with the "Mouvet' sclassification" (Figures 6c, p. 116 and 7c , p. 117)are very different from the other maps using the differentclassification methods. For both Cd and As , thedominant colour is green, indicating that the majorityof sites shows concentrations in mosses simila r tothe calculated reference value. But, although the Cdmap (Figure 6c) shows nearly no difference betweenFrench areas (the whole France is represented bythe first c1ass below 0.63 IJg/g of DW, except just onesite located in the Champagne-Ardenne provi nce,that is in the th ird class), the As map (Figure 7c ,p. 117) indicates prablematic hot spots belonging tothe fourth and fifth classes (showing respectively animportant and a exceptional pollution according to theMouvet's definitions adapted by Mersch and Claveri[26,27]).

ln conclusion, the interpolation method of mappingimproved by removing border sites provides the bestspatial representation of the values. The maps using"European" and "K-means" classifications are similarand sho w weil the variations of concentration ofmetal in mosses in France . However, they do notshow any level of pollution. Maps made fram valuesc1assified according to Mouvet's method point out thehighly contaminated sites, but do not show variationsof metal concentrations in mosses very we il . It isimportant to point out that Mouvet's classi ficationshould be tested with other metals, with a larger dataset , and should probab ly be specifica lly ada ptedto ter restr ial mosses .to ensure that it is the mostsuitable method.

Exploitation and optimisation of results

Is the metal concentration in mosses able to giveinformation on cr itical limits and cr itical loads?

The concentration of metal in mosses is an inte­gration of metal in mosses over a period of severalyears. This information on the metal concentrationsdoes not reflect only the load of metal deposition .This acc umula tion ref lects comp lex interactionsbetween mosses and their enviranment , including thebiodisponibility of metals.

Critical limits, the maximal concentration of pollutantssupported by the whole ecosystem, can be estimated

118

from metal conce ntra t ion in unpoll uted soi ls andgroundwaters [6]. We suggest that metal accumulationin mosses reflects the load of metal deposition onsoi ls, as weil as interactions between an organismand the enviranment, and could pravide supplemen­tary data fo r the estimation of such critical loads.Mouvet's method of classification of metal concen­tr ati on s in mosses assoc ia ted to in te rpo la t io nmapping visualizes different levels of pollution, andis based on a refere nce value concentration (seeMethodo logical appraach) . This method can thus beadapted to metal accumulation values in terrestrialmosses, with later comparison to measurements andaddition of data for metals in soils, soil solution andgroundwater, providing a useful method for critica lload calculation .

Another way to estimate critical limits is toxico­logical tests [3]. Moss species used for the survey"Atmospheric metal deposition based on moss analysis"are supposed to be metal tolerant and are not supposedto be sensitive enough to metal pollutants to be usedfor toxicolog ical stud ies . However, for aquaticmosses , a sensitive method for estimatin g the stressin mosses based on pigment ana lys is has givenreliable information on the toxicity level of the wholeecosystem [29-35] . Since research is going on theadaptation of th is method for terrestrial mosses, itcould provide useful data for crit ical load calculationin the future on national and regional scales.

The European metal survey in mosses was initiatedin order to obtain an estimation of metal depositionon a large scale. The second question arising framthis aim is:

Is the metal concentration in massesequal ta the metal deposition?

Is it possible to convert from the concentration ofmetal in moss (expre ssed in IJg of metal per 9 ofmoss) to a deposition expressed in IJg of metal persurface area unit? Metals in mosses depend directlyon metal deposition over time, but other parametersmay influence this accumulation level.

Firstly , metal accumulation leve ls can vary framone moss species to an other. Interspecies calibrationof metal concentrations in mosses has been performedfor two decades and some works have given contra­dictory results that are difficult to interpret [18, 36-40].

Secondly, for one moss species , metal uptakeefficiencies can vary from one element to an otherone (Pb for example is the element which is integratedmore (100%) by mosses in laboratory conditions [14]).The saturation effects for one element can also leadto changes in the accumulation of other elements .

