determination of atmospheric pollution around the thermoelectric power plant using a moss...

Determination of atmospheric pollutionaround the thermoelectric power plant using a mossbiomonitoring

Bojana Dimovska & Robert Šajn & Trajče Stafilov &

Katerina Bačeva & Claudiu Tănăselia

Received: 15 January 2014 /Accepted: 11 March 2014# Springer Science+Business Media Dordrecht 2014

Abstract To establish the level of atmospheric deposition ofheavy metals in Bitola and its environs, Republic ofMacedonia, biomonitoring moss technique was applied.Moss samples were collected from 38 locations in the areaof 1,400 km2. About 60 elements were analyzed by theapplication of inductively coupled plasma-mass spectrometry(ICP-MS) and atomic emission spectrometry with inductivelycoupled plasma (ICP-AES). The obtained results of the inves-tigated elements were statistically processed, and the maps ofareal distribution were prepared. Two geogenic (F1 and F4)and two anthropogenic geochemical associations (F2 and F3)were determine: F1 (Al, As, Ba, Be, Bi, Co, Cr, Cs, Fe, Ga,Ge, Hf, In, Li, Ni, Rb, Sc, Sr, Ta, Te, Tl, V, Y, Zr, lanthanidesand platinum group metals); F2 (Ag, Cd, Cu, Hg, Pb, Sn andZn); F3 (B, Ca, K, Mg, Na and P) and F4 (Nb, Sb and W). Onthe basis of visual inspection of similarities of spatial distri-bution of element patterns, comparison of basic statisticalparameters, the correlation coefficient matrices and the resultsof multivariate statistical analyses were established. Arsenicwas a special case; it was the only element that illustrates theoperation of open pit brown coal mining and thermoelectricpower plant (REK-Bitola).

Keywords Air pollution . Heavymetals . Moss .

Biomonitoring . ICP-MS . Bitola . Macedonia

Introduction

The use of mosses as biomonitors of atmospheric depositionof metals on a regional scale was introduced in Scandinaviamore than four decades ago (Rühling and Tyler 1973), and it ispresently widely accepted as a method to assess the atmo-spheric deposition of metals (Rühling and Steinnes 1998;Harmens et al. 2003). Mosses have only a rhizoids systemand readily take up elements from the atmosphere.Mosses canuptake metal cations though ionic exchange and formation ofcomplex compounds. Exchange and chelating of metals inmosses are made possible by the chemical constituents of themoss structure, which consists of a variety of organic func-tional groups capable of chelate formation and ion exchange(Rühling and Tyler 1968, 1971; Rühling and Steinnes 1985;Harmens et al. 2003, 2008, 2010; Steinnes 1995).

Atmospheric pollution with heavy metals is a global processinitiated by the world technology progress and human exploita-tion of natural resources. Four types of processes affect airpollution levels: emissions, chemistry, transport and deposition(Manahan 2000). Although the most obvious reasons for thiskind of study concerns are human health issues and environmen-tal problems from air pollution, the possible economic impact isalso an important issue, bearing in mind that Republic ofMacedonia is an exporter of food products.

Republic of Macedonia was involved in the UNECE ICPVegetation (United Nations Economic Commission forEurope International Cooperative Programme on Effects ofAir Pollution on Natural Vegetation and Crops)—HeavyMetals in European Mosses—for the first time in 2002 (sur-vey 2000/2001) and then in 2005 and 2010 when atmosphericdeposition of trace elements was studied over the entire terri-tory of the country using moss samples (Barandovski et al.2006, 2008, 2012, 2013). It was found that the most importantemission sources are mines and drainage systems and smeltersnear the towns of Veles, Tetovo, Kavadarci and Radoviš, and

B. Dimovska : T. Stafilov (*) :K. BačevaInstitute of Chemistry, Faculty of Science, Ss Cyril and MethodiusUniversity, POB 162, 1000 Skopje, Republic of Macedoniae-mail: [email protected]

R. ŠajnGeological Survey of Slovenia, Ljubljana, Slovenia

C. TănăseliaINCDO-INOE 2000 Research Institute for AnalyticalInstrumentation (ICIA), Cluj-Napoca, Romania

Air Qual Atmos HealthDOI 10.1007/s11869-014-0257-8

some uranium deposition patterns were described by the activityof power plants using lignite coal as fuel. Therefore, additionalstudies on air pollution were realized on specific area such ascopper mine (Balabanova et al. 2010, 2011, 2012) andferronickel smelter (Bačeva et al. 2011, 2012). Also, additionalstudies were performed on soil pollution which is also a reasonfor air pollution near the Pb-Zn smelter plant in the city of Veles(Stafilov et al. 2010a) and ferronickel smelter (Stafilov et al.2010b, 2012). Due to the similar content of heavy metals in flyash produced in the process of coal burning with the soil, it isdifficult to establish the size of the heavymetals pollution aroundthe thermoelectric power plants (Kapicka et al. 1999) throughsoil analysis compared with the pollution around the metallurgi-cal plants (Stafilov et al. 2010a, b). For this reason, it is moreeffective to follow the influence of the pollution from suchfacilities through air monitoring.

Near the city of Bitola, situated on the south of theRepublic of Macedonia, the biggest thermoelectric powerplant using lignite is situated. Therefore, the purposes of thisstudy were to establish heavy metals deposition in the city ofBitola and its surrounding area environ, by using the mossbiomonitoring technique, and to define the extent of possibleair pollution.

Materials and methods

Study area

The study area is located in southwest part of the Republic ofMacedonia (Fig. 1), with largeness of 35 km (W-E)×40 km(S-N), total 1,400 km2.

