Spatial variation of eco-physiological parameters in ...

Spatial variation of eco-physiological parameters in the lichen Pseudevernia furfuracea transplanted in an area surrounding a cement plant (S Italy) Lucio Lucadamo, Anna Corapi, Stefano Loppi, Luca Paoli & Luana Gallo

Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Volume 187 Number 8 Environ Monit Assess (2015) 187:1-21 DOI 10.1007/s10661-015-4712-2

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Author's personal copy Environ Monit Assess (2015) 187:500 DOI 10.1007/s10661-015-4712-2

Spatial variation of eco-physiological parameters in the lichen Pseudevernia furfuracea transplanted in an area surrounding a cement plant (S Italy) Lucio Lucadamo & Anna Corapi & Stefano Loppi & Luca Paoli & Luana Gallo

Received: 6 November 2014 / Accepted: 29 June 2015 # Springer International Publishing Switzerland 2015

Abstract Thalli of the lichen Pseudevernia furfuracea were transplanted for 3 months (November 2010–January 2011) at 61 monitoring sites around a cement plant near Castrovillari (Calabria, southern Italy). NH3, NOx and SO2 concentrations were monitored monthly in a subarea of 10 sites (SA10) where the cement plant was located. At the end of the exposure period, the integrity of cell membranes; membrane lipid peroxidation (thiobarbituric acid reactive substances, TBARS level); vitality (cell respiration); chlorophyll a; chlorophyll b; carotenoids; phaeophytization quotient; photosynthetic efficiency and thalli concentrations of Al, Ca, Mg, Vand Fe were measured. NOx concentrations correlated with the site distance from the cement plant while NH3 concentrations correlated with lichen vitality within SA10. For the monitoring area as a whole, only Fe and Mg concentrations correlated with membrane lipid peroxidation, while TBARS levels showed a significant increase and chlorophyll a, chlorophyll b and carotenoids a significant decrease with respect to the lichen origin area. Multivariate analysis (detrended correspondence analysis, cluster analysis and multi-response permutation procedure) of the eco-physiological parameters × monitoring sites data set resulted in four L. Lucadamo (*) : A. Corapi : L. Gallo DiBEST (Dipartimento di Biologia, Ecologia e Scienze della Terra), Università della Calabria, 87036Arcavacata di Rende, Cosenza, Italy e-mail: [email protected] S. Loppi : L. Paoli Dipartimento di Scienze della Vita, Università di Siena, 53100 Siena, Italy

clusters termed C1, C2, C3 and C4. The ecophysiological parameters were compared among the four clusters and lichen origin area by one-way ANOVA. An index of environmental favourableness (IEF) to lichens was calculated to evaluate the spatial recovery of impaired values of TBARS, chlorophyll a, chlorophyll b, xanthophylls + carotenoids and phaeophytization quotient. The results indicate that there is no clear spatial trend in mycobiont impairment even though the IEF values suggest a higher number of sites with low levels of membrane lipid peroxidation in the 2-–3-km distance band from the cement plant (the outermost) than in the two other distance bands (0–1 and 1–2 km). The photobiont seems to be damaged mainly in the inner distance band of the study area as suggested by the gradual but significant recovery trend of pigment levels and phaeophytization quotient from the inner distance band to the outer one (as shown by the IEF values). Conversion of chlorophyll to phaeophytin probably is not the only process affecting pigment levels. Keywords Cement plant . Pseudevernia furfuracea . Lichen transplants . Eco-physiological parameters . Internal control . Spatial trends

Introduction Biological monitoring is a very effective way of evaluating how much environmental conditions satisfy the ecological demands of species inhabiting terrestrial and aquatic habitats. There are three basic approaches to the

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use of living beings to detect environmental impairment (Walker et al. 2006): studying the effect of contamination on biodiversity, measuring the accumulation of contaminants in biomonitors and bioaccumulators, and studying the cytological and biochemical responses to contaminants. The last approach can be used to roughly quantify the energy flux devoted to self-sustainment and, as a consequence, the amounts of energy subtracted from biomass increase and reproduction (Odum and Barrett 2005). Lichens are well suited for all three types of biological monitoring (Herzig and Urech 1991; Asta et al. 2002; Conti and Cecchetti 2001; Bačkor and Loppi 2009). Due to the dual nature of lichens, most biomarkers are used to separately evaluate the effects of environmental alteration on the mycobiont and photobiont. Biomarkers can be effective in tracing the exposure to some categories of stressors (Cuny et al. 2002), but most of them are physiological parameters whose positive or negative variation can be apportioned between exposure to contaminants and changes in ecological limiting factors. To improve their effectiveness in evaluating air quality, their response to anthropogenic substances has been tested in laboratory trials (Bačkor and Fahselt 2005; Munzi et al. 2009) and in field monitoring. The transplantation technique has been adopted to improve the ability of lichens to detect perturbations in field studies on account of the high number of experimental designs it makes possible, often involving comparison of eco-physiological values measured in the monitoring area with those recorded in the origin area of the transplants (Frati et al. 2006; Tretiach et al. 2007; Gonzalez et al. 1998). However, due to the well-known effect of temperature and humidity on lichen physiology (Nash 2008; Marini et al. 2011), the climatic difference between the lichen origin area and the monitoring area can be a confounding factor that compromises the isolation of eco-physiological variation caused by atmospheric contaminants. Many of the components of the cement work atmospheric emissions are harmful for higher plants and lichens. The gases NOx and SO2 are known to negatively affect both the physiological status of lichens (Egger et al. 1994; Das et al. 2011) and the species composition of communities (Davies et al. 2007; Hawksworth and Rose 1970). Ammonia can cause changes in epiphytic communities (Wolseley et al. 2006) since it is detrimental to some eco-physiological parameters (Paoli et al. 2014). Dusts emitted from kilns are highly noxious due to their alkaline nature and the associated metals. Several studies have shown how dust

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deposition can modify the species representativeness on tree trunks (Cieslinski and Jaworska 1986; Gilbert 1976; Wittman and Turk 1988) or depress physiological parameters of both lichens and higher plants (Kumar and Thambavani 2012; Giri et al. 2013). The aims of the present study, using an experimental design based on a high density of monitoring sites, were as follows: a) To identify a group of sites to be used as a better local control (inside the monitoring area) than the lichen origin area (LOA) or other non-polluted sites outside and quite far from the study area (far enough to avoid autocorrelation due to contamination promoted by the anthropogenic source investigated) which, albeit relatively similar, differ in micro/ mesoclimatic conditions from those of the monitoring area b) To detect spatial trends in eco-physiological parameters of thalli of the lichen Pseudevernia furfuracea transplanted in a monitoring area around a cement plant and nearby industrial zone c) To compare the values of eco-physiological parameters measured in the transplants with those recorded in thalli in the lichen origin area d) To search for correlations between the monitored pollutants and eco-physiological parameters

Materials and methods The monitoring area, near the town of Castrovillari (northern part of Calabria, southern Italy), has a total surface area of 30 km2. The main potential anthropogenic sources of pollutants in the area (Fig. 1) are the Italcementi cement plant; a small industrial zone near the cement plant (including two glass works, one furniture showroom, one mechanic’s workshop, one car accessory shop, one bookbinding workshop, one small typography and many warehouses); two quarries; two small illegal solid waste dumps; the A3 motorway (Salerno-Reggio Calabria) and a broad agricultural area with vineyards, olive groves and cereal fields. Since lichens are virtually absent in the area, thalli of P. furfuracea were transplanted from a site (La Fossiata, 1600 m above sea level (a.s.l.)) in Sila National Park to 61 monitoring sites (Fig. 1) and left there for 3 months (November 2010–January 2011). Lichen transplantation

