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Article

Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations

by
Malek Bousbih
1,2,3,
Mohammed S. Lamhamedi
4,
Mejda Abassi
2,
Damase P. Khasa
3 and
Zoubeir Bejaoui
1,2,*
1
Faculty of Sciences of Bizerte, University of Carthage, Jarzouna 7021, Tunisia
2
Laboratory of Forest Ecology (LR161INRGREF03), National Institute of Research in Rural Engineering, Water and Forests, University of Carthage, Hédi Elkarray Street, Elmenzah IV, BP 10, Ariana 2080, Tunisia
3
Centre for Forest Research and Institute for Integrative Systems Biology, University Laval, Quebec, QC G1V 0A6, Canada
4
Faculty of Forestry, Geography and Geomatics, Abitibi-Price Building, University Laval, Québec, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Environments 2025, 12(6), 191; https://doi.org/10.3390/environments12060191
Submission received: 24 April 2025 / Revised: 31 May 2025 / Accepted: 4 June 2025 / Published: 6 June 2025
(This article belongs to the Special Issue Trace Metals in the Environment: Advances in Detection Techniques, Pollution Assessment and Management)
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Abstract

:
Atmospheric emissions of industrial-origin trace metals are a major environmental problem that negatively affects air quality and the functioning of forest ecosystems. Traditional air quality monitoring methods require investments in equipment and infrastructure. Indeed, it is difficult to measure most of these pollutants because their concentrations usually occur at very low levels. However, this study explores an ecological approach for low-cost air quality biomonitoring that is based on native biological indicators in the context of the Mediterranean basin. This study aims (i) to evaluate the lichen species composition, diversity, and distribution across three distinct forest sites; (ii) investigate the relationship between lichen species richness and proximity to the pollution source; and (iii) evaluate heavy metal bioaccumulation using a moss species (Funaria hygrometrica) and a lichen species (Xanthoria parietina) as bioindicators of atmospheric pollution. High concentrations of toxic metals were observed along the transect and closer to the pollutant source with marked interspecies variability. X. parietina exhibited high bioaccumulation potential for most toxic metals (Fe, Zn, Pb, Cr, Cu, and Ni) compared to F. hygrometrica with concentrations varying across the three sites, reaching maximum dry-mass values of 6289 µg/g for Fe at the first site and 226 µg/g for Zn at Site 3. Our results suggest that X. parietina can be used as a potential bioindicator for long-term spatial biomonitoring of air quality by determining atmospheric toxic metals concentrations.