The Bitola region, as well as the whole Pelagonia Valley israther southern positioned and due to the latitude should havean altered Mediterranean climate. However, althoughPelagonia Valley is at a distance of 155 km from theAdriatic Sea and at about 130 km from the Aegean Sea, stillthe Mediterranean climate influence isn’t much felt, becauseof the highmountainous surrounding of the valley, and its ownheight above the sea level (it is between 571 and 770 m).That’s why the temperate-continental, continental and moun-tainous climate are mostly felt (Lazarevski 1993).

In Bitola, the north wind prevails and is with averageannual constancy of 189‰, the average speed is 2.2 m s−1

and the maximum speed is up to 15.5 m s−1 (Fig. 2). Thesecond for its constancy is the south wind with average of134‰ with annual average speed of 3.7 m s−1 and maximumspeed of 18.9 m s−1. The northwest wind is also present withaverage of 83‰ with average speed of 2.4 m s−1 and maxi-mum speed up to 18.9 m s−1. The often draft and relativelyhigh temperatures allow increasing of vapor conditions fromfree water and soil surfaces. In this regard, conditions for

increasing of possible vapor are created. The average vaporis 855 l m−2 (Lazarevski 1993).

Generally speaking, if we analyze the characteristicsand the appearance of the climate phenomena during ayear, we will see that Bitola and Pelagonia belong to awarm continental area. The climate in Bitola has mod-erate continental characteristics with an emphasized con-tinental component, because of the closeness of themountainous relief, the height above the sea level, thenear-by valley and so on, and these facts make theclimate in Bitola and Pelagonia very dynamic and nonstable. The microclimate is influenced by the air pollu-tion caused by the great polluters (REK Bitola, the cityboiler rooms, the traffic, the chimneys of the houses andthe industrial zone), which release gases that representone of the components of the thick fogs transiting intoindustrial smog.

The Mining Power Complex “Bitola” is located in theperiphery of Pelagonia plain near the village of Novaci. Theplant which basic activity is the production of electricity, andcoal is the biggest in the system of the Macedonian electricpower plant and consists of mine “Suvodol” andThermoelectric power plant. The production in the plantstarted in 1980 and has a capacity of 660 MW. The open pitmine Suvodol is located 15 km east from Bitola and extendsover 9.0 km2. The exploitation in the mine Suvodol has startedin 1977 with the excavation of coal has started in 1982.

Other industries that could be considered air pollutants areRenosil, the factory produces quartz, and the industry formetal processing “Metalec” Bitola. Both are located in theindustrial zone in Bitola.

Geological description

The study area includes parts of two large tectonic units: thePelagonian massif and the West-Macedonian zone. ThePelagonian massif is separated from the West-Macedonianzone by a big reverse Pelagonian fault, which is covered byyoung Quaternary deposits. Simplified geological map withmajor lithological unit is provided in Fig. 2. The Pelagonianmassif is built of precambrian metamorphic and igneousrocks. The predominant rocks on the western Pelagonianmassif are banded muscovite gneisses, but also other varietiessuch as the banded muscovite-biotite gneisses and augen-amydaloidal two-mica gneisses. Medium to coarse grainedgranodiorites, garnet-staurollite and garnet-cyanite occur asintrusive bodies in the gneiss-mica-schist series. By the influ-ence of granodioritic magmas, the surrounding rocks werefelpatized and augen-amydaloidal two-mica gneisses andbanded two-mica gneisses originated, the feldspatization ofmuscovite gneiss was weak. Some relicts of amphibolite andschistose amphibolite are present in the study area.

Air Qual Atmos Health

The oldest rocks in the West-Macedonian zone arePaleozoic age and consist of low metamorphic schistsand granitic rocks. The most widespread granite isalkalic granite, extending in N-S direction. The oldestgranitoides rocks are represented by biototic andamphibolitic granodiorites to which granosyenite andsyenite are connected on the southern part of theWest-Macedonian zone. Following the direction N-S,on the left side of the map, Silurian-Devonian formationoccurs composed of phyllite, slate, metasandstone andmetaconglomerate which alter to older Paleozoic rocks.Ordovician-Silurian greenshist and conglomerates withintrusions of quartz-sericite schist and schistose quartzitedeveloped in southern part of Bitola. During Plioceneand Quaternary were first deposited the lacustrine sedi-ments in the Pelagonian depression. They begin withMiddle Pliocene gravels, sands and clays with coalbeds, while the upper part is composed of poorly sortedgravels, sand and silty clays. Holocene is represented bydeluvial and alluvial deposits.

Sampling and sample preparation

The collection of moss samples was performed according tothe protocol adopted within the European Heavy MetalSurvey (Harmens 2010). Moss species were collected accord-ing to previously defined sampling network around the Bitolain area of 1,400 km2. Thirty-eight samples of mosses werecollected during the period from July 2010 till December2010. Dominant types of collected moss are Hypnumcupressiforme and Homalothecium lutescens. The samplingnetwork with numbered sampling sites is shown in Fig. 3. Thesampling procedure rule was: samples that were collectedmust be in a distance of minimum 300 m from main roads,100 m from local roads and 200 m from settlements. Eachsample was composed of five subsamples collected within anarea of 50×50 m. Collected material was stored in paper bags.A separate set of disposable polyethylene gloves was used forthe collection of each sample. In the laboratory, the sampleswere cleaned from extraneous plant material and soil particles,and air-dried at room temperature. Only the last 3 years, worth

Fig. 1 Studying area


of growths of the moss materials was used without washingfor the analysis. Prepared in that way, moss species were readyfor digestion.

Chemicals that were used for digestion are: nitric acid, tracepure (Merck, Germany) and hydrogen peroxide, p.a. (Merck,Germany). Redistilled water was used for the preparation ofall solutions.

Microwave digestion system was applied for these sam-ples. In teflon digestion vessels, 0.5 g of moss samples to-gether with 5 mL concentrated nitric acid, HNO3 and 2 mLhydrogen peroxide, H2O2 (30 %, m V−1) were added, and thevessels were closed, tightened and placed in the rotor of amicrowave digestion system (Marsx, CEM, USA). After

digestion and cooled, samples were quantitatively transferredinto 25-mL calibrated flasks.