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Fig. 1 Map of the study area showing the spatial distribution of monitoring sites and main local anthropogenic sources of atmospheric pollutants

was not carried out in the LOA. There were two reasons for the choice of the transplantation period: (a) in this trimester, the differences in climatic conditions between La Fossiata and Castrovillari are less than in other periods of the year (ARPACAL 2011) so that the physiological stress on the lichen due to transplantation can be reduced as much as possible and (b) thermal inversions, which impede pollutant dispersion, are more frequent in winter; in addition, the location of the study area in a valley favours the formation of inversions, increasing the potential detrimental effect of pollutants on the biota. Only five sites were located in the NE sector since the presence of the Pollino massif impeded access to that part of the monitoring area. At each site, three twigs bearing whole thalli of P. furfuracea were attached by plastic strings to tree branches or wooden poles (at a height of ca. 2.5 m above ground). The minimum and maximum altitudes were 300 and 690 m a.s.l., respectively. The mean temperatures in the LOA and monitoring area during the trimester were 10 and 5.4 °C, respectively. The mean humidity values were similar (Castrovillari 79 %, La Fossiata 83 %) although La Fossiata had more rainfall than Castrovillari during the study period (respectively 368 and 225 mm). During the 3 months, atmospheric concentrations of NH3, NOx and SO2 were monitored by means of passive diffusion samplers (National Research Council patent No. RM-98–000584) attached to tree branches near the exposed thalli in a subarea (2.89 km2) including the cement plant, a section of the A3 motorway, the

conveyor belt carrying materials extracted from the main quarry and 10 of the 61 sites (the acronym used to indicate this area is SA10). Gases were adsorbed by a filter that was replaced after an exposure period of 1 month. At the end of the 3 months, samples were retrieved and taken to the laboratory for evaluation of the following eco-physiological parameters: integrity of cell membranes; membrane lipid peroxidation; viability assay; pigments and phaeophytization quotient; photosynthetic efficiency and the concentrations of Ca, Mg, Al, Fe and V. These elements were selected because they are among those with the highest coefficient of emissions from cement plants (EPA, 2015). For the analysis, three subsamples were taken from each of three different lichen thalli up to the amounts required by each experimental protocol. The same number of subsamples was used for lichen origin area thalli before exposure. Integrity of cell membranes (general indicator of stress) The variation in electrical conductivity, measured by placing a piece of lichen thallus in deionized water, is a simple test to check the integrity of the plasma membrane enclosing lichen cells (Simon 1974; Marques et al. 2005). About 100 mg of lichen material was rinsed three times for 5 s in deionized water to remove particles deposited on the lichen surface (Garty et al. 1993) until stable conductivity values were obtained. The conductivity of 50 mL of deionized water was then measured (Eutech Instruments Thermo Fischer Scientific) before

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and after thalli were soaked and shaken for 1 h. The lichens were subsequently boiled at 100 °C for 10 min to rupture the cell membranes and, after cooling, the conductivity was measured again. The integrity of cell membranes was estimated by percentage electrical conductivity (EC%), expressed as a percentage ratio between the conductivity after 1 h of soaking and 10 min of boiling, after accounting for the initial conductivity of the deionized water. Membrane lipid peroxidation (oxidative stress) Membrane lipid peroxidation was estimated using the thiobarbituric acid reactive substances (TBARS) assay (Huang et al. 2004). About 50 mg of lichen thalli was rinsed in distilled water and then homogenized using 2.5 mL of 0.1 % trichloroacetic acid (TCA). A 1.5-mL amount of the homogenate was put into Eppendorf tubes and centrifuged at 12,000×g for 20 min. In glass tubes, 0.5 mL of the supernatant was collected and added to 1.5 mL of 0.6 % thiobarbituric acid in 10 % TCA. The tubes were placed in an oven at 95 °C for 30 min and then cooled in an ice bath, after which the solutions were centrifuged at 12,000×g for 10 min. The absorbance of the supernatant was measured at 532 nm (PerkinElmer Lambda 4) and corrected for non-specific absorption at 600 nm. The concentration of TBARS was calculated using the extinction coefficient for the thiobarbituric acid-malondialdehyde (TBA-MDA, one of the main reactive substances) complex (155 mM/cm) and the result was expressed as μmol/g (dry weight). Lichen viability (dehydrogenase activity) (toxic effect) An easily measured indication of metabolic activity or viability in lichens is the ability to reduce 2,3,5-triphenyltetrazolium chloride (TTC) to triphenyl formazan (TPF) (Bačkor and Fahselt 2005). About 15 mg of lichen thalli was added to 2 mL of 0.6 % TTC in 0.05 M phosphate buffer (pH 6.8) containing 0.005 % Triton X-100 in sterilized 15-mL conical centrifuge tubes. The closed conical tubes were incubated in the dark at 25 °C for 20 h to permit TPF production. Samples were then rinsed in distilled water until bubbles of Triton X-100 were produced. Water-insoluble formazan was extracted from all tested samples with 2 mL of dimethyl sulphoxide at 65 °C for 2 h and mixed with a vortex mixer with glass beads for 2 min. Four millilitres of n-hexane was added to each sample and the

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samples were mixed with a vortex mixer for 1 min, hand-shaken and centrifuged at 400×g for 15 min. The absorbance of the hexane fraction was measured at 492 nm (Lin et al. 2001) with a PerkinElmer spectrophotometer (Lambda 4) to quantify TPF production. Pigments and phaeophytization quotient (toxic effect) Lichenic substances were previously removed to avoid phaeophytization processes by washing the thalli with 3 mL of CaCO3-saturated acetone six times for 5 min each time. Complete removal of lichenic substances was tested by adding some drops of KOH to the washing acetone and checking for yellow colour. Samples of thalli (60 mg) were air dried in a desiccator for 24 h. Pigment extraction was performed under weak green light. Thalli were added to 50-mL glass tubes containing 3 mg of polyvinylpolypyrrolidone (PVPP) and 3 mL of dimethyl sulphoxide and homogenized in an ULTRATURRAX for 1.5 min. The homogenate was transferred to new glass tubes to which were added 4 mL of dimethyl sulphoxide (2 used to wash the ULTRA-TURR AX head and 2 to remove residues from the glass tube walls) and stored in the dark at room temperature for 18 h. At the end of the extraction, the suspension was centrifuged at 4000 rpm for 10 min, and 3 mL of dimethyl sulphoxide was added to the precipitate which was stored in the dark at room temperature for 6 h. The two volumes of dimethyl sulphoxide were mixed and centrifuged, and the absorbance was measured at 665, 649 and 480 nm with a PerkinElmer spectrophotometer (Lambda 4). Concentrations of chlorophyll a, chlorophyll b and carotenoids were calculated according to the Wellburn equations (1994). The phaeophytization quotient was expressed as the ratio of the absorbances at 435 and 415 nm (and indicated as OD435/OD415) (Ronen and Galun 1984). Photosynthetic efficiency (general indicator of stress) The potential quantum yield of electron transfer through photosystem 2 was expressed as the ratio of Fv/Fm. These parameters were determined according to the following protocol. Thalli were hydrated and adapted at room temperature for 1 h. Chlorophyll fluorescence was measured with a Handy PEA fluorimeter (Hansatech Instruments). Dark adaptation was achieved via lightweight plastic leafclips whose shutter blades were closed to isolate the sample from light for

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10 min. Fluorescence was measured twice, immediately after the shutter blades were opened (Fo) and then following exposure to a saturating light flash (2400 μmol/s/m 2 ) (Fm). Fv was calculated by subtracting Fo from Fm (Jensen 2002). Element concentrations in transplanted thalli Element concentrations were measured by mineralizing 500 mg (d.w.) of homogenized powder of thalli in Teflon vessels in a microwave oven (Milestone Ethos 900) and adding a mixture of 6 mL of 70 % HNO3, 1 mL of 30 % H2O2 and 0.2 mL of 60 % HF. After digestion, the samples were diluted in distilled water and analysed by ICP-MS (Agilent 7005). The accuracy and precision of the analyses of element concentrations in both exposed and LOA thalli were checked by determinations (six replicates) of the certified reference material BCR-482 (lichen P. furfuracea). Atmospheric concentrations of gases Nitrogen oxides NOx are adsorbed on the surface of a filter (part of the passive sampler) and oxidized to NO2. At the end of the exposure period (1 month), the filter was removed from the sampler and, once in the laboratory, shaken for 1 h in 5 mL of a solution of 8 mM Na2CO3 and 1 mM NaHCO3. Thereafter, a volume of 50 μL was filtered (IC Millex—LG 0.2 μm) and injected into an ionic chromatographer (Dionex ICS 1000) using an anionic exchange column (AS12A) and as eluent the same solution used to detach nitrite from the filter. The NOx were determined as mg/L of nitrite. Since the extraction volume is known, the total amount of nitrite can be calculated and then converted into μgNOx/m3 by means of the following equation: NOx ¼ 2:89  103

W T

where W Total amount of nitrite captured by the filter during the exposure period T Time of exposure (1 month)

