deposition and disease: a moss monitoring project as an

Ž .The Science of the Total Environment 249 2000 243]256

Deposition and disease: a moss monitoring project asan approach to ascertaining potential connections

O. WappelhorstU, I. Kuhn, J. Oehlmann, B. Markert¨International Graduate School Zittau, Markt 23, 02763 Zittau, Germany

Abstract

Ž .In the years 1995 and 1996 the atmospheric deposition of elements in the EuroRegion Neisse ERN wasdetermined in a biomonitoring project using mosses. The mosses Pleurozium schreberi and Polytrichum formosumwere chosen as biomonitors because of their wide distribution in the area studied. The moss samples were analysedby ICP-MS and ICP-OES for their concentrations of 37 chemical elements. The results were shown in the form ofmaps. The data from the moss monitoring project served as a basis for determining those elements in the depositedmaterial that promote the occurrence of disease. This was done by correlating the figures for the various diseaseswith the appropriate element concentrations in the mosses. Indications were found that a connection exists betweenthe thallium content of mosses and the occurrence of cardiovascular disease and between Ce, Fe, Ga and Ge levelsin the mosses and the incidence of diseases of the respiratory system. Q 2000 Elsevier Science B.V. All rightsreserved.

Keywords: Atmospheric pollution; Biomonitoring; Deposition; Disease; Human health; Moss; Pleurozium schreberi; Polytrichumformosum

1. Introduction

Air pollution is known to be detrimental tohuman health, but it is very difficult to prove aconnection between a specific pollutant and adisease. Such proof can only be furnished on the

U Corresponding author.Ž .E-mail address: [email protected] O. Wappelhorst

basis of data on atmospheric pollution and theincidence of disease in a particular region.

In general, expensive and delicate measuringinstruments are used to ascertain the pollutantinput in a region. Such instruments have to beinstalled in a measuring network, which ties uppersonnel and is costly to use and maintain. As aresult, quantitative and qualitative monitoring ofthe pollutants can only be carried out at a fewselected locations by this method. Biomonitoring

0048-9697r00r$ - see front matter Q 2000 Elsevier Science B.V. All rights reserved.Ž .PII: S 0 0 4 8 - 9 6 9 7 9 9 0 0 5 2 1 - 5

( )O. Wappelhorst et al. r The Science of the Total En¨ironment 249 2000 243]256244

Fig. 1. Paths by which pollutants are taken in by human beings and moss. Unlike mosses, human beings are exposed to pollutants innumerous places and take substances in by several routes.

is a more elegant, indirect method of determiningthe pollutants and their distribution. It makes useof the plants’ ability to accumulate pollutant subs-

tances over a considerable period of time. Thismakes it possible to determine pollution of theenvironment at the site of a plant, e.g. a moss, as

Fig. 2. EuroRegion Neisse, where the three states Germany, Poland and the Czech Republic meet.

( )O. Wappelhorst et al. r The Science of the Total En¨ironment 249 2000 243]256 245

a function of time without the risk that the resultswill be distorted by short-term fluctuationsŽ .Markert et al., 1999 .

Besides damaging the environment, the deposi-tion of chemical elements in all manner of formsand compounds may also impair human health.To determine which elements promote the occur-rence of specific diseases it is necessary to com-pare the incidence of the disease with the ele-ment concentrations in atmospheric deposits. Abiomonitoring project using mosses was carriedout to measure this input of elements. To make iteasier to detect the differences in pollution withinthe study area, the data were presented in theform of contour maps.