Instrumentation

Atomic emission spectrometry with inductively coupled plas-ma (AES-ICP) and inductively coupled plasma-mass spec-trometry (ICP-MS) were the techniques that were used. Thefollowing elements were determined: Al, As, Be, Bi, Co, Cr,Cs, Fe, Ga, Ge, Hf, In, Li, Ni, Rb, Sc, Ta, Te, Tl, V, Y, Zr, La,Lu, Ru, Pt, B, Ca, Cu, K, Mg, Mo, Na, P, Zn, Au, Ba, Hg, Sr,Ag, Cd, Pb, Sn, Br, I, Nb, Sb, W, Ce, Dy, Er, Eu, Gd, Ho, La,Lu, Nd, Pr, Sm, Tb, Tm and Yb.

Fig. 2 Geological map with windrose


For ICP-MS measurements, a SCIEX Perkin Elmer ElanDRC II (Canada) inductively coupled plasma mass spectrom-eter (with quadruple and single detector setup) was used(Tănăselia et al. 2008). The running parameters of the instru-ment were checked and adjusted before every batch of mea-surements, using a solution with 1 μg mL−1 In, 1 μg mL−1 Ce,10 μg mL−1 Ba and 1 μg mL−1 Th and Mg. Oxide levels anddouble ionised levels were kept under 3 %, background forboth low and high mass was under 1 cps and all the otherparameters were chosen by considering the best signal/noiseratio. The dynamic reaction chamber (DRC) was used in rf-only mode (no gas), and its parameters were optimised else-where (Tănăselia et al. 2008). For the sample introductionsystem, a classic setup was used consisting of a peristalticpump, a Meinhard nebulizer and a cyclonic spray chamber,where the fine aerosols are formed that go directly into theplasma. All other reagents were supplied by Merck. Eighteenmegaohm per centimetre of DI water was prepared in thelaboratory, using a Millipore-Milli-Q® ultrapure water purifi-cation system.

All measurements were conducted using the semi-quantitative method (TotalQuant) supplied by the Elan 3.4software that uses a response factor calibration curve which

was obtained by calibration at multiple points (low, mediumand high mass for optimum setup), using a multi-elementMerck VI standard solution, diluted to mimic real samplecomposition. The drawback is that the accuracy tends to beworse than a proper quantitative method for some elements;however, the main advantage is the large mass interval thatcan be studied (up to 65 elements per sample during asingle run) and a good choice for screening type mea-surements that requires a large throughput of samples withmany elements of interest.

For this study, a NIST 2709 and NIST 1643e certi-fied reference materials were used to check the accuracyof the method and for all the elements considered, thedifference between the measured and certified valueswas within 15 %. The theoretical limit for ICP-MSmethods are in the part per trillion (ng L−1) range forthe majority of the elements. Matrix effects above the1 ppb (μg L−1) threshold while using TotalQuant werenot observed during our study. For some elements,values between these two levels were further investigat-ed using more complex quantitative methods.

Quality control was also ensured by standard moss refer-ence materials M2 and M3, which are prepared for the

Fig. 3 Land-use map withlocation of sapling sites


Table 1 Basic statistic parameters (n=38)

Unit Dis X Md Xg Min Max P10 P90 S Sx CV A E

a

Ag μg kg−1 Log 22 17 20 7.8 81 10 38 13 2.2 60 0.51 0.72

Al mg kg−1 Log 4,200 3,000 3,300 1,300 16,000 1,500 8,100 3,100 500 74 0.33 −0.99As mg kg−1 Log 0.46 0.38 0.41 0.22 1.3 0.25 0.77 0.23 0.038 51 0.67 −0.17Au μg kg−1 Log 0.36 0.27 0.20 0.050 1.2 0.050 0.90 0.35 0.057 96 −0.01 −1.67B mg kg−1 Log 0.89 0.73 0.77 0.27 2.4 0.40 1.7 0.53 0.086 59 0.36 −0.47Ba mg kg−1 Log 38 34 35 17 77 19 63 16 2.7 43 0.12 −1.04Be mg kg−1 Log 0.14 0.11 0.11 0.029 0.43 0.044 0.24 0.097 0.016 71 0.04 −1.22Bi μg kg−1 Log 23 18 18 6.5 140 8.2 37 23 3.7 100 0.78 1.40

Br mg kg−1 Log 0.85 0.77 0.72 0.095 2.9 0.37 1.3 0.50 0.082 59 −0.74 2.44

Ca mg kg−1 Log 8,000 7,500 7,800 4,400 13,000 6,300 10,000 1,600 260 20 −0.18 1.29

Cd μg kg−1 Log 70 57 62 24 270 36 120 44 7.1 62 0.85 1.43

Ce mg kg−1 Log 4.3 3.3 3.3 1.3 17 1.3 7.9 3.4 0.55 79 0.25 −1.00Co mg kg−1 Log 1.1 0.83 0.86 0.34 3.1 0.38 2.3 0.74 0.12 70 0.29 −1.09Cr mg kg−1 Log 4.3 3.1 3.3 1.2 14 1.5 11 3.3 0.54 78 0.43 −0.74Cs mg kg−1 Log 0.20 0.14 0.15 0.054 0.78 0.064 0.39 0.15 0.025 78 0.27 −1.12Cu mg kg−1 Log 4.2 3.6 3.8 2.1 14 2.6 6.4 2.1 0.35 51 0.94 1.34