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Sulphur dioxide Sulphur dioxide is captured on the surface of a filter (part of the passive sampler) where it is nonquantitatively oxidized to sulphate by ozone. After 1 month of exposure, the filter was removed from the sampler and, once in the laboratory, shaken for 1 h in 5 mL of a solution of 8 mM Na2CO3 and 1 mM NaHCO3 with added hydrogen peroxide to transform all sulphur from oxidation state IV to oxidation state VI; 50 μL was then filtered (IC Millex—LG 0.2 μm) and injected into an ionic chromatographer (Dionex ICS 1000) using an anionic exchange column (AS12A) and as eluent the same solution using to detach sulphate from the filter. SO2 was determined as mg/L of SO43−. Since the extraction volume is known, the total amount of sulphate can be calculated and then converted into μgSO2/m3 by means of the following equation: SO2 ¼ 1:32  103

W T

where W Total amount of sulphate captured by the filter during the exposure period T Time of exposure (1 month) Ammonia Ammonia, once hydrated, becomes ammonium ion that is adsorbed on the surface of the filter of the passive sampler. At the end of the exposure period, the filter was removed from the sampler and taken to the laboratory where it was stored at −10 °C because ammonium is easily biodegradable. The filter was then dipped in 5 mL of a solution of 8 mM Na2CO3 and 1 mM NaHCO3 and shaken for 1 h;50 μL was then filtered (IC Millex—LG 0.2 μm) and injected into an ionic chromatographer (Dionex ICS 1000) and eluted using a cationic exchange column (CS12) and as eluent a solution of 20 mM methanesulphonic acid. Since the extraction volume is known, the total amount of sulphate can be calculated and then converted into μgNH3/m3 by means of the following equation: NH3 ¼ 9:06  103 where

W T

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W Total amount of ammonium captured by the filter during the exposure period T Time of exposure (1 month) Statistical analyses Multivariate and univariate analyses were performed on the eco-physiological data and the concentrations of the measured contaminants. One-way parametric analysis of variance with a post hoc multiple comparison test (Tukey) was performed to test the null hypotheses of no differences in NH3, NOx and SO2 concentrations between the 3-month means of the 10 sites in SA10. Correlation analyses (Pearson) were performed to detect significant associations between (a) the variation of the 3-month mean concentrations of NH3, NOx and SO2 monitored at each site of SA10 and the distance of each site from the cement plant within SA10; (b) the variation of the 3-month mean concentrations of NH3, NOx and SO2 monitored at each site of SA10 and the eco-physiological parameters within SA10; (c) the variation of transplanted thalli element concentrations and the distance of each site from the cement plant in the whole study area; (d) the variation of transplanted thalli element concentrations and eco-physiological parameters in the whole study area; (e) the variation of ecophysiological parameters and the distance of each site from the cement plant in the whole study area. Detrended correspondence analysis (DCA) and cluster analysis were performed on the eco-physiological parameters × monitoring sites data set. DCA—by setting the number of segments for detrending at 26 (default value) and, due to the weakening of the relation between eigenvalue and variation associated with the axes, by calculating the coefficient of determination of the correlation between the relative Euclidean distances in real space and the Euclidean distances in ordination space. Cluster analysis—using the Euclidean distance as the distance measure and the Ward method as the rule for cluster formation. Multi-response permutation procedure (MRPP)— using the Sorensen distance as the distance measure. It tested the null hypothesis of no differences between the groups detected by matching the results of the ordination and classification techniques.

One-way parametric analysis of variance with a post hoc multiple comparison test (Tukey) was performed to test the null hypotheses of no differences in ecophysiological parameters between (a) the lichen origin area (external control) and the whole monitoring area and (b) the lichen origin area and clusters of sites detected by matching the results of the ordination and classification techniques. Three of the clusters obtained from the multivariate analyses (C1, C2, C3) showed impairment of some of the eco-physiological parameters (TBARS, chlorophyll a, chlorophyll b, xanthophylls + carotenoids and phaeophytization quotient) while the sites of the last one (C4) showed healthy values of all the ecophysiological parameters. As the index of environmental favourableness (IEF) to lichens, we calculated the ratio between the number of sites taking part in cluster C4 and the number of sites taking part in clusters C1, C2 and C3 for all the above-mentioned parameters (IEF1) except TBARS for which the ratio was calculated by summing C1 sites and C4 sites (IEF2). In fact, for TBARS, the only statistically significant differences were detected between C2, C3 and LOA. C1 and C4 did not differ from each other or from both of the other two clusters and LOA; hence, they were considered an Bintermediate state of recovery/alteration^ and their sum was used as the numerator of the ratio. Two-way parametric analysis of variance was performed to test the null hypothesis of no effect of geographical sides and distance bands (0–1, 1–2, 2–3 km) from the cement plant on the two kinds of IEF. Analysis of wind frequencies was made possible by data from the Environmental Protection Agency of Calabria (ARPACAL). The multivariate and univariate analyses were performed with PC-ORD4 and Minitab 13.2 statistical packages, respectively.

Results During the exposure period, the winds had the following frequencies: NW = 38 %, SW = 27 %, NE = 11 % and SE = 24 % (ARPACAL 2011). This suggests that the most frequent wind direction is well covered by the sampling locations (SE sector, 43 % of all monitoring sites). Table 1 shows the monthly concentrations as well as the 3-month and yearly means of NH3, NOx and SO2

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Table 1 Monthly mean concentrations (μg/m3) of NH3, NOx and SO2 monitored in a subarea of 2.89 km2 (SA10) located in the middlenorth part of the study area and including the cement plant 0B

0C

1C

1D

2A

2C

3A

3D

4B

4D

NH3+ Nov. 10

4.65

7.73

5.51

4.02

3.77

8.38

11.43

8.86

8.10

7.59 11.06

Dec. 10

5.73

7.10

7.03

5.17

7.03

7.49

11.76

6.47

11.80

Jan. 11

6.14

6.47

5.86

4.28

5.23

6.20

7.71

5.73

8.06

9.97

Mean

5.51

7.10

6.13

4.49

5.34

7.36

10.30

7.02

9.32

9.54

Yearly mean C.V.%

5.86

5.15

4.79

4.17

3.90

5.06

6.88

5.63

5.85

6.08

70.30

41.75

29.00

25.00

34.04

35.59

38.19

29.91

46.91

50.48

NOx Nov. 10

22.22

14.73

21.08

16.06

7.76

19.75

13.11

17.03

24.03

17.97

Dec. 10

26.00

30.27

32.94

24.22

5.28

26.91

23.38

26.07

21.51

21.11

Jan. 11

24.11

21.75

26.69

12.49

6.18

12.54

9.04

18.20

17.21

17.87

Mean

24.11

22.25

26.90

17.59

6.41

19.73

15.18

20.43

20.92

18.98

Yearly mean

23.55

20.38

34.76

24.13

9.20

18.39

19.95

23.54

21.51

20.63

C.V.%

22.34

25.02

32.68

119.31

91.20

21.91

66.32

51.95

23.94

44.30

SO2 Nov. 10

3.64

2.68

3.94

5.62

4.52

2.00

5.66

6.20

3.53

4.33

Dec. 10

2.10

8.64

4.78

4.78

3.02

2.17

3.30

3.69

3.59

3.77

Jan. 11

2.53

8.56

7.23

5.16

4.82

3.38

3.63

5.76

4.39

3.18

Mean

2.76

6.63

5.31

5.19

4.12

2.52

4.19

5.21

3.84

3.76

Yearly mean C.V.%

3.45

6.06

6.78

5.21

4.36

3.44

5.61

5.54

5.11

4.95

42.45

46.77

57.37

48.49

48.66

46.29

87.34

40.75

43.89

62.47

measured in the subarea of 2.89 km2 (SA10) located in the middle-north part of the experimental area. The ammonia (3-month mean) minima detected at sites 0B, 1D and 2A (where also the yearly minimum mean was detected) were significantly lower (p < 0.05) than the value at site 3A (the highest concentration). Other maxima, although not significantly different from the minima, were detected in the SE sector (4D and 4B). The NOx monthly concentration at site 2A was significantly lower (p < 0.05) than the values at sites 0B, 0C and 1C; site 1C was located at a petrol station (along the A3 motorway) while the other two are the sites nearest to the cement plant. Although the maxima of SO2 partially overlapped with those of NOx (0C and 1C), no significant differences were detected between them and the minimum 3-month mean value (2C). Variation of the NOx concentration showed a significant association with variation of the distance of each site in SA10 from the cement plant (r = −0.758, p = 0.011). NH3 concentrations significantly correlated (r = −0.647, p = 0.043) with lichen viability (A492) within SA10.