T hi rdl y , env ironmental factors (cl ima te , soi lchemistry , leakage) have an impact on the physio­logy and morphology of mosses, and may inducechanges in the final concentrations of metals accu­mulated in mosses .

Is it now conceivab le to think of a conversion frama concentration of metals to a flux of metals expressed

POLLUTION ATMOS PHÉRIQUE N" 173 - JANV IER-MARS 2002

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in I-'g of metal per surfa ce area unit, and per timeunit? An equation has been suggested [17, 41] tocalcu late the deposition fram the metal concentrationin mosses . This calculation is based on a theoreticalcalculation of the deposition flux (F = C x GlU, withF = flux, C = metal concentration in mosses , G =growth and U = uptake efficiency). Other parametersIike uptake eff iciencies and moss growth rate areintegrated in th is formu la. These parameters arequite difficult to extrapo late fram literature based dataand will require more investigations at each site andon eac h sp ecies. Even if some cor re lat io ns arepossib le between meta l conce ntrations in mossesand metal flux (obtained with chemical collectors) ina few sites , dete rmination of flux deposition frommetal concentration in mosses (resulting of an accu­mulat ion ove r time) on a large scale seems to bepremature based on the actual state of knowledgetoday.

Although the "moss-metal method" does not yetpermit the calculation of absolute meta l depos itionfrom metal's concentration in mosses , it does allowus to compare data spatially (locations that are moreor less contaminated than each other at a particularmoment) or temporally (increase or decrease conta­mination in one location from one survey to another).We can thus conclude that the "moss-metal method"gives the relative metal deposition for a large scale,and can be weil visualized by the "European" or"K-means" classifications associated with interpolationmapping.

However, because the investigations on the para­meters influencing metal accumulation in mosses, asweil as comparisons between actual depositions andmetal concen trations in mosses, are going on inFrance, it is conceivable that the estimation of actualmetal deposition and exceedances fram accumulatedmetal in mosses in France will be possib le in thefuture.

Conclusion

The "European" and "K-mea ns" class ificationsassociated with the interpolation mapping pravide agood method of visualisation for relative metal depo­sition , but not of actual absolute metal deposition onecosystems in present state of knowledge.Interpolat ion maps made from values classif iedaccording to "Mouvet's method " could also pravideuseful data for critica l loads estimation . Furthermore,a method esti mating the stress in moss (pigment

POLLUTION ATMOSPHÉRIQUE W 173 - JANVIER-MARS 2002

measurements ) extrapo lated to give an idea of theecosystem stress could pravide additional data forcritical limi ts calculations . Consi dering the uniquenature and large scale of the set of data pravided bythe "moss-metal" method, and information given oninteractions between mosses and their environment ,it is thus suggested that the unit J.lg of metal accumu­lated per 9 of moss could provide reliab le data forcrit ica l loads: critical limits estimation and visual i­sation of relative atmospheric metal deposition(exceedances estimation) . This is subject to furtherinvestigations to confirm the adequacy of the methodand of the inclusion of additional information usingfield trials.

Acknowledgments

The 1996 French moss survey was coordinate dby the Laboratory of Ecology (University of Paris 6).The 2000 French moss survey is coord inated, aipresent, by the Laboratory of Cryptogamy (NationalMuseum of Natura l History , Paris). The 1996 ana­lyses were performed by the Pierre Süe Laboratory(CEA-CNRS). The moss network is now fully supportedby the ADEME (French Agency for the Enviranmentand Energy Management), and the 1996 campaignwas partly funded by the French Min istry ofEnvi ronment. We are grateful to Michelle Priee(Conservatory and Botanical Garden of the city ofGeneva) for English correction and to Jean-FrançoisMony and Serge Muller (University of Metz, EBSE)and Bruno Charré (ADEME) for assistance with themapping.

KeywordsMetal depos ition . Mapp ing. Air pollution .

Mosses . Critica l loads and criti ca l l im its .Classi fication methods. Data interpr etati on .French data.

Mots clésDépôts métall iques. Cartographie. Poll ution

atmosphérique . Mousses. Charges critiques etseu ils cr itiques. Méthodes de classifi cation.Interprétation des données. Données françaises.

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