Dy mg kg−1 Log 0.31 0.20 0.22 0.076 0.97 0.090 0.71 0.25 0.040 81 0.29 −1.33Er mg kg−1 Log 0.16 0.10 0.11 0.038 0.53 0.044 0.39 0.13 0.022 85 0.33 −1.31Eu μg kg−1 Log 84 67 68 23 290 30 160 59 10 71 0.30 −0.70Fe mg kg−1 Log 3,700 2,900 3,000 1,100 11,000 1,300 6,800 2,400 400 67 0.22 −1.07Ga mg kg−1 Log 0.90 0.78 0.72 0.30 3.5 0.34 1.7 0.64 0.10 72 0.27 −0.86Gd mg kg−1 Log 0.43 0.30 0.32 0.11 1.4 0.13 0.92 0.33 0.054 78 0.25 −1.27Ge μg kg−1 Log 40 40 35 14 80 17 72 21 3.4 52 −0.10 −1.45Hf μg kg−1 Log 25 22 21 3.7 88 10 49 16 2.6 64 −0.22 0.84

Hg μg kg−1 N 16 14 10 0.50 51 1.8 33 12 2.0 76 0.84 0.42

Ho μg kg−1 Log 59 36 42 15 190 16 140 48 7.9 83 0.31 −1.34I μg kg−1 Log 58 50 50 20 140 23 120 32 5.2 55 0.13 −0.61In μg kg−1 Log 2.9 2.4 2.3 0.85 9.1 1.1 6.1 2.1 0.34 72 0.36 −0.90Ir μg kg−1 Log 0.18 0.15 0.14 0.049 0.6 0.049 0.35 0.12 0.020 70 −0.03 −0.55K mg kg−1 Log 6,000 5,300 5,500 3,000 12,000 3,300 9,800 2,500 410 42 0.33 −0.89La mg kg−1 Log 2.1 1.5 1.6 0.61 8.5 0.68 3.7 1.6 0.26 79 0.35 −0.84Li mg kg−1 Log 1.7 1.2 1.3 0.41 6.5 0.63 3.7 1.3 0.21 79 0.48 −0.47Lu μg kg−1 Log 20 13 14 4.6 69 5.2 48 17 2.7 86 0.31 −1.32

b

Mg mg kg−1 N 3,800 4,000 3,800 2,700 4,800 3,200 4,500 500 82 13 −0.27 −0.43Mn mg kg−1 Log 150 150 140 46 360 85 230 69 11 45 −0.30 0.03

Mo mg kg−1 Log 0.19 0.15 0.16 0.050 0.70 0.082 0.35 0.13 0.021 66 0.32 −0.35Na mg kg−1 Log 140 110 120 47 540 64 290 116 19 81 0.94 0.51

Nb μg kg−1 Log 73 73 42 1.8 320 5.7 150 66 11 90 −0.84 −0.32Nd mg kg−1 Log 1.8 1.4 1.4 0.55 6.7 0.56 3.6 1.4 0.23 77 0.25 −1.08Ni mg kg−1 Log 4.2 3.6 3.8 2.1 11 2.3 7.8 2.2 0.35 51 0.84 0.18

Os μg kg−1 Log 0.11 0.050 0.075 0.025 0.45 0.025 0.30 0.10 0.016 92 0.29 −1.07P mg kg−1 Log 1,400 1,300 1,300 510 2,600 700 2,100 530 87 39 −0.39 −0.46Pb mg kg−1 Log 3.4 2.8 2.8 0.91 21 1.3 5.0 3.2 0.51 94 0.99 3.18

Pd μg kg-1 μg kg−1 Log 20 14 15 2.0 85 6.2 48 17 2.7 83 −0.03 0.19

Pr mg kg−1 Log 0.48 0.36 0.37 0.14 1.8 0.15 0.92 0.37 0.060 77 0.24 −1.08Pt μg kg−1 Log 0.46 0.35 0.38 0.10 1.5 0.19 0.85 0.31 0.051 68 0.18 −0.38Rb mg kg−1 Log 7.5 5.3 6.2 2.4 25 3.1 15 5.0 0.82 68 0.44 −0.56Re μg kg−1 Log 0.25 0.050 0.084 0.025 6.2 0.049 0.20 1.0 0.16 394 2.68 10.97

Rh μg kg−1 Log 4.4 3.8 3.6 0.73 14 1.4 8.3 3.1 0.50 70 −0.05 −0.34Ru μg kg−1 Log 1.3 0.22 0.27 0.050 17 0.050 2.10 3.7 0.59 286 1.18 1.64

Sb μg kg−1 N 10 10 6.3 0.35 34 0.79 19 7.7 1.3 77 0.88 1.07


Table 1 (continued)

Unit Dis X Md Xg Min Max P10 P90 S Sx CV A E

Sc mg kg−1 Log 0.97 0.92 0.88 0.28 2.5 0.41 1.6 0.45 0.073 46 −0.31 0.40

Sm mg kg−1 Log 0.36 0.26 0.28 0.10 1.2 0.11 0.73 0.27 0.044 75 0.22 −1.23Sn mg kg−1 Log 0.11 0.11 0.10 0.036 0.30 0.058 0.18 0.051 0.0082 45 −0.07 0.16

Sr mg kg−1 Log 24 24 22 8.0 57 13 35 9.0 1.5 38 −0.29 1.12

Ta μg kg−1 Log 0.8 0.54 0.57 0.19 2.6 0.20 1.79 0.67 0.11 86 0.30 −1.04Tb μg kg-1 μg kg−1 Log 57 39 42 15 170 17 120 45 7.3 79 0.25 −1.29Te μg kg−1 Log 1.3 0.94 0.57 0.050 5.3 0.050 3.15 1.2 0.20 99 −0.60 −1.15Tm μg kg−1 Log 22 14 16 5.3 77 5.8 56 19 3.1 86 0.31 −1.32Tl μg kg−1 Log 44 37 37 11 140 17 85 28 4.5 63 0.13 −0.57V mg kg−1 Log 5.1 4.2 4.1 1.7 20 1.8 11 3.9 0.63 76 0.45 −0.52W μg kg−1 N 9.0 9.4 7.9 1.6 22 3.0 15 4.4 0.72 49 0.61 0.69