The accuracy of the analytical determinations of element concentrations in exposed thalli was 9 % and the precision 5.62 %. Table 2 shows the variation of element concentrations in exposed thalli (μg/g d.w.) according to geographical sectors and distance bands (0–1, 1–2, 2– 3 km) from the cement plant. Al, Fe and V showed the highest number of sites with concentrations denoting atmospheric enrichment within the first 2 km from the cement plan. Moreover, 50 % of all the contaminated sites within the 0-–2-km zone were located in the SE sector, in agreement with the dominant wind frequency. Ca and Mg concentrations in thalli showed the same spatial pattern as the other elements except for the distribution within the 0-–2-km zone where the number of sites with atmospheric contamination was evenly distributed among the NW, SW and SE sectors. There was no correlation between the element concentrations in transplants and the distance of each site from the cement plant in the whole monitoring area. The only significant correlations between elements and eco-

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physiological parameters were those between the Fe and Mg concentrations and the TBARS levels (respectively: r = 0.246, p = 0.046; r = 0.311, p = 0.015). Table 3 reports the values of the eco-physiological parameters measured in the lichen origin area (LOA) before exposure (external control) and at the 61 monitoring sites at the end of the exposure period. TBARS and pigment concentrations (chlorophyll a, chlorophyll b, xanthophylls + carotenoids) calculated for all 61 monitoring sites showed respectively the most significant increase and decrease compared with the LOA (Table 4). Photosynthetic efficiency and integrity of cell membranes (EC%) were 15 and 45 % higher than in the LOA while absorbance at 492 (lichen viability) decreased by 22 %, although none of these variations was statistically significant. The phaeophytization quotient was virtually unchanged. Figures 2 and 3 show respectively the DCA ordination diagram and the cluster analysis dendrogram. Most of the variation was associated with axis 1 of the DCA diagram; in fact, the coefficient of determination of the first axis for the correlation between the ordination (Euclidean) distances and the (relative Euclidean) distances in the original n-dimensional space was 0.97. Analysis of the scores of the sites indicated four main clusters termed C1, C2, C3 and C4 (Fig. 2). The percent chaining of the cluster analysis was very low (1.83), implying a high level of similarity among the components of the different groups. The clustering of sites mostly overlapped with that of the DCA. At the highest distances of similarity, 27 of the 33 sites in cluster C4 in the DCA segregated from the remaining sites which further separated into the same clusters resulting from the DCA except for a small group of six sites (originating from cluster 4). In the light of the score analysis, the solution proposed by the DCA was considered better than that of the cluster analysis and was accepted as the Bgrouping^ criterion for the MRPP analysis of the ecophysiological parameters × sites data set. The value of the MRPP test statistic T was −12.890327 with a p value of 0.0000001 and the chance-corrected within-group agreement A = 0.29622641, a result that strongly suggested that the significant differences in the ecophysiological parameters among clusters were independent of the sample sizes and not due to a stochastic factor. When the mean values of the eco-physiological parameters were calculated for the clusters resulting from the multivariate analysis of the eco-physiological

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parameters × sites data set, the TBARS levels of clusters C2 and C3 still showed a significant (p < 0.05) increase with respect to the LOA (Table 2) while clusters C1 and C4 did not differ from either clusters C2 and C3 or the LOA. The chlorophyll a, chlorophyll b and xanthophylls + carotenoids concentrations of cluster C4 and the LOA were significantly higher than those measured in thalli of the other clusters (C1, C2, C3). The pigment levels of cluster C3 were significantly different from those of cluster C2 (30 % increase on average). The phaeophytization quotient of cluster C4 was significantly higher than that of the other clusters, while the values of the ratio of absorbances at 435 and 415 nm (OD435/OD415) of the LOA, cluster C2 and cluster C3 differed significantly (p < 0.05) from that of cluster C1. The Fv/Fm value (photosynthetic efficiency) of cluster C2 was significantly lower than that of the others. No significant correlation was found between the distance of each site from the cement plant and the variation of the eco-physiological parameters within the whole study area. However, the four clusters showed different distances from the cement plant (medians: C1 = 1.47 km, C2 = 1.47 km, C3 = 1.63 km, C4 = 2.12 km) with cluster C4 segregating more from the other clusters, despite the apparent interspersion of the sites of the four clusters between the inner and outer parts of the experimental area. The calculation of IEF and its statistical testing was needed to support the hypothesis of an association between the degree of transplant environmental recovery (how much the number of sites with lichens in healthy physiological state increases) and the distance bands from the cement plant and geographical sectors. The values of IEF1 and IEF2 are shown in Tables 5 and 6, respectively. Two-way ANOVA supported the association with distance bands for IEF1 (F = 7.01, p = 0.035) but not for IEF2 (F = 2.15, p = 0.212), with the Tukey test suggesting that the result was ascribable to a difference in IEF1 values between distance bands 0–1 and 2–3 km (IEF1 respectively 0.75 and 2.33), while the intermediate band (1–2 km) acts as a Btransition zone^ (IEF1 1.15) due to the lack of significant differences from both the other two IEF1 values. The lack of significance of an association between IEF2 and the distance bands is ascribable to the smaller difference (compared with IEF1) between the 0-–1- and 2-– 3-km bands (from −69 to −47 %) due to the summing of clusters C1 and C4. Although the geographical sides did not display any significant effect on variation of IEF1 (or IEF2), the lowest value of IEF1 (0.3) was detected in the

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Table 2 Variation of element concentrations in exposed thalli (μg/g d.w.) according to the geographical sectors and distance bands from the cement plant Geographical sectors

Distance bands (km)

Site codes

Mg

Al

Ca

V

Fe

SE

0–1

0B

SE

0–1

3C

420

691.93

26690

2.83

1440.43

1290

1781.21

60620

6.78

2825.81

SE

0–1

3D

1190

1256.33

46980

5.68

2102.64

SE

0–1

5A

940

912.52

39100

4.50

1737.20

SE

1–2

4A

1810

1774.32

68190

7.37

4033.19

SE

1–2

4D

780

891.68

35530

4.55

1844.49

SE

1–2

1

1450

1861.75

63910

3.76

2819.43

SE

1–2

3B

1120

1276.84

47530

4.17

2072.36

SE

1–2

4C

960

1033.83

33510

4.99

2237.16

SE

1–2

5B

1500

1317.99

52880

6.10

2503.17

SE

1–2

4B

1210

1718.72

52660

7.80

2877.62

SE

1–2

15A

1270

1134.24

30520

5.16

2262.44

SE

2–3

3

960

857.06

32970

2.31

1250.99

SE

2–3

5

1090

1239.88

56170

4.47

2029.15

SE

2–3

14

1080

469.33

30920

3.84

1278.33

SE

2–3

1D

1110

1329.52

33040

3.47

2233.39

SE

2–3

2

1130

1185.71

39900

2.83

1803.49

SE

2–3

4

1580

1582.68

46240

5.08

2729.44

SE

2–3

6

1170

1472.79

54960

5.07

2408.27

SE

2–3

8

1310

1376.18

48190

5.58

2326.42

SE

2–3

11

1380

1134.26

35380

4.36

2187.38

SE

2–3

15

1040

1138.66

41500

4.74

2124.85

SE

2–3

16

1080

1270.79

43240

5.11

2177.38

SE

2–3

16A

1100

1421.57

39340

5.12

2287.51

SE

2–3

17.00

1150

935.49

37350

4.72

2064.36

NE

0–1

1B

820

825.47

27150

2.10

1268.87

NE

0–1

2C

670

910.54

26930

2.95

1453.27

NE

1–2

2B

1000

1198.59

36440

3.13

1831.18

NE

1–2

2A

710

933.85

25310

2.23

1437.09

NE

1–2

3A

1050

1278.74

38180

3.78

2236.98

NW

0–1

8B

1190

1407.44

42730

5.93

2430.86

NW

0–1

7A

1250

1171.53

38550

5.45

2004.21

NW

0–1

1C

750

805.55

28280

2.31

1364.23

NW

1–2

9

1390

1114.19

37730

4.86

2181.33

NW

1–2

27

1220

1197.58

35720

4.84

2192.78

NW

1–2

8D

1030

1926.44

48190

5.77

2973.32

NW

1–2

1D

1110

1329.52

33040

3.47

2233.39

NW

1–2

7C

1330

1460.29

44660

6.81

2472.19

NW

1–2

8A

1100

1136.42

33910

4.66

1959.81

NW

1–2

8C

1340

1421.95

40810

5.57

2415.87

NW

2–3

25

1120

1082.18

37460

5.69

2149.26

NW

2–3

26

700

722.05

28330

5.83

2191.65

NW

2–3

2

1100

1101.03

38720

5.30

1949.42

NW

2–3

23

1200

1378.75

39750

5.68

2397.32

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Table 2 (continued) Geographical sectors