1. Introduction

Forests are ecosystems, the vulnerability of which is often linked to industrial technologies, fossil fuel combustion, intensive farming, and waste incineration [1,2]. Toxic metals, nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), and particulate matter are air pollutants that can be harmful to living organisms and habitats [3]. Long-term exposure to non-essential elements, such as lead (Pb), chromium (Cr), cadmium (Cd), aluminum (Al), arsenic (As), and mercury (Hg), adversely affects the physiology and productivity of plants [2,4]. Arsenic is not strictly a heavy metal; rather, it is a metalloid and, like other members of this group (e.g., antimony, tellurium), a very toxic element. Excessive quantities of these metals (and metalloids) may result in the increased production of reactive oxygen species (ROS), causing oxidative stress that leads to lipid peroxidation, alteration of cell structure, and loss of membrane integrity [5]. Moreover, forest ecosystems are subjected to frequent disturbances and stress (like pollution) that challenge their sustainability.
Air quality monitoring has been the subject of intensive research into predicting pollutant concentrations in ambient air, particularly those of toxic metals, which require detectors that are equipped with specific sensors [6,7,8]. Such measurements at fixed monitoring sites can provide a broad overview of air pollution concentrations [9]. Several current monitoring methods using real-time sensors are subject to several limitations and disadvantages, including the requirement for maintaining fixed positions for an extended time, limitations in sampling, and the high cost of acquiring and maintaining the equipment. Furthermore, these methods cannot reflect the harmful effects of pollutants, especially on biological processes in the environment [7,10,11].
However, biological sensors are based on bio-indicator species such as vascular plants, animals, fungi, lichens, and mosses that have been employed and deployed for monitoring air pollutants [10,12,13]. They have been widely adopted as biosensors while offering numerous advantages, including low cost, provision of information from the past, studies on the spatial distribution of toxic metals over a large landscape, and, therefore, a higher sampling density that would provide us with information regarding landscape health [14,15,16]. Biological systems also provide simultaneous information on the interactions between different heavy metal ions and their effects on these living organisms [17].
To monitor the ecological integrity of forests, numerous studies have focused on selecting species that can reliably detect degradation caused by air pollution [18,19]. Mosses and lichens represent an important part of the diversity in forest ecosystems. Mosses and lichens, sensitive air pollution indicators, have been widely used for studying the deposition of airborne pollutants [20,21].
Mosses and lichens are better indicators than vascular plants for monitoring environmental contamination by heavy metals [22,23] because the former can accumulate heavy metals [24,25] due to their morpho-physiological properties [26,27]. They are poikilohydric. In other words, mosses and lichens cannot regulate their water content, given that they do not have true vascular tissue, lack an absorptive root system, and possess primitive tissues with high permeability to moisture and gases [28,29]. Indeed, mosses and lichens simply absorb water and nutrients from the atmosphere surrounding them, and they can accumulate trace metals without any apparent visible adverse effects on their growth [5,30]. There is also a strong relationship between air pollution and the air quality index (AQI) [27,31,32].
The accumulation capacity of toxic metals is very complex and depends on several factors, such as the species under study and their genotypes, age, and levels of ion toxicity that they can tolerate [13,21]. The most frequently used moss species in forest monitoring are Pleurozium schreberi (Brid.) Mitt., Hylocomium splendens (Hedw.) Schimp., Funaria hygrometrica Hedw., Hypnum cupressiforme Hedw., Pseudoscleropodium purum (Hedw.) M. Fleisch., and Taxithelium nepalense (Schwägr.) Broth. [2,14,33]. Lichen species that are widely used and recommended include Flavoparmelia caperata (L.) Hale, Parmotrema arnoldii (Du Rietz) Hale, Xanthoria parietina (L.) Th. Fr., Hypogymnia physodes (L.) Nyl., Letharia vulpina (L.) Hue, and Pseudevernia furfuracea (L.) Zopf [34,35,36]. Many of these moss (and lichen) species are cosmopolitan in their distribution and are found either in high-latitude forests across the Holarctic or at low latitudes globally in moist forest ecosystems. However, to the best of our knowledge, no scientific study has yet evaluated the possibilities of using lichens or mosses to assess the extent of industrial pollution in the semi-arid and arid Mediterranean region. The results of our work, which is conducted at a very low cost, would be complementary to those data that are obtained by electronic instruments, for which sampling intensity is very limited.
We hypothesize that different species exhibit distinct bioaccumulation potentials and that decreasing distance to the pollution source leads to reduced species diversity and altered metal accumulation patterns. Providing this relevant information could reveal the reliability of air quality biomonitoring, which could be beneficial for better air pollution management. This study innovatively integrates ecological (lichen community composition, diversity, distribution) and physiological (metal bioaccumulation) analyses using two phylogenetically distinct thallophytes. By evaluating their sensitivity and accumulation patterns along an industrial emission gradient, we provide novel comparative insights into species-specific responses under real-world Mediterranean forest conditions—a rarely explored framework. Additionally, our low-cost field protocol offers a scalable, instrument-free biomonitoring solution for air quality assessment. The present study was carried out (i) to evaluate the lichen species composition, diversity, and distribution across different forest sites; (ii) to investigate the influence of distance from the source of atmospheric emissions of industrial origin on air quality, using lichen community associations; and (iii) to evaluate the bioaccumulative potential of toxic metals (Fe, Zn, Pb, Cu, Cr, Ni, As, Co, and Cd), two bioindicators, a species of moss (Funaria hygrometrica (Hedw.), and a species of lichen (Xanthoria parietina (L.) Th.Fr.), which were tested in the same forest sites.