Y mg kg−1 Log 1.5 1.0 1.1 0.33 4.6 0.46 3.4 1.3 0.20 82 0.28 −1.25Yb μg kg−1 Log 140 87 97 30 480 35 340 120 19 86 0.30 −1.31Zn mg kg−1 Log 19 15 16 7.3 140 8.3 27 22 3.5 112 2.11 7.47

Zr mg kg−1 Log 1.1 0.98 0.93 0.21 4.1 0.46 2.1 0.75 0.12 67 0.15 0.14

Dis distribution; X mean; Md median; XG geometrical mean; Min minimum; Max maximum; P10 lower quartile; P90 upper quartile; S standarddeviation; SX standard error of mean; CV coefficient of variation (%); A skewness; E kurtosis

Table 2 Comparison of median values obtained in the present study with the same parameters obtained in similar studies in the Republic of Macedonia(in mg kg−1)

Element Bitola 2010 (present work) Macedonia 2010, (Barandovski et al. 2013;Harmens et al. 2013)

Macedonia 2005,(Barandovski et al. 2012)

Md Min–Max Md Min–Max Md Min–Max

Al 3,000 1,300–16,000 1,900 540–8,700 3,600 1,466–25,860

As 0.38 0.22–1.3 0.68* 0.18–4.32 0.67 0.18–4.3

B 0.73 0.27–2.4 6.6 0.010–94 / /

Ba 34 17–77 34 7.1–240 52 18–184

Ca 7,500 4,400–13,000 7,100 2,900–12,000 8,547 5,237–16,280

Cd 0.057 0.024–0.270 0.22* 0.068–2.2* 0.29* 0.015–3.01*

Cr 3.1 1.2–14 3.5 1.0–40 6.79 2.09–82

Cs 0.14 0.054–0.78 / / 0.32 0.13–2.3

Cu 3.6 2.1–14 3.5 2.0–11 6.7* 0.7–21.4*

Fe 2,900 1,100–11,000 1,500 510–6,300 2,238 998–8,130

Hg 0.014 0.0005–0.051 0.09* 0.01–0.59 0.07* 0.01–0.42

K 5,300 3,000–12,000 4,600 2,100–7,600 7,510 4,652–13,530

Li 1.2 0.41–6.5 1.0 0.29–5.1 / /

Mg 4,000 2,700–4,800 1,900 610–4,900 1,311 658–3,351

Mn 150 46–360 130 30–440 188 54–595

Ni 3.6 2.1–11 3.5 1.3–52 5.8 1.80–43

P 1,300 510–2,600 1,100 420–2,000 / /

Pb 2.8 0.91–21 4.6* 1.9–22* 7.6* 0.1–46.1*

Sr 24 8.0–57 24 5.6–100 34 13–140

V 4.2 1.7–20 3.5 1.0–17 6.4 2.50–32

Zn 15 7.3–140 20 1.0–129 36 16–91

*Determined by AAS


European Moss Survey (Steinnes et al. 1997). The measuredconcentrations were in good agreement with the recommend-ed values (difference between the measured and certifiedvalues was within 10 %). The method of standard additionswas also applied, and quantitative recoveries were achievedfor the most of the elements.

Data processing and statistical analyses

All field observations, analytical data and measurements wereentered into a data matrix. For each observation, there were 86variables recorded: sample identification number, geographicalcoordinates, altitude, type of sampling and analysis, land use,lithological characteristics and the analytical results of 66elements.

For statistical analysis, parametric and nonparametric sta-tistical methods were used, and normality tests of data distri-butions were performed (Zhang et al. 1998). Data analysis andproduction of maps were performed on a PC using theParadox (Ver. 9) Statistica (Ver. 11-Software), AutoDeskMap 3D (Ver. 2012) and Surfer (Ver. 11-Software) software.

Results and discussion

On the basis of the results of normality tests and visualinspection of individual histograms of all analysed elements,normal distribution was assumed for natural values of Hg,Mg, Sb and W. For the all other elements, logarithms ofconcentration values were considered as normally distributed.The descriptive statistics of analysed elements are shown inTable 1a and b. Due to the very similar distribution of lantha-nides (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nb, Pr, Sm, Tb, Tm andYb) and platinum group metals (Ir, Os, Pd, Pt, Rh and Ru) inbivariant and multivariant statistical analyzes, processing as atwo new variables (La-Lu and Ru-Pt) standardized to zeromean. The degree of association of chemical elements wasassessed using the linear coefficient of correlation (Cohen1988).

Median values for the content of all elements were com-pared with the median values for the same elements for theentire territory of Republic of Macedonia (Barandovski et al.2012, 2013) and with some other countries in the Europeanmoss network from the Balkan region: Serbia (Frontasyeva

Table 3 Comparison of the results obtained in present study with other Balkan countries and Europe (in mg kg-1)

Element Bitola, 2010(this work)

Macedonia, 2010(Barandovski et al. 2013)

Bulgaria, 2010/2011(Harmens et al. 2013)

Serbia, 2005(Harmens et al. 2008)

Albania, 2010(Harmens et al. 2013)

European mosssurvery 2005/2006 Harmenset al. 2008)

Md Min–Max Md Min–Max Md Min–Max Md Min–Max Md Min–Max Md Min–Max

Al 3,000 1,300–16,000 1,900 540–8,700 242 114–696 3,946 1,117–31,180 1,650 535–6,974 / /

As 0.38 0.22–1.3 0.68* 0.18–4.32 0.16 0.05–0.89 1.41 0.22–21.6 0.24 0.04–2.20 0.42 0.10–9.36