Whereas atmospheric deposition is the mainsource of the substances taken up by the mossesused as biomonitors in this survey it is only one ofseveral paths by which such substances can betaken in by human beings; its share of the overallintake depends on the individual’s personal cir-

Ž .cumstances and way of life Fig. 1 .Ž .The EuroRegion Neisse Fig. 2 was chosen as

a model for the survey. It is situated at the pointwhere three countries } Germany, the CzechRepublic and Poland } meet. Until the early1990s this area was infamous for its high pollutionlevel. The burden on the environment has sincebeen reduced considerably through the closing ofnumerous industrial facilities and HagenwerderPower Station, the fitting of modern filters at the

Ž .brown coal power stations Boxberg, Turow and´the replacement of old power station units by new

Ž . Žones Boxberg, Schwarze Pumpe Wappelhorst et.al., 1999 . A further source of emissions is the

metal-working and glass industry in the Liberecˇand Ceska Lıpa area.´ ´

The plants chosen as biomonitors were themosses Pleurozium schreberi and Polytrichum for-mosum. The use of epiphytic plants as passivebiomonitors is an established method of de-termining atmospheric deposition. In Scandi-navia, mosses have been used as biomonitors fordetermining pollution with heavy metals since the

Ž .late 1960s Ruhling and Tyler, 1968 . Numerous¨projects have since been carried out with mosses;the method has been developed systematicallyŽEllison et al., 1976; Maschke, 1981; Engelke,

.1984; Steinnes, 1984; Ross, 1990 and also used inŽlarge-scale European studies Ruhling, 1994; Her-¨

.pin, 1997 . It is mainly the endohydric mossesPleurozium schreberi and Hylocomium splendensthat have been used in these studies.

The bryophytes can be divided into two groups} the ectohydric and endohydric types } ac-cording to the manner in which they take up and

Ž .transport water Buch, 1947a,b . Ectohydricmosses do not have differentiated internal con-ductive tissue. Water is transported by capillaryforces between the stem and the leaves closelyadhering to it. Such mosses have no cuticle. En-dohydric mosses such as Polytrichum formosumhave an efficient internal water transportationsystem and a thin cuticle. In both types, nutrients

Žare taken up in the same way as water Buch,.1947a; Proctor, 1984 . Both ectohydric and en-

dohydric mosses draw their nutrients almost solelyfrom precipitation and not from the soil throughtheir root-like rhizoids, which only serve to an-chor the plants to the substrate.

Moss analyses were used to determine the oc-currence and distribution of the elements Ag, Al,Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga,Ge, K, La, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pr, Rb,Sn, Sr, Ti, Tl, Th, U, V, Y, Zn and Zr in the

Ž . Ž .EuroRegion Neisse ERN Wappelhorst, 1999 .Little is known about the environmental concen-trations and distribution of some of these ele-ments, such as Ce, La, Nd, Y and Zr, but theirindustrial significance as constituents of alloys,semiconductors and catalysts has increased in re-cent years.

2. Materials and methods

2.1. Sampling

The samples were collected over a period of 4weeks from mid August to mid September in theyears 1995 and 1996. The sampling procedurefollowed that of the pan-European moss moni-

Ž .toring project Ruhling, 1994 , which requires that¨moss samples be taken from the soil in openareas, preferably forest clearings. The samplingsites should be at least 300 m from the nearest


major road, townrvillage or industrial facility andat least 100 m from smaller country roads andindividual houses. To avoid the direct influence ofwater dripping from the leaf canopy, the mini-mum distance from the nearest tree must be 5 m.Several sub-samples were taken in an area of50=50 m at each sampling site. PE gloves wereworn to prevent contamination during samplingand further work on the plants.

In the laboratory the dirt adhering to the sam-ples was removed and the green and brown partsof the plants were separated. In the case of Pleu-rozium schreberi the green parts are the growth ofthe last 2]3 years. The age of Polytrichum formo-sum can be determined by the antheridium cupsthrough which the plant has grown; the growth ofthe last 2 years was used for analysis. The un-washed samples were dried at 458C and thenhomogenised in a disk vibration mill with a wol-fram carbide container.