Distance bands (km)

Site codes

Mg

Al

Ca

V

NW

2–3

24

1060

1045.91

32930

4.59

1750.36

SW

0–1

0C

1180

1360.53

40670

4.84

2139.37

SW

0–1

7B

1510

1090.11

43730

4.77

1781.86

SW

0–1

5D

1210

1475.46

52710

8.29

2657.24

SW

1–2

6B

1420

1069.10

47960

5.41

2354.40

SW

1–2

6C

1170

1005.33

33210

5.16

2385.26

SW

1–2

6D

1240

908.00

38490

4.69

2180.14

SW

1–2

6A

1410

1103.55

45180

6.05

2887.99

SW

1–2

7C

1330

1460.29

44660

6.81

2472.19

SW

2–3

21

1150

1139.52

41560

5.54

2066.73

SW

2–3

29

1030

1446.32

33390

4.94

2377.96

SW

2–3

12

1320

1220.93

40190

5.05

2203.72

SW

2–3

13

1150

818.49

40420

4.55

1823.43

SW

2–3

18

1500

1916.93

60860

7.24

3287.42

SW

2–3

19

800

574.03

35430

3.99

1720.80

SW

2–3

20

1310

1295.68

35530

5.38

2436.98

SW

2–3

28

1190

907.57

38510

4.52

1935.25

1-–2-km distance band of the SE sector, i.e. the geographical side with the highest wind input (38 % from the NW sector). This suggests that here the deposition of contaminants possibly emitted from the cement plant would take place at a greater distance than in the geographical sectors (NW and SW) with the least frequent wind input (respectively 11 and 24 %), where the lowest value of IEF (0.5) was detected in the 0—1-km distance band probably due to the less effective dispersion by winds. The NE sector also showed the lowest IEF1 value in the 0-–1- km distance band, although the value (1.0) was markedly higher than the others.

Discussion The subarea located in the middle-north part of the study area (SA10) was selected for the monitoring of gases (as well as element levels) due to the high density of anthropogenic sources of atmospheric pollutants (cement plant, conveyor belt, A3 motorway). Although the results cannot be extrapolated to the whole study area, they provide information about how critical the gas contamination can be there and if it correlates with the variation of eco-physiological parameters.

Fe

The ammonia values were above the critical levels for lichens (Cape et al. 2009; Pinho et al. 2012) at all 10 sites of the subarea. The lowest values were measured near the motorway (1D), the cement plant (0B) and at site 2A whose altitude (656 m a.s.l.) is about 200 m above the average elevation of the other nine sites, which could explain its lower contamination. Motor vehicle exhausts are a well-known source of reduced nitrogen (EPA 2003) due to catalytic converters, and the minimum measured near the motorway may suggest a low level of traffic. There was no correlation of spatial variation of ammonia concentrations with the distance of each site from the cement plant within SA10. On the other hand, the highest levels were measured at sites 3A (significantly different from the minima, p < 0.05), 4D and 4B, i.e. at locations downwind of the cement plant and consistent with the dominant winds coming from NW. Ammonia emissions from the cement plant stack are associated with the pyrolysis of nitrogenous compounds in fossil fuels and raw materials, and the minimum measured close to the Italcementi plant may be due to the length of the deposition trajectories of ammonia, as indicated by the distances (around 1 km) of the sites where maxima were detected (sites 3A, 4D and 4B). However, since there is a broad agricultural area in the SE sector, it is very difficult to apportion the

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Table 3 Eco-physiological parameters measured in thalli of Pseudevernia furfuracea in the external control (lichen origin area) and the exposure area at the end of the experimental period (November 2010–January 2011) Sites