B 0.73 0.27–2.4 6.6 0.01–94 / / / / / / / /

Ba 34 17–77 34 7.1–240 / / / / / / / /

Ca 7,500 4,400–13,000 7,100 2,900–1,2000 / / / / / / / /

Cd 0.06 0.02–0.27 0.22* 0.07–2.2* 0.30 0.09–0.69 0.26 0.04–1.11 0.11 0.04–0.90 0.20 0.07–1.26

Cr 3.1 1.2–14 3.5 1.0–40 0.92 0.54–3.89 6.44 2.0–78.8 4.83 1.62–31.8 2.32 0.72–29.3

Cu 3.6 2.1–14 3.5 2.0–11 6.50 3.27–11.5 11.1 3.9–451 3.96 1.52–11.1 6.80 3.07–91.2

Fe 2,900 1,100–11,000 1,500 510–6,300 365 171–1,109 2,267 670–16,100 1,629 469–5,488 799 233–6,147

Hg 0.014 0.005–0.051 0.09* 0.01–0.59 0.06 0.02–0.32 / / 0.13 0.03–2.23 0.06 0.01–0.42

K 5,300 3,000–12,000 4,600 2,100–7,600 / / / / / / / /

Li 1.2 0.41–6.5 1.0 0.29–5.1 / / / / / / / /

Mg 4,000 2,700–4,800 1,900 610–4,900 / / / / / / / /

Mn 150 46–360 130 30–440 / / / / / / / /

Ni 3.6 2.1–11 3.5 1.3–52 1.41 0.72–22.7 4.43 1.7–23.8 5.81 1.56–131 2.26 0.71–63.4

P 1,300 510–2,600 1,100 420–2,000 / / / / / / / /

Pb 2.8 0.91–21 4.6* 1.9–22* 3.87 2.12–12.5 16.7 1.0–249 2.42 1.34–19.7 1.76 1.76–46.9

Sr 24 8.0–57 24 5.6–100 / / / / / / / /

V 4.2 1.7–20 3.5 1.0–17 1.14 0.41–2.76 5.76 1.94–32.7 3.52 1.15–16.9 2.82 0.80–21.6

Zn 15 7.3–140 20 1.0–129 44.2 16.6–132 29.0 13.2–259 13.8 1.00–68.1 33.6 15.2–177

*Determined by AAS


et al. 2004; Harmens et al. 2008), Bulagria (Harmens et al.2013), Albania (Harmens et al. 2013) and the values forEurope (Harmens et al. 2008), Tables 2 and 3.

Because in the West Balkan, the Neogen coal bedswere enriched with U and mostly with As (Lazarov andSerafimovski 1997; Barandovski et al. 2008; Bunnellet al. 2002; Stuhlberger 2009) with great importancewas to established the content of the arsenic in themoss samples from the Bitola region. To investigatethe anthropogenic influence in the studied area, specialattention was given to the behavior of As that waspresent mostly as a consequence of coal mining activi-ties and later as a result of its burning in thethermoelectrical power plant. In this study, the medianvalue for As was 0.38 mg kg−1 (Table 2), ranges from0.22 to 1.3 mg kg−1, which was 1.5 times smaller thenthe value of 0.67 mg kg−1 for the whole territory of

Republic of Macedonia (Barandovski et al. 2012).Compared with the other Balkan countries andRepublic of Macedonia (Barandovski et al. 2012),Bitola had the highest values for As (Table 3). Thus,the investigated region has significantly higher medianvalues for As than Bulgaria and Albania (0.16 and0.24 mg kg−1, respectively) (Table 3) confirming thatits present is due to the coal processing in thethermoelectical plant near Bitola. This is also confirmedby the spatial distribution of arsenic where it can beseen that the highest contents of arsenic are precisely inthe moss samples collected near the power plant and flyash landfill.

Compared with the medians for the whole territory ofRepublic of Macedonia and other European countries, otherelement that shows deviation was vanadium.Median value forthe moss samples that were collected in 2010 in the Bitola

Fig. 4 Dendrogram of clusteranalyses (n=38)


region was 1.2 times higher than the median value forthe whole territory of the Republic of Macedonia in2010. The results were opposite for the survey in2005, where the median value for V was 1.5 timeshigher compared with the median value for V in theBitola region 4.2 mg kg−1 (Tables 2 and 3). Thesedecreased differences in moss survey from 2005 to2010 in the content of V were probably as a result bythe use of various techniques: neutron activation analy-sis (NAA) in the survey in 2005 and ICP-AES in thesurvey in 2010. The content for V in the moss samplescollected from this region has the highest value com-pared with some other Balkan countries and the medianfor the whole Europe (Table 3).

Multivariate cluster and R-mode factor analyses (FA) wereused to reveal the associations of chemical elements. Bymultivariate analysis, 38 samples were examined. The clusteranalysis (Garson 2000; Reimann et al. 2002; Šajn 2005, 2006)was performed on 42 selected chemical elements includingnew variables La-Lu and Ru-Pt. Following elements: Au, Br,I, Mn, Mo and Re were eliminated from further analysis,because of their tendency to form their own clusters, notshowing a reasonable connection with other chemical ele-ments. The dendrogram displays the results of hierarchicalcluster analysis, grouping the elements into four groups(Fig. 4).

The factor analysis was performed also at aforementionedselected chemical elements (Davis 1986; Reimann et al.2002; Filzmoser et al. 2005). Elements with low shareof communality or tendency to form independent factorswere excluded too. FA was performed after normaliza-tion of the data on variables standardized to zero meanand unit standard deviation. As a measure of similaritybetween variables, the product-moment correlation coef-ficient (r) was applied. For orthogonal rotation, thevarimax method was used. With FA, the characteristicsof the individual elements were reduced to four synthet-ic variables (F1 to F4), which account for c. 80 % ofthe total variability of treated elements (Table 4).