2.2. Instrumental analysis

Ž .The samples 300 mg were subjected to mi-crowave-assisted pressure digestion with 4.0 ml of

Ž .concentrated nitric acid suprapur and 2.0 ml ofŽ .hydrogen peroxide suprapur in closed PTFE

vessels. After cooling, the samples were made upto 50.0 ml. A digestion with certified reference

Žmaterial Peach Leaves SRM 1547 or Cabbage.GBW 08504 was carried out in each series as a

Ž .control for the analytical results Markert, 1996 .The equipment used for analysis was ICP-MS

Ž . ŽPerkin Elmer, ELAN 5000 and ICP-OES Per-.kin Elmer, Optima 3000 . All standards, blanks

and samples contained 3% HNO and also 503ngrml Sc, Rh and Ir as an internal standard.

2.3. Mapping

Ž .Inverse Distance Weighting IDW was used asa method for interpolating the element concen-trations in terms of space. The interpolation wascarried out with the aid of the Geographic Infor-mation System ARCrINFOW using the IDW in-terpolation function in the grid module accordingto the following formula:

Formula 1:

Y svalue of the grid celljd sDistance between the measuringi j

Y s f point and the relevant grid cellj

1xÝ z iž /di ji

Žz s1.8 The exponent may take the ¨alues0.5]3. A higher ¨alue means less

.influence of distant measurements.

A grid with cells 350=350 m in size is defined

Table 1Classification of the diseases studied according to ICD 9

ICD 9 Description of the disease

140]208 Neoplasms162 Malignant neoplasms of the trachea, bronchus and lung172, 173 Malignant melanomas of the skin and other malignant neoplasms of the skin204]208 Leukaemia390]459 Diseases of the circulatory system393]398, 410]429 Heart diseases410 Acute myocardial infarction411]414 Other forms of ischaemic heart disease401]405 Essential and secondary hypertension430]438 Diseases of the cerebrovascular system460]519 Diseases of the respiratory system480]486 Pneumonia466, 490, 491 Bronchitis490]496 Chronic obstructive lung disease493 Asthma680]709 Diseases of the skin and subcutaneous tissue710]739 Diseases of the musculoskeletal system and connecti e tissue


Table 2The European Standard Population according to WaterhouseŽ .1976

Age group Europe

0 16001 to -5 64005 to -10 7000

10 to -15 700015 to -20 700020 to -25 700025 to -30 700030 to -35 700035 to -40 700040 to -45 700045 to -50 700050 to -55 700055 to -60 600060 to -65 500065 to -70 400070 to -75 300075 to -80 200080 to -85 100085q 1000

Total 100 000

to cover the area between the points, and theseven closest measuring points are used for calcu-lating the value of each cell. The value calculatedfor a grid cell depends on the element concentra-tions at the surrounding measuring points andalso on the distance between these values and thegrid cell concerned. A detailed description of theinterpolation technique is to be found in Watson

Ž .and Philip 1985 . For mapping, the element con-centrations were divided into five classes using

Ž .the method of Erhardt et al. 1996 . Initially, astandard value is formed that incorporates a ho-mogeneous group of low values. These values }and the standard value thus formed } reflect thebackground concentration. Three further classeslie above these standard values, and a furtherclass comprises lower concentrations.

2.4. Impact on human health

Air-borne pollutants are inhaled by man andchiefly cause chronic diseases of the upper andlower respiratory tract. At high concentrations

they may also cause acute disorders. After inhala-tion the various components may have synergisticeffects, with the result that their overall impact isgreater than the sum of the effects of the individ-

Ž .ual substances Berenbaum, 1985; Spurny, 1993 .The substances entering the lungs may be takenup by the blood and metabolised. Air pollutantsare said to be responsible for 1]5% of all additio-

Ž .nal cases of cancer Doll and Peto, 1981 and,according to some estimates, 11]21% of all cases

Žof lung cancer are caused by air pollution Karch. Ž .and Schneiderman, 1981 . Doll and Peto 1981

.and Karch and Schneiderman, 1981 investigatedthe health hazard resulting from the total aerosolof the atmosphere. They did not consider itscomposition and the proportions of the individualsubstances. These were first investigated by

Ž .Spurny 1993 , who compared industrial, residen-tial and clean-air regions in his study.