Clusters

1B

C1

EC%

MDA

A 492

Fv/Fm

Chlo a

Chlo b

Xan + Car

OD435/OD415

20.40

13.69

1.19

0.66

601.64

162.76

238.49

0.47

21.30

11.82

1.28

0.65

573.39

173.33

299.48

1.02

3

18.80

15.80

0.16

0.77

470.88

171.53

242.22

0.78

4A

24.30

9.30

0.42

0.65

883.95

316.39

434.46

0.84

4D

12.80

10.36

0.46

0.73

773.01

235.95

384.38

0.88

8B

26.10

22.34

0.87

0.65

542.29

162.43

265.73

1.11

8.90

16.65

0.41

0.76

939.93

289.38

451.08

0.88

Mean

18.94

14.28

0.68

0.70

683.58

215.97

330.83

0.85

C2

18.70

18.65

0.86

0.66

529.90

187.76

271.93

1.18

6B

11.40

24.52

0.66

0.71

333.64

138.65

171.41

1.17

7A

16.70

24.86

0.92

0.57

730.52

235.73

381.22

1.21

7B

25.60

36.91

0.33

0.64

474.63

159.50

239.25

1.17

21

12.80

18.59

0.68

0.67

692.90

209.28

328.92

1.17

25

28.50

10.31

0.48

0.63

676.32

212.75

329.84

1.22

27

18.60

34.83

0.53

0.57

555.10

185.91

277.62

1.20

29

22.40

14.57

0.83

0.40

474.13

158.25

252.39

1.18

Mean

19.34

22.91

0.66

0.60

558.39

185.98

281.57

1.19

C3

1.25

2B

9 0C

0B

45.70

18.63

1.38

0.61

817.59

255.74

368.17

1

29.10

44.17

0.26

0.73

1033.73

342.73

482.46

1.25

3B

12.20

10.92

0.58

0.74

993.90

290.15

448.15

1.24

3C

27.50

22.64

0.26

0.76

802.72

250.59

391.96

1.24

4C

21.40

26.25

0.57

0.72

610.15

207.37

325.37

1.24

5

18.20

26.44

0.32

0.53

685.07

195.09

324.24

1.24

5B

21.80

16.82

0.64

0.75

675.68

201.01

310.15

1.24

6C

18.40

21.11

1.66

0.75

900.90

269.74

413.98

1.24

6D

10.60

14.56

0.34

0.68

607.56

177.97

294.16

1.23

8D

11.60

11.34

0.48

0.75

762.23

225.69

356.37

1.23

10

12.60

11.23

0.44

0.68

811.51

233.89

369.92

1.23

14

14.90

13.54

0.74

0.65

868.62

248.60

404.09

1.24

26

20.00

30.95

0.62

0.68

679.49

202.24

323.26

1.25

Mean

20.31

20.66

0.64

0.69

788.40

238.52

370.17

1.24

C4

16.90

16.55

0.72

0.74

1153.38

344.39

543.07

1.34

1D

23.30

20.83

0.99

0.67

1329.26

382.43

569.93

1.32

2

16.90

21.51

0.72

0.56

1012.51

322.93

464.62

1.28

2A

15.60

12.52

1.16

0.67

1192.52

338.03

547.79

1.33

2C

18.50

10.40

1.36

0.78

842.41

269.33

419.45

1.27

3A

23.00

20.22

0.27

0.71

1106.62

333.50

517.01

1.30

3D

25.30

24.18

0.46

0.73

598.11

178.31

284.36

1.31

4

24.60

15.60

0.21

0.68

945.13

276.61

447.30

1.32

4B

19.90

29.33

0.80

0.68

911.85

280.28

442.25

1.27

5A

20.60

12.44

0.67

0.71

1120.93

317.63

507.78

1.35

5D

26.60

22.00

0.40

0.72

1072.60

308.82

515.29

1.34

1C

6

18.50

15.70

0.61

0.65

1096.09

314.35

525.84

1.32

6A

18.80

28.15

1.39

0.67

1265.48

377.60

569.44

1.31

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Table 3 (continued) Sites

Clusters

EC%

MDA

A 492

Fv/Fm

Chlo a

Chlo b

Xan + Car

OD435/OD415

7C

18.40

13.42

0.61

0.75

1112.13

326.43

506.62

1.34

7D

17.30

22.98

0.52

0.65

821.91

259.97

410.05

1.29

8

20.90

23.99

0.90

0.72

1017.10

305.37

466.79

1.28

8A

28.20

26.40

0.43

0.77

855.47

274.25

435.95

1.32

8C

24.30

21.99

1.16

0.75

953.55

302.64

452.10

1.28

11

8.30

12.42

0.74

0.65

1061.30

331.48

492.24

1.30

12

18.00

24.16

1.45

0.64

967.23

277.56

449.76

1.33

13

19.60

19.77

0.81

0.68

912.80

294.91

421.41

1.27

15

22.80

16.40

0.57

0.69

906.01

258.22

414.59

1.31

15A

16.60

41.54

0.69

0.65

1142.87

357.85

526.11

1.30

16

14.90

12.35

0.28

0.73

1124.37

327.09

516.91

1.31

16A

12.90

20.91

0.35

0.68

716.24

214.46

322.24

1.29

17

14.00

18.03

0.40

0.73

1179.04

339.84

520.76

1.29

18

14.10

12.01

1.15

0.65

1532.62

441.13

660.64

1.36

19

14.50

7.90

0.47

0.74

1150.62

335.13

539.04

1.36

20

8.80

15.64

0.94

0.61

1026.76

305.02

484.50

1.34

22

12.60

13.12

1.54

0.67

995.61

321.02

465.75

1.26

23

20.70

9.52

1.00

0.63

988.46

296.36

476.06

1.32

24

17.70

8.31

0.58

0.77

872.97

248.72

395.03

1.29

28

8.90

4.94

0.76

0.77

866.00

252.05

374.32

1.25

18.24

1.31

18.04

0.76

0.69

1025.76

306.47

475.30

Exposure area general mean

Mean

18,91

18.80

0.71

0.68

874.61

265.80

410.91

1.22

External control (lichen origin area)

12,90

6.52

0.92

0.59

363.70

548.13

1.21

1249.5

The second column to the left shows the grouping of sites resulting from the multivariate analysis of the eco-physiological parameters × monitoring sites data set C cluster

contribution of the two sources to the environmental enrichment of NH3. Spatial variation of NH3 within SA10 inversely correlated with the variation of lichen

viability although we do not know if such an association holds in the whole study area. Moreover, comparison of the mean of the LOA with that of all 61 monitoring sites

Fig. 2 Ordination diagram resulting from detrended correspondence analysis of the eco-physiological parameters × monitoring sites data set

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Fig. 3 Dendrogram resulting from cluster analysis of the eco-physiological parameters × monitoring sites data set

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Table 4 IEF1 changes according to the geographical sectors and distance bands from the cement plant of the monitored area 0–1 km

1–2 km

Table 6 Results of one-way ANOVA and post hoc multiple comparison test (Tukey) (LOA = lichen origin area

2–3 km

Comparison between the lichen origin area (LOA) and all sites of the exposure area Parameters

SE

1.0

0.3

2.5

NE

1.0

2.0

−

EC%

2.62

0.111

NW

0.5

1.3

1.5

MDA

6.75

0.012

0.5

1.0

3.0

A492

0.86

0.366

0.75

1.15

2.33

Fv/fm

0.09

0.759

Chlo a

7.77

0.007

Chlo b

6.93

0.011

Xan + Car

5.79

0.019

OD435/OD415

0.00

0.997

SW Mean

(−) not present due to environmental constraints (Pollino massif) in detecting exposure sites

and the mean of cluster 4 with those of the other three clusters never showed significant differences (Table 5), suggesting that the lichen viability was not affected by transplantation in the whole study area. The ammonia levels measured in SA10 are higher than those known to affect chlorophyll (Frati et al. 2011), and NH3 can promote lipid peroxidation in plant cells by increasing the reduction of molecular oxygen in the Mehler reaction (Zhu et al. 2000). We hypothesize that the lack of correlation between NH3 and pigment and TBARS levels within SA10 may be due to the interaction with or additive/negative effect of other non-monitored contaminants (i.e. hydrocarbons, volatile organic compounds, dioxins, furans, HCl, etc.). For NOx, only site 1C (located in a petrol station) showed a yearly mean higher than the threshold for terrestrial plant protection, i.e. 30 μg/m3 (E.U. & C. 2008). During the experimental trimester (autumn–winter period), this value was exceeded only in December at sites 1C and 1D (near the motorway), again supporting a low level of traffic. Interestingly, between May 2010 and April 2011 (when gas emissions were monitored), 21 exceedances were measured of which 67 % at sites close to the A3 motorway (1C, 1D, 3D and 4B) and Table 5 IEF2 changes according to the geographical sectors and distance bands from the cement plant of the monitored area 0–1 km

1–2 km

2–3 km

SE

1.0

1.0

3.3

NE

2.0

3.0

−

NW

2.0

2.5

1.5

SW

0.5

1.0

3.0

1.38

1.88

2.6

Mean

(−) not present due to environmental constraints (Pollino massif) in detecting exposure sites

F

p

Comparison between the lichen origin area (LOA) and the clusters (C) resulting from the multivariate analysis of the element concentrations × monitoring sites data set Parameters

F

p

SSC −

EC%

0.89

0.475

MDA

3.15

0.020

C2, C3 > LOA

A492

0.53

0.713

−

Fv/Fm

3.66

0.010

C2 < C1, C3, C4

Chlo a

21.79

0.000

LOA, C4 > C1, C2, C3 C3 > C2

Chlo b

17.52

0.000

LOA, C4 > C1, C2, C3 C3 > C2

Xan + Car

19.87

0.000

LOA, C4 > C1, C2, C3 C3 > C2

OD435/OD415

59.04

0.000

C4 > C1, C2, C3 C2, C3, LOA > C1

71 % between June and September when the highest tourist flows take place, suggesting that traffic is the main source of NOx in SA10 on a yearly basis. However, NOx spatial variation showed a significant correlation with site distance from the cement plant within SA10 and 21 % of the exceedances were measured at the same sites where NH3 maxima were detected (3A, 4D and 4B). This result, together with the very few high contributions shown by sites near the motorway, supports the hypothesis of a major effect of the cement plant on atmospheric NOx enrichment in SA10 during the experimental trimester. No eco-physiological parameters correlated significantly with NOx concentrations within SA10. Although cement works are known to produce high SO2 emissions (Richards et al. 2008), the concentrations measured in SA10 during both the 3-month experimental period and the yearly gas monitoring never approached the critical levels (annual means) for the protection of

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ecosystems, namely 20 μg/m3 (E.U. & C. 2008), and lichens, namely 10 μg/m3 (UN ECE 1992). There were no significant correlations of spatial variation of SO2 concentrations with either the site distance from the cement plant or the eco-physiological parameters. This was an unexpected result and may be a consequence of adequate abatement systems of SO2 emissions. In a study previously published on the spatial trends in the variation of element levels in exposed thalli in the same whole monitoring area (61 exposure sites) and in the same trimester (Gallo et al. 2014), the concentrations of Fe, Al, V, Ca and Mg showed a different pattern. Aluminium, iron and vanadium contamination was evident in the S and E sectors, and their concentrations were significantly higher (p < 0.05) than those of an internal control group located at the foot of the Pollino massif where the ascending winds effectively remove pollutants. Calcium and magnesium contamination was diffuse in the whole study area due to the location in the NE sector of two quarries, and the concentrations were significantly higher than those of the internal control group. When we considered, in the present work, element concentrations in relation to both distance bands from cement plant and geographical sectors, all of them showed some of the highest values, comparable with concentrations detected in thalli exposed in urban/industrialized sites (Garty et al., 2003; Nimis and Bargagli 1999; Williamson et al. 2008), in the SE sector within the first 2 km from the cement plant. Moreover, in the case of calcium (a very good marker of cement kiln dust; (Chaunsali and Peethamparan 2013)), seven of the eight sites showing impairment of eco-physiological parameters (belonging to clusters 2 and 3) within the 0—2-km band from the cement plant in the SE sector showed Ca concentrations within the first quartile of its ordered series. Calcium-enriched alkaline dust deposition due to cement manufacturing can result in a decrease in photosynthetic pigment content of plants (Farmer 1993) and lichens (i.e. Physcia adscendens) (Zaharopoulou et al. 1993). The reasons for such a decrease could be light deficiency, caused by the shading effect of the deposition of Ca-enriched alkaline dust particles, and nutrient depletion following Ca saturation of ion exchange sites (Farmer 1993; Bates and Farmer 1990). Calcium alkaline dust caused plasmolysis and membrane alteration in the epigeic moss Mnium punctatum (Czaja 1966). Disruption of mitochondrial membrane integrity can result in electron transfer