Factor 1 (F1) is strongest factor representing c. 47 % ofthe total variability. F1 associates majority of the analysedelements: Al, As, Ba, Be, Bi, Co, Cr, Cs, Fe, Ga, Ge, Hf,In, Li, Ni, Rb, Sc, Sr, Ta, Te, Tl, V, Y, Zr, lantanides (La-Lu) and platinum group metals (Ru-Pt). The group repre-sents chemical elements that are probably naturally distrib-uted. Factor 2 (F2) is the second strongest factor, with c.13 % of the total variability. This factor principally asso-ciates typical heavy metals, Ag, Cd, Cu, Hg, Pb, Sn andZn, which are presumed to be of mainly anthropogenicorigin. Factor 3 (F3) are associated elements such as B,Ca, K, Mg, Na and P, with also c. 13 % of the totalvariability. The group also links elements that are mostprobably anthropogenically distributed. Factor 4 is the

most prominent factor and joins only three elements Nb,Sb and W with c. 8 % of total variability. The group

Table 4 Matrix of rotated factor loadings (n=38)

F1 F2 F3 F4 Comm

Al 0.90 0.00 0.23 –0.30 96.3

As 0.74 0.33 0.08 0.00 66.5

Ba 0.64 0.15 0.32 0.37 67.2

Be 0.88 0.15 0.14 –0.22 87.0

Bi 0.77 0.50 0.15 –0.13 88.0

Co 0.84 0.21 0.32 –0.09 86.6

Cr 0.85 0.26 0.28 –0.05 86.7

Cs 0.92 0.19 0.10 –0.18 92.5

Fe 0.89 0.06 0.26 –0.29 95.1

Ga 0.94 0.16 0.21 –0.16 98.1

Ge 0.85 0.20 0.28 –0.29 91.7

Hf 0.89 0.08 0.15 0.05 82.0

In 0.91 0.25 0.21 –0.13 95.5

Li 0.89 0.17 -0.06 –0.04 83.0

Ni 0.73 0.46 0.18 –0.08 77.9

Rb 0.76 0.29 0.36 –0.10 81.0

Sc 0.72 0.25 0.29 –0.08 66.9

Sr 0.70 –0.04 0.17 0.29 60.8

Ta 0.79 0.07 0.37 –0.30 86.1

Te 0.74 0.02 0.06 –0.06 56.2

Tl 0.85 0.26 -0.02 –0.03 78.2

V 0.92 0.21 0.23 –0.08 95.2

Y 0.84 0.09 0.32 –0.24 87.0

Zr 0.90 0.05 0.19 –0.03 84.8

La-Lu 0.94 0.11 0.21 –0.13 95.2

Ru-Pt 0.71 0.04 0.33 –0.32 72.0

Ag –0.01 0.87 0.03 0.06 76.9

Cd 0.10 0.89 0.02 0.07 81.1

Cu 0.49 0.61 0.45 0.08 82.3

Hg 0.13 0.69 0.09 0.43 68.1

Pb 0.39 0.82 –0.19 –0.07 86.8

Sn 0.30 0.70 0.14 0.31 69.8

Zn 0.29 0.77 0.41 0.04 84.8

B 0.26 –0.07 0.65 0.30 58.7

Ca 0.32 0.23 0.75 –0.17 74.9

K 0.17 0.06 0.92 0.01 87.0

Mg 0.50 –0.32 0.72 0.01 86.8

Na 0.27 0.22 0.76 –0.08 70.5

P 0.04 0.09 0.91 0.13 86.1

Nb –0.36 0.04 –0.05 0.82 79.8

Sb –0.59 0.28 0.03 0.69 89.6

W –0.32 0.08 0.12 0.77 70.9

Prp. Totl. 46.9 13.1 13.3 7.8 81.2

F1–F4 Factor loadings;ComCommunality in%;Prp. Totl. Principal totalvariance in %; Eigen Eigenvalues


represents chemical elements that are probably naturallydistributed.

Universal kriging with the linear variogram interpolationmethod (Davis 1986) was applied for the construction of mapsshowing the spatial distribution of factor scores, as well asmaps displaying the distribution of heavy metals in topsoiland subsoil (Beelen et al. 2009). The basic grid cell size forinterpolation was 500×500 m. For class limits, the percentilevalues of the distribution of interpolated values were chosen.Four classes at the following percentiles were selected: 0–25,25–50, 50–75 and 75–100.

Two geogenic (F1 and F4) and two anthropogenic(F2 and F3) geochemical associations were establishedon the basis of (a) visual inspection of similarities ofspatial distribution of element patterns according to thegeology of the investigated area (Fig. 2); (b) comparisonof basic statistical parameters; (c) the correlation coeffi-cient matrices and (d) the results of cluster, factoranalyses. Arsenic was a special case, since it was the

only element that illustrates the operation of open pitbrown coal mining and thermoelectric power plant(REK-Bitola) and has not been classified correctly bymultivariate statistical methods.

As already was mentioned, the majority of the ana-lyzed elements (Al, As, Ba, Be, Bi, Co, Cr, Cs, Fe, Ga,Ge, Hf, In, Li, Ni, Rb, Sc, Sr, Ta, Te, Tl, V, Y, Zr,lanthanides and platinum group metals) were conductedto F1, and in the same time represents the dominantchemical fingerprint of the investigated area. Thehighest concentrations of this geochemical group occupythe western edge of the Pelagonia close to the West-Macedonian massif, which were primarily Pz rock(Fig. 2). In the mountains area itself, the concentrationswere very low. This can be explained only by the factthat the increased concentration in the moss samplesrepresenting a soil dusting or weathering of the bedrockat accumulation areas of eroded, transported and sec-ondary deposited sites of less resistant Pz rocks of the

Fig. 5 Spatial distribution ofvanadium


West-Macedonian massif, especially Pz shale. Our as-sumption was confirmed by the fact that the river andstream valleys were much more indicated on the west-ern part (Fig. 3), which also shows lower persistence ofthe West-Macedonian rocks. The shape of spatial distri-bution this geochemical association was conditioned byclay-size particles, agriculture and dominant SN winddirection. Vanadium was a leading element illustratingthe spatial distribution of whole geochemical association(Fig. 5).