An investigation into the effects of atmosphericpollution on individuals over large areas involvesthe collection of data throughout the region. Bio-monitoring is an excellent means of doing this.Such an approach was used by Cislaghi and NimisŽ .1997 in a study in which they compared mortal-ity from various pulmonary diseases with a biodi-versity index for lichens. High correlation coeffi-cients were found between the index and thenumber of deaths from lung cancer. However, nodirect conclusions were drawn in respect of levelsof individual elements.

The present study was to be the first attempt tocompare pollution with numerous elements }detected by using mosses as biomonitors } withthe incidence of disease.

The numbers of patients discharged from hos-pitals, including deaths, in the years 1993]1997were taken as the basic data for the frequencywith which a disease occurs. In Germany thisdata, which includes the diagnosis and thepatient’s sex, age and place of residence, is passedon from the hospitals to the Statistical Offices ofthe individual states every year. The diseases are

Žclassified according to ICD 9 International Sta-tistical Classification of Diseases and Related

. ŽHealth Problems, ninth revision, WHO Table.1 .


We were able to evaluate the data from thedistricts of Bautzen, Kamenz, Lobau]Zittau, the¨

Ž .Upper Lusatia district of Lower Silesia NOLand the county boroughs of Gorlitz and Hoyer-¨swerda. The data are classified according to sex

Žand age 0 to -5, 5 to -10, . . . , 80 to -85, 85.and over . The age structure, which has a bearing

on the incidence of disease, differs from one areato another. In order to compare the disease fig-ures for the individual areas nevertheless, it wasnecessary to standardise them. Two possiblemethods of standardisation were available.

In the first method the data were converted inaccordance with the European Standard Popula-

Ž . Ž .tion see Tables 1 and 2 Waterhouse, 1976 . Theage groups are taken into account in the stan-dardised overall incidence of a disease with aweighting that corresponds to their share of thestandard population. In the second method thenumbers of cases of the disease are converted toa figure per 100 000 inhabitants divided up ac-cording to sex and age; the overall incidence ofthe disease per 100 000 inhabitants is then calcu-lated. This method has the advantage that thedata can be evaluated separately according to age

and sex. But such a breakdown can lead to verysmall case numbers per group and thus, causemajor errors in the results. This does not happenwith the first method. A further advantage is agreat reduction of the individual data to beprocessed.

2.5. Distribution of elements in the EuroRegionNeisse

Figs. 3]8 are examples showing the inter-polated concentrations of the elements Ce, Cr,Nd, Sn and Tl in Polytrichum formosum and theinterpolated thallium concentrations in Pleuroz-ium schreberi. Concentrations exceeding the stan-dard value were found for the elements Ce, Crand Nd shown in the figures and also for Fe, Ge,La, Li, Nb, Ni, Pr, Th, Ti, U, V and Zr north ofTurow Power Station, along the valley of the´Neisse, across the Liberec region and as far asthe south of the ERN. The other elements inves-tigated have different distribution patterns. Thedifferences in distribution patterns found betweenPolytrichum formosum and Pleurozium schreberican be explained by the morphological and physi-

Fig. 3. Cerium concentration in mgrg dry matter in Polytrichum formosum.


Fig. 4. Chromium concentration in mgrg dry matter in Polytrichum formosum.

Fig. 5. Neodymium concentration in mgrg dry matter in Polytrichum formosum.


Fig. 6. Thallium concentration in mgrg dry matter in Polytrichum formosum.

Fig. 7. Thallium concentration in mgrg dry matter in Pleurozium schreberi.


Fig. 8. Tin concentration in mgrg dry matter in Polytrichum formosum.

Žological differences between the plants Wap-.pelhorst, 1999 . An influence of the soil on the

element concentrations was not detected for ei-ther of the mosses.