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decoupling that favours the formation of oxyradicals which then trigger lipid peroxidation (Lieberman and Marks 2012). Mitochondria are an important store of calcium, and elevated concentrations of these elements can inhibit dehydrogenase activities both in animal (Lai et al. 1988) and plant (Gahan et al. 1998) cells. Iron, vanadium and aluminium are known to promote both lipid peroxidation (Halliwell and Gutteridge 2007; Russanov et al. 1994) and pigment degradation (Wolterbeek et al. 2003). V and Al are also effective inhibitors of chlorophyll biosynthesis (Madejon 2013; Mihailovic et al. 2008) while Al and Fe can depress carotenoid levels in lichens (Bačkor and Fahlset 2004) and plants (Azmat and Hasan 2008). In spite of all this evidence from the literature, we detected a significant inverse correlation between calcium in exposed thalli and pigments (chlorophyll a: r = −0.503, p < 0.0005; chlorophyll b: r = −0.496, p < 0.0005; xanthophylls + carotenoids: r = −0.522, p < 0.0005), membrane damage (EC%: r = 0.292, p < 0.0005, TBARS: r = 0.212, p = 0.006) and lichen vitality (r = −0.458, p < 0.0005) only in relation to temporal variation on a yearly base in the study area, as a consequence of a marked peak of elements detected in the trimester November 2010–January 2011 compared with the other trimesters (Corapi 2011). The same negative correlations were detected for Fe, Vand Mg but not for Al. The only elements that showed a significant correlation with spatial variation of one of the ecophysiological parameters (i.e. TBARS) were Mg and Fe (respectively r = 0.215, p = 0.006 and r = 0.318, p < 0.0005). However, while iron is a well-known promoter of lipid peroxidation of cell membranes, it is doubtful whether the association detected with Mg can support a cause-effect relationship. Due to its alkaline nature, Mg can be detrimental to an acidophilous lichen (Pisut and Pisut 2006) such as P. furfuracea but we do not know if it can promote membrane damage in the manner reported above for calcium. Once again, we speculate that the apparent lack of spatial covariance in the whole study area between elements and ecophysiological parameters, and the weak correlations detected for Fe and Mg, may be due to the interaction with or additive/negative effect of other non-monitored contaminants. In the analysis of the spatial pattern of ecophysiological biomarkers in the whole study area, no correlation was detected between their values and the distance of each monitoring site from the cement plant.

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However, calculation of the index of environmental favourableness (IEF) to lichens and the statistical testing of the variation of the values vs their location in the whole study area suggested that a spatial gradient (in the form of distance bands from the cement plant) in recovery of the eco-physiological status of lichens is still detectable when pigments and the phaeophytization quotient are considered. A clear spatial gradient of recovery from peroxidative damage was not detected even though the mean values of IEF2 showed an apparent progressive increase from the inner distance band to the outer one (Table 5), suggesting a relatively more patchy distribution within the study area of sites with transplants affected by oxidative stress. This suggests that the two partners of symbiotic association show a different spatial distribution of their impairment. In fact, due to the major contribution of fungus to the total lichen biomass and its more external location (Johansson 2011; Tyler 1989), the increase in cellular peroxidation levels (compared to the LOA value) is likely due mainly to damage that the fungal partner suffers following exposure to noxious gas and/or atmospheric particulate matter bearing toxic substances. The MDA levels, approaching values detected in other lichens exposed in urban/industrial sites (Carreras and Pignata 2007; Gonzalez and Pignata 1997), suggest that there was relatively widespread pollution (associated with the above-mentioned types of contaminants). The strongest contributors to the lichen’s ecophysiological spatial recovery (indicated by changes of IEF1 values) were chlorophyll a, chlorophyll b, xanthophylls + carotenoids and the phaeophytization quotient. In fact, their mean values in clusters C1, C2 and C3 showed a significant reduction when compared with those of both cluster C4 and the LOA (except OD435/OD415). With the exception of cluster C1, the phaeophytization quotient values do not seem to suggest a strong decline of this parameter in clusters C2 and C3 because lichens located in environments not affected by human pressures show OD435/OD415 values between 1.20 and 1.40 (Frati et al. 2011; Pirintsos et al. 2011; Bačkor and Loppi 2009). Nevertheless, the values showed a very low coefficient of variation (C2 = 1.714 %, C3 = 0.494 %) and differed significantly from C4 (CV = 2.214 %), meaning a slight but true increase in chlorophyll degradation. The LOA always showed values that were higher, albeit not significantly different, than those of C4 for all parameters except the phaeophytization quotient: its OD435/OD415 value was lower than those of C4 and

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C3 and higher than those of C1 and C2 but differed significantly only from C1 due to its strong reduction. It is difficult to explain this result. Again, the phaeophytization quotient in the LOA is within the range of values measured in lichens located in uncontaminated environments. Perhaps the higher variance of this parameter (CV = 8.138 %) compared with those of the pigments measured in LOA (CV: chlorophyll a = 3.28 %, chlorophyll b = 2.068 %, xanthophylls + carotenoids = 2.42 %) suggests the need of a higher number of measures for a more accurate evaluation of its value (Loppi 2006). Due to the lack of significant correlations between the selected contaminants (Ca, Mg, Al, Fe and V) and the eco-physiological parameters (except the very weak ones between Fe and Mg and lipid peroxidation) in the whole study area, we cannot explain the different spatial contamination for TBARS (a patchy distribution of impaired and non-impaired sites) on the one hand and the pigments and OD435/OD415 on the other (a constant increase in the number of non-impaired sites vs the number of impaired sites moving from the inner to the outer part of the 0-–3-km area around the cement plant). The higher number of sites showing relief from peroxidative damage (C1 + C4) located within the 2km band from the cement plant removes any polarization in spatial recovery. Whether this is a consequence of contaminants not monitored in the present study should be tested in future monitoring activities. A proper control must isolate the variation of monitored parameters due to the pressure in which we are interested, i.e. it must lack only this pressure but not the others present at the impacted sites (Downes et al. 2002). We consider cluster 4 a Blocal control^ with regard to the pigment concentrations and phaeophytization quotient. Although the pigment values showed an average reduction of about 15 % compared with the LOA (external control), perhaps as a consequence of both suboptimal temperature for the lichen (Pirintsos et al. 2011) and contaminants emitted by anthropogenic sources, they did not differ significantly from the values for thalli in the lichen origin area; hence, these potential negative factors did not have any marked effect on the pigment concentrations. Moreover two thirds of the cluster 4 sites had pigment values higher than the remaining clusters and, as mentioned above, the mean values of chlorophylls, xanthophylls + carotenoids and phaeophytization quotient were significantly different from those of clusters 1, 2 and 3. This suggests that cluster 4 is effective in accurately isolating the effect

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of pollutants that we believe act mainly close to the industrial zone and cement plant. Degradation of chlorophyll into phaeophytin may not be the only mechanism causing the reduction of pigment concentrations in our study. Cluster C1 showed an appreciable and statistically significant decrease with respect to C4 (internal control) and LOA (external control) in both OD435/OD415 (−35 and −29.8 %) and chlorophyll a, chlorophyll b, xanthophylls + carotenoids (−33.3 and −29.5, −30.9 and −45.3 %, −40.6 and −39.6 %). Clusters C2 and C3 also differed significantly (p < 0.05) from C4 in both phaeophytization quotient and pigment levels. Nevertheless, although clusters C2 and C3 had significantly different chlorophyll values, they did not differ from each other in the phaeophytization quotient. The further reduction of pigment levels in cluster 2 with respect to cluster 3 is not easily explainable. However, there are two glass works in the industrial zone (mainly situated in the SW sector where six of the seven sites of cluster 2 are located). SO2, NOx and Fl− are the main components of atmospheric emissions of glass works (IFC, 2015; Richardson 1992). Yet the SO2 standard emissions are lower than the SO2 levels that can be detrimental to P. furfuracea (O’Hare and Williams 1975), while although NOx emissions can damage lichens (Nash 1976), they usually promote chlorophyll degradation either directly (Frati et al. 2006) or indirectly by conversion into nitric acid (Minambiente, 2015; Riddell et al., 2012). Fluoride emissions take place at levels potentially harmful for lichens (Pananjay Kartikey Tiwari 2008) (i.e. 4 mg/m3 on average), especially because they can be concentrated in hydrated thalli (our transplants should have been hydrated due to the satisfactory rainfall and humidity) to more than 200 times its background concentration. Although the mechanism of its damage could be a displacement of Mg atoms from chlorophylls (Kartick et al. 2012), other studies have demonstrated that the harmful effect on plant pigment concentrations is due to inhibition of enolase, which affects the formation of acid precursors for chlorophylls and xanthophylls (Miller et al. 1984), and of incorporation of γ-aminolevulinic acid into the coproporphyrinogen pool of the tetrapyrrole biosynthesis pathway (Wallis et al. 1974). The inhibitory effect of fluoride on carotenoid biosynthesis has also been demonstrated (Goodwin 1954; McNulty 1958). We did not measure Fl− levels but we may reasonably assume that fluoride was included in atmospheric emissions coming