Factor 4 (Nb, Sb and W) illustrates natural distributionpattern. High concentrations of these elements were found athigher altitudes of West-Macedonian massif and Pelagonianmassif. This specific distribution was conducted to the rela-tively resistant rocks, such as Pz and Pt granite and less in Ptgneiss, found on both areas (WandN part). The distribution of

this geochemical association was linked to areas with poorweathering, where the material remains “in situ”, elementsaccumulate in initial soil or in district rankers that were nottransported. It seems that dust particles were more grained(silty or sandy) which this group makes less affected by thewinds. This assumption was supported with the fact that theirhigh concentrations were not in the Pelagonia basin. Niobium,as a leading element illustrates the best spatial distribution ofwhole geochemical group (Fig. 6).

Typical anthropogenic association, represented by Factor2, conducts following heavy metals: Ag, Cd, Cu, Hg, Pb, Snand Zn. Their high concentrations of these elements wererelated to the wider area of Bitola, the second largest urbanzone in Republic of Macedonia. The highest concentrationswere found in city center but decrease with distance of thepollution source. At the same time, increased concentrations

Fig. 6 Spatial distribution ofniobium


are in E part, in the mountains. At first glance, it might beassumed that high concentrations have natural origin, butnumerous previous studies have been demonstrated that theyare result of remote transport (Johansson et al. 2009; Pal et al.2011; Duong and Lee 2011). This transport was influenced bydominant W and NW winds and precipitation.

It was obvious that the distribution wasn’t affectedby open pit coal mining and thermoelectric power plant(REK-Bitola). Cadmium may be a leading element thatillustrates the spatial distribution of the whole associa-tion (Fig. 7).

Second anthropogenic geochemical association isolatedwith Factor 3 combines B, Ca, K, Mg, Na and P. Areola ofhigh values was mainly found in Pelagonija basin, which wasan area of intensive agriculture activities. High concentrationsof K and P are most probably a consequence of the use of

fertilizers (Barandovski et al. 2013), can be concluded thatwhole group resulting with its use. Because of lowprecipitation, intensive irrigation may affect their highconcentration as well. Their distribution was influencedalso by wind, due to the fact that the fine particles fromthe plough land were transported, especially in summermonths. Potassium illustrates the best distribution of thewhole group (Fig. 8).

Operation of open pit brown coal and thermoelectric powerplant (REK-Bitola) was illustrated by distribution of As(Fig. 9). According to the results of multivariate statisticalanalyses, this element was showing high-correlation coeffi-cients with elements of F1 (natural distribution). This examplewas showing that we should not believe blindly to the multi-variate analysis, but each distribution must be checked indi-vidually. Arsenic was isolated, because in West Balkan

Fig. 7 Spatial distribution ofcadmium


(particularly Former Yugoslavia) the Neogen coal beds wereenriched with U and mostly with As (Lazarov andSerafimovski 1997; Barandovski et al. 2008; Bunnell et al.2002; Stuhlberger 2009).

Arsenic can be used as a main “marker” of pollution whichwas a consequence of coal mining activities and later to thethermo power plant. Samples of moss, a very well-establishedmedium for biomonitoring (Barandovski e al. 2008), wereclearly identified the Areola of As at S part of Pelagonija.The areola has a bit elongated shape, influenced by localwinds (Fig. 3). Some particularly high concentrations in Wpart of study area were showing natural distribution of thiselement.

From the anthropogenic association that was conductedwith the heavy metals (Ag, Cd, Cu, Hg, Pb, Sn and Zn), theleading element was Cd. High concentrations of this elementwere related to the wider area of Bitola. The median value forCd (0.29 mg kg−1) was lower in the moss samples from the

Bitola region compared with the median values for the mosssurvey in 2005 and 2010 for the whole territory of Republic ofMacedonia (0.22 mg kg−1) (Barandovski et al. 2012, 2013),Table 2. Compared with the other Balkan countries, thehighest median of 1.95 mg kg−1 in the moss samples wasestablish in the moss samples from Serbia (Harmens et al.2008), Table 3.

From the second anthropogenic geochemical association ofB, Ca, K,MgNa, and P, leading element was K.Median valuefor K in the moss samples from this research was5,300 mg kg−1 with ranges of 3,000 to 12,000 mg kg−1

(Table 1a). A high content of K in the moss samples collectedfrom the Bitola region was probably as a consequence of theuse of fertilizers in Pelagonija basin where agricultural activ-ities were present.

Median value for Niobium that was leading element in F4was 0.073 mg kg−1 (Table 2). For the whole territory ofRepublic of Macedonia and other Balkan countries were not

Fig. 8 Spatial distribution ofpotassium


found in the data for Nb. Therefore, value about Nb can’t becompared with any other.

Conclusion

This research proves that mosses are good bioindicators of airpollution with heavy metals. In this investigation, due to thelocation of Bitola city and the proximity of the thermoelectricpower plant (REK) based on moss biomonitoring technique,several anthropogenic elements were detected. Arsenic wasused as marker of pollution in this region, especially nearREK. This fact confirms the influence of the dust from thethermoelectric power plant to the air pollution in the largerregion of the city of Bitola. In this research, two geogenic andtwo anthropogenic geochemical associations were found.Cadmium and potassium were leading anthropogenic ele-ments from Factor 2 and Factor 3 appropriate. The highestconcentration of Cd was found in the city center considering

urban and traffic activities. A high concentration of K wasmost probable a consequence of the use of fertilizers forPelagonia Valley. From here can be seen that the only elementwhich causes concern was arsenic. It may cause many healthdisorders and it is a serious threat to the citizens of Bitola.

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