In the ERN, the atmospheric deposition re-flected in the element concentrations in the twomoss species is similar to that found in moder-ately to slightly polluted regions of Europe as a

Ž .whole Wappelhorst et al., 1999 . Exceptions tothis are those elements whose main source ofemission is the burning of brown coal. Since browncoal deposits exist in the ERN, this fuel is one ofthe major sources of energy in the region.

2.6. Disease and atmospheric deposition

To determine possible connections between de-position and disease, the incidence of the diseaseswas correlated with the element concentrations inthe mosses. The concentrations of a number ofelements in the mosses Pleurozium schreberi and,to a lesser extent, in Polytrichum formosum differonly slightly from one sampling site to another inthe ERN. A priori, high correlation coefficients

would result between these elements and all dis-eases that also show little geographic differencein incidence. But such correlations yield ex-tremely little information; for this reason, onlyelements with a mean relative deviation of atleast 35% from the median were considered. Inthe ERN, differences of this magnitude werefound for Ag, Al, Be, Bi, Ce, Cr, Cs, Fe, Ga, Ge,La, Li, Mn, Mo, Na, Nb, Nd, Pb, Pr, Rb, Sn, Th,Ti, Tl, U, V, Y and Zr in Polytrichum formosumand for Be, Bi, Cs, Mn, Na and Tl in Pleuroziumschreberi.

The incidences of disease converted in accor-Ž .dance with the standard population Method 1

and the numbers of cases broken down accordingŽ .to age and sex Method 2 were correlated with

the element concentrations in the moss samplesfrom the various districts. The results are similar;for this reason, the results of Method 2 are onlygiven in part.

Ž .Significant correlations PF0.1 between theelement concentrations in the mosses and theincidence of the diseases covered by the surveyare shown in Table 3. For the sake of simplicity

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Table 4Ž .Coefficients of correlation between the incidence of essential and secondary hypertension ICD 401]405 and element concentrations in Pleurozium schreberi in the

ayears 1993]1997, broken down according to age groups

20]25 25]30 30]35 35]40 40]45 45]50 50]55 55]60 60]65 65]70 70]75 75]80 80]85 )85

Be y0.10 y0.22 y0.02 y0.18 y0.14 y0.08 y0.01 y0.18 y0.12 y0.03 y0.12 y0.05 y0.10 y0.05 BeBi 0.33 0.38 y0.13 y0.08 0.44 0.45 0.01 0.20 0.59 0.76 0.69 0.81 0.82 0.86 BiCs 0.46 0.54 y0.10 0.13 0.61 0.60 0.11 0.42 0.76 0.85 0.84 0.90 0.94 0.92 CsMn 0.14 y0.23 0.63 0.44 y0.07 y0.33 0.29 y0.09 y0.36 y0.57 y0.48 y0.60 y0.63 y0.71 MnNa 0.46 0.07 0.75 0.78 0.31 0.10 0.66 0.36 0.05 y0.25 y0.12 y0.31 y0.36 y0.49 NaTl 0.49 0.39 y0.08 0.20 0.63 0.71 0.19 0.44 0.81 0.96 0.92 0.98 0.99 0.99 Tl

a Significant correlation coefficients are printed in bold type.

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Table 5aCoefficients of correlation between thallium concentrations in Polytrichum formosum and the incidence of diseases in ERN