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from the two glass works. Hence, we can speculate that, due to the lack of difference in phaeophytization quotient between the two clusters despite their difference in pigment concentrations, an impairment of biosynthetic pathways of pigments could be one of the processes involved in the reduction of pigment levels at sites of cluster C2. For Fv/Fm, 56 of the 61 values were typical of healthy lichens (Bačkor and Loppi 2009). Cluster 2 showed a mean value of 0.60 which was virtually identical to that of the lichen origin area (0.59) but significantly different from that of the other clusters. However, this difference was due to a particularly low record (0.4) which when deleted made the ANOVA non-significant, a result that supports the hypothesis of no changes in this parameter due to local pressures. In a recent study (Paoli et al. 2015) carried out in Slovakia, thalli of the epiphytic acidophilous lichen Evernia prunastri, which has ecological requirements similar to those of P. furfuracea, were transplanted at sites with different potential anthropogenic sources of contaminants, one of which was a cement plant. After a 6-month exposure, thalli located near the cement factory showed a strong decrease in Fv/Fm with respect to both the initial value and those of other exposure sites (quarry, urban area, agricultural area). The different results between the two monitoring studies could be ascribable to the longer exposure period (3 vs 6 months) and the different substances emitted by the two cement plants. Indeed after 3 months, the exposure area around the Slovak cement works did not differ from the other exposure sites, and the two cement plants differed both in geological materials extracted in the respective quarries and the fuels used to power them, i.e. Slovak plant: coal and wastes, Italian plant: petcoke and heavy oil. Air pollution can also impair the growth rate and reproductive function of lichens. However, growth is usually a rather slow process and the effect of atmospheric pollutants can rarely be detected by a month-tomonth change in dry weight or thallus size, although the highly significant correlation between lichen dry matter gain and chlorophyll contents (Lobaria pulmonaria), especially in unshaded environments (Gauslaa et al. 2006), would allow the use of pigments as a suitable surrogate for change in dry biomass. Structures devoted to both asexual and sexual reproduction can be affected by urban/industrial air pollution. Sensitive species (Ramalina celastri) can show a reduction in number and coverage of apothecia (Estrabou et al. 2004), while

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resistant species (Physcia undulata) display a decrease in soralia coverage. The results of our study cannot predict any growth and reproductive compromise of thalli exposed in the study area. In fact, we do not have any data on the association of change in dry biomass of P. furfuracea with change in chlorophyll levels nor did we evaluate the status of asexual and sexual structures in the lichen before and after exposure in the monitoring area or between clusters with different degrees of eco-physiological impairment. However, an aim of future research could be the detection of a physiological damage threshold above which both the lichen’s growth rate and sexual and asexual reproduction are affected.

Conclusions In our study, only the variation of NOx concentrations measured close to the cement works correlated with the distance of the sites from the Italcementi plant, suggesting a contribution of cement manufacturing to their atmospheric enrichment. This association was not detected for ammonia, probably as a consequence of a confounding effect due to ammonia emitted by agricultural activities. NH3 levels, unlike NOx, were above the threshold of environmental protection for lichens. The lack of correlation between gases (except ammonia) and trace element concentrations on the one hand and ecophysiological parameters on the other was probably caused by the overriding/additive/interactive effect of other non-monitored pollutants emitted mainly from the cement plant. The multivariate-univariate approach for the ecophysiological parameters × sites analysis resulted in the identification of four clusters showing a different degrees of physiological impairment, in the calculation of an index of environmental favourableness for lichens and in the detection of a different spatial pattern of stress suffered by the mycobiont and photobiont, with the latter showing a clear spatial trend to gradual physiological recovery from the inner (close to the cement plant) to the outer band of the study area. The reduction of pigment concentrations is probably only partially due to conversion into phaeophytin. We consider the cluster with the best physiological status in the monitoring area to be a local (internal) control group better suited to accurate detection of the variation in eco-physiological parameters following the exposure period than the

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lichen origin area (LOA) due to the lack of an important confounding factor such as climatic differences.

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Page 19 of 21 500 Garty, J., Tomer, S., Levin, T., & Lehr, H. (2003). Lichens as biomonitors around a coal-fired power station in Israel. Environmental Research, 91, 186–198. Gauslaa, Y., Lie, M., Asbjorn Solhaug, K., & Ohlson, M. (2006). Growth and ecophysiological acclimation of the foliose lichen Lobaria pulmonaria in forests with contrasting light climates. Oecologia, 147, 406–416. Gilbert, O. L. (1976). An alkaline dust effect on epiphytic lichens. The Lichenologist, 8, 173–178. Giri, S., Shrivastava, D., Deshmukh, K., & Dubey, P. (2013). Effect of air pollution on chlorophyll content of leaves. Current Agriculture Research Journal, 1(2), 93–98. Goodwin, T.W. (1954). Carotenoids—their comparative biochemistry. New York. Chemical Publishing Co. Inc. http://archive. org/stream/carotenoidstheir00good/carotenoidstheir00good_ djvu.txt. Accessed 2 September 2014 Gonzalez, C. M., & Pignata, M. L. (1997). Chemical response of the lichen Punctelia subrudecta (Nyl.) Krog transplanted close to a power station in an urban-industrial environment. Environmental Pollution, 97(3), 195–203. Gonzalez, C. M., Orellana, L. C., Casanovas, S. S., & Pignata, M. L. (1998). Environmental conditions and chemical response of a transplanted lichen to an urban area. Journal of Environmental Management, 53, 73–81. Halliwell, B., & Gutteridge, J. M. C. (2007). Free radicals in biology and medicine (4th ed., ). Oxford:Oxford Press University. Hawksworth, D. L., & Rose, F. (1970). Qualitative scale for estimating sulphur dioxide air pollution in England and Wales using epiphytic lichens. Nature, 227, 145–148. Herzig, R., & Urech, M. (1991). Flechten als Bioindikatoren Integriertes biologisches Messsystem der Luftverschmutzung fur das Schweizer. Mittelland, Bibliotheca Lichenologica, 43, 1–283. Huang, Z. A., Jiang, D. A., Yang, Y., Sun, Y. W., & Jin, S. H. (2004). Effects of nitrogen deficiency on gas exchange, chlorophyll fluorescence, and antioxidant enzymes in leaves of rice plants. Photosynthetica, 42, 357–364. IFC (2015) International Finance Corporation. Environmental health and safety guidelines. Glass manufacturing. http://www.ifc.org/ wps/wcm/connect/384e20804885574ebc0cfe6a6515bb18/ Final%2B-%2BGlass%2Bmanufacturing.pdf?MOD = AJPERES&id = 1323152002618 (accessed, 16 June, 2015) Jensen, M. (2002). Measurement of chlorophyll fluorescence in lichens. In I. Kranner, R. P. Beckett, & A. K. Varma (Eds.), Protocols in lichenology—culturing, biochemistry, ecophysiology and use in biomonitoring (pp. 135–151). Berlin Hidelberg: Springer Verlag. Johansson, O. (2011). Epiphytic lichen response to nitrogen deposition. Doctoral Thesis. Umea University, Department of Ecology and Environmental Sciences Kartick, C. P., Mondal, N. K., Bhaumik, R., Banerjee, A., & Datta, J. K. (2012). Incorporation of fluoride in vegetation and associated biochemical changes due to fluoride contamination in water and soil: a comparative field study. Annals of Environmental Sciences, 6, 123–139. Kumar, S. R., & Thambavani, S. D. (2012). Effect of cement dust deposition on physiological behaviors of some selected plant species. International Journal of Scientific and Technological Research, 1(9), 98–105. Lai, J. C., Di Lorenzo, J. C., & Sheu, K. F. (1988). Pyruvate dehydrogenase complex is inhibited in calcium loaded

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