ICD 9 -1]5 5]10 10]15 15]20 20]25 25]30 30]35 35]40 40]45 45]50 50]55 55]60 60]65 65]70 70]75 75]80 80]85 )85

140]208 y0.50 0.30 y0.29 y0.14 I0.76 0.20 0.42 y0.15 y0.20 y0.30 y0.07 0.32 0.05 y0.41 y0.36 0.73 0.71162 y0.68 y0.20 y0.14 y0.68 0.84 y0.13 y0.36 0.56 0.77 0.12 0.59 0.09 0.22 0.58 y0.19 0.36172, 173 0.37 0.26 0.67 0.10 0.25 0.81 0.30 y0.40 y0.11 y0.51 0.30 0.07 0.83 y0.01 0.46 0.79204]208 y0.30 I0.80 y0.53 0.75 y0.26 y0.65 0.04 0.02 0.36 y0.13 y0.65 y0.33 y0.68 y0.65 y0.67 y0.34 0.52390]459 y0.27 y0.24 0.22 0.67 0.64 0.82 0.42 0.46 0.71 0.72 0.14 0.65 0.92 0.66 0.82 0.78 0.86 0.72393]398, y0.37 I0.73 I0.75 y0.52 y0.45 0.59 0.48 0.55 0.66 0.57 y0.06 0.45 0.89 0.48 0.81 0.64 0.74 0.49410]429401]405 0.90 0.79 0.56 0.60 0.90 0.65 0.64 0.66 0.83 0.69 0.75 0.70 0.70 0.61410 0.31 0.85 0.70 0.91 0.77 0.88 0.58 0.81 0.82 0.91 0.87 0.90 0.92 0.88411]414 y0.68 0.73 y0.40 y0.64 0.94 0.69 0.76 0.75 0.37 y0.17 0.76 0.74 0.72 0.94 0.93 0.87 0.80430]438 0.88 y0.68 y0.68 y0.23 y0.41 0.74 0.17 0.19 0.65 0.42 0.21 0.68 0.60 0.42 0.45 0.45 0.66 0.66460]519 0.24 y0.53 0.55 0.12 y0.21 0.22 0.28 0.39 0.59 0.78 0.88 0.90 0.64 0.84 0.86 0.66 0.76 0.60466,490, y0.05 y0.34 y0.42 0.25 y0.21 0.40 0.85 0.65 0.66 0.81 0.86 0.75 0.48 0.87 0.74 0.70 0.73 0.36

491480]486 0.23 0.05 0.27 0.34 0.58 0.07 0.52 0.59 0.75 0.78 0.55 0.56 0.42 0.64 0.40 0.53 0.63 0.52490]496 0.46 0.48 0.87 0.59 0.62 0.80 0.73 0.74 0.54 0.31 0.76 0.71 0.56 0.85 0.75 0.73 0.93 0.63493 0.51 0.45 0.84 0.64 0.68 0.91 0.21 0.56 y0.58 y0.11 0.39 y0.03 y0.01 y0.22 y0.57 0.27 y0.36 y0.17680]709 0.74 0.68 0.51 0.16 0.78 0.33 0.47 0.78 0.38 0.73 0.21 y0.09 0.60 0.72 0.51 0.39 0.80 0.59710]739 0.67 0.41 0.59 0.33 0.46 0.24 0.55 0.21 0.10 0.31 0.08 y0.15 0.05 0.14 0.43 0.29 0.25 0.62

a Significant coefficients are printed in bold type. For classification according to ICD 9, see Table 1.


the remaining correlation coefficients are not in-cluded. The fact that a disease is caused or pro-moted by increased rates of deposition of anelement would be reflected in a positive correla-tion; positive correlation coefficients are there-fore, printed in bold type. Negative correlationsmay be interpreted as a ‘protective effect’ of anelement against the disease concerned.

Correlations between the incidence of diseasesand the mean element concentrations in the mosssamples from each district. Only significant corre-

Ž .lation coefficients PF0.1 are shown; positivecorrelation coefficients are printed in bold type.

The concentrations of the elements Ce, Fe, Gaand Ge in Polytrichum formosum correlate sig-nificantly with the incidence of malignant neo-

Žplasms of the trachea, bronchus and lung ICD.162 and the incidence of diseases of the skin and

Ž .subcutaneous tissue ICD 680]709 .There is a significant positive correlation

between the thallium concentrations detected inthe moss Polytrichum formosum and the inci-dence of diseases of the circulatory system in

Ž .general ICD 390]459 ; in particular there is acorrelation with essential and secondary hyper-

Ž .tension ICD 401]405 , acute myocardial infarc-Ž .tion ICD 410 , other forms of ischaemic heart

Ž .disease ICD 411]414 and chronic obstructiveŽ .lung disease ICD 490]496 . The Tl concentra-

tion in Pleurozium schreberi also correlates sig-nificantly with the incidence of essential and sec-

Ž .ondary hypertension Table 3 . The evaluationŽ .according to age groups Method 2 gives a very

clear indication of the connection between Tlconcentrations and the occurrence of acute my-ocardial infarction and hypertension in the age

Ž .groups over 40 Pleurozium schreberi, Table 4Žand over 25 years of age Polytrichum formosum,

.Table 5 . In the older age groups there are highlysignificant correlations for both men and women.

ŽSince Tl and K have a similar ionic radius 150.and 151 pm, respectively , Tl has an effect on the

conduction system of the heart and the cardiacŽ .muscle Marquart and Schafer, 1997 .¨

Table 4 shows the coefficients of correlationbetween essential and secondary hypertensionŽ .ICD 401]405 and the element concentrations inPleurozium schreberi.

Table 5 shows the results of correlating thethallium concentration in Polytrichum formosumwith the diseases in the ERN.

Significant correlations were found betweenconcentrations of the elements Nd, Sn and Th inPolytrichum formosum and the incidence of

Ž .leukaemia ICD 204]208 . In contrast to this, thecorrelation coefficient for Tl is significantly nega-tive. Practically nothing is known about the toxic-ity of Nd. The toxicity of inorganic tin compoundsis generally considered to be low, but tin organylsare suspected of having a carcinogenic effectŽ .Oehlmann and Markert, 1997 . Thorium mayhave a carcinogenic effect because of its radioac-tivity.

Chromates are carcinogenic; they mainly causeŽtumours of the nose and lungs Marquart and

.Schafer, 1997; Oehlmann and Markert, 1997 . The¨Cr concentrations detected in Polytrichum formo-sum show a positive but not significant correlationŽ .rs0.63 with the malignant neoplasms of the

Ž .trachea, bronchus and lung ICD 162 .Significant correlations were found between the

concentrations of such elements as Ce, Fe, Gaand Ge in the biomonitors and diseases of therespiratory tract. Cerium may be regarded asnon-toxic, and Fe is essential to all organisms.Gallium is slightly toxic and has a stimulant effectlike that of Ce. Germanium is also thought to benon-toxic, but some Ge compounds are poisonous.These elements are found in dust deposits. Theirsources are the burning of fossil fuels and thedrifting of dust on the ground. High Fe concen-trations in the mosses indicate a generally highlevel of pollution with dust, which may result inrespiratory tract disease.

3. Summary and evaluation

This is the first study comparing pollution withnumerous elements, determined by moss moni-toring, with the incidence of various types ofdisease. For most of the elements, the regionstudied was found to have a level of pollutionsimilar to that of many other European regions.This means that it may be regarded as a modelcase.


Since the elements are taken in chiefly by in-halation, a connection between pollution and dis-eases of the respiratory tract was to be expected.A connection was indeed found between suchdiseases and levels of the elements Ce, Fe, Gaand Ge in the mosses. Unexpectedly, a correla-tion was also proved to exist between thalliumconcentrations and heart disease.

In the case of some other elements, such as Cr,there seems to be a connection with certain dis-eases but no significant correlation was observed.The reason may be that the element concentra-tions in the deposits are only one of many factorsinvolved in pollution. Other factors, such as in-door air contaminants or personal habits, mayoverlay the effects of atmospheric deposition.

The significant correlations found between theelement concentrations in the mosses Pleuroziumschreberi and Polytrichum formosum and the inci-dence of a disease can only provide indications asto the possible causes of the disease. Causality isnot taken into account when the correlation co-efficients are calculated. This means that correla-tions can never prove that a connection exists. Todo so will be the task of further studies, for whichthese results may offer initial hints.

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