2. Materials and Methods

2.1. Sampling Sites in Mediterranean Forest Plantations

The Rimel Forest plantations (37°13′24″ N 10°00′38″ E) are located southeast of the City of Bizerte, which is in the extreme northeast of Tunisia (Figure 1). These plantations cover 3502 ha with a roughly triangular shape (30 km × 2 km). These forest plantations, which were established in 1903, are composed mainly of pines (Pinus halepensis Mill., Pinus pinea L., Pinus pinaster Aiton, Pinus canariensis C.Sm. ex D.C.), Eucalyptus sp., and Acacia sp. The site is characterized by sandy soil [37]. Prevailing winds come from the northwest, and annual precipitation was 458 mm in 2023, with an average annual temperature of 20 °C [38].
These forest plantations are located near the Zarzouna Industrial Zone (ZIZ), which consists of two large industries: the Tunisian Company of Refining Industries (TCRI) and the Tunisian Company of Lubricants (TCL) (Figure 1). TCRI has been operating in Bizerte since 1960 and covers a total area of 180 ha. Its main sector of activity is petroleum refining. TCL has been operating since 1984; its main activities are the collection and regeneration of used lubricating oils, together with the manufacturing and marketing of lubricating greases. The forest plantations are also crossed throughout their length by a highway that was constructed in 2002 and which has very high daily road traffic (Figure 1). Collectively, these industrial facilities and the highway are the major sources of air pollution, particularly toxic metals [1,13].
Sampling followed an NW-SE transect deliberately aligned with prevailing winds to quantify pollution gradients from the petrochemical zone. This spatial design substitutes traditional background values by establishing a contamination continuum [33] (Figure 1). The first site that was selected is close to the petrochemical zone, which emits pollutants at a distance of 3.7 km. The second site is in an intermediate zone, which is further from the pollution source, i.e., around 7.8 km. The third site represents the area that is least exposed to pollution and furthest from the industrial zone at 12.9 km. Each site was subdivided into 3 plots, which were separated by 300 m. Each plot has a surface area of 50 m × 50 m [7,39]. Furthermore, no true control site (i.e., an unpolluted and ecologically comparable reference area) exists within the study region due to widespread anthropogenic influences and the ecological homogeneity of the forest ecosystem. It is very difficult to find a true control (not a false control) on the scale of Tunisia, i.e., a stand of the same species of the same age grown under the same edaphic and environmental conditions.

2.2. Determination of Lichen Diversity and the Air Quality Index

Lichen diversity was assessed using three ecological indices: species richness, species abundance, and the Shannon index [40,41]. To enhance the representativeness of lichen diversity in each site, surveys were conducted on 10 trees of the most widespread native forest species, i.e., Aleppo pine (Pinus halepensis) [42]. Observations were conducted on the trunk side that was exposed to the prevailing winds (northwest) according to [38], using a rectangular grid (20 cm × 50 cm). The grid consisted of 10 cells (2 rows × 5 columns), with each cell measuring 10 cm × 10 cm (i.e., 100 cm2). The grid was placed at a breast height of around 1.3 m to avoid any soil or animal contamination. The distance separating the two trees was a minimum of 15 m. The surveys were conducted between March and April 2023, according to ref. [43].
Lichen species were identified morphologically using a stereomicroscope (Nikon SMZ-445, Nikon corporation, Tokyo, Japan) based on diagnostic criteria, such as thallus morphology (crustose, foliose, fruticose, and leprose), coloration, surface texture, and the presence of reproductive structures (apothecia, pycnidia) [18,19,26,27]. Some taxa also exhibited specialized structures that were informative for both taxonomic identification and ecological interpretation. These included rhizines (root-like structures) that anchor the lichen to its substrate, commonly found in foliose forms [15,19,26,27]; isidia (small outgrowths) involved in vegetative reproduction [44]; and cephalodia (dark-colored structures) housing cyanobacteria, which enable nitrogen fixation [18,19]. Lichen species identification relied on both traditional morphological keys [45,46] and current advances in lichen symbiosis and structural understanding [47].
Species abundance was determined by calculating the frequency of occurrence of each lichen species in sites S1, S2, and S3. This was expressed as the proportion of meshes containing the species relative to the total number of meshes that were sampled, amounting to 100 meshes per site [43]. Species richness was estimated by counting the number of species that were identified across the three sites [43]. The Shannon index (H’) integrates information on both diversity and species abundance and is calculated using the following formula [41]:
H ′ = − ∑ i = 1 S p i · l o g 2 ( p i ) ,
where H’ is the Shannon index; pi is the proportion abundance of a species that is present (pi = ni/N); ni is the number of individuals that were counted for a species that were present. N is the total number of individuals that were counted, all species combined, and S is the total or cardinal number of species that are present.
The air quality index (AQI) is a quantitative metric that evaluates air pollution levels using the diversity and frequency of epiphytic lichens as bioindicators. It was determined using the VDI method (Verein Deutscher Ingenieure) [43,48], a standardized protocol for lichen-based air quality assessment. This method calculates the AQI for each site as the sum of the average frequencies of all lichen species recorded across 10 sampled trees. According to the AQI that was obtained in each site under study, the level of air pollution can be estimated. This method is based on a correspondence scale between AQI values and air pollution levels [43]. AQI is inversely proportional to the level of air pollution present. AQI values ranging from 0 to 12.5 correspond to very high air pollution levels (sensitive lichens absent/severely reduced), while values between 37.5 and 50 indicate low levels of air pollution (high lichen diversity) [43].
While mosses can function as bioindicators, this study focused exclusively on lichen diversity for two primary reasons: (i) methodological specificity (lichens are established as sensitive atmospheric bioindicators and are integral to air quality index (AQI) frameworks) and (ii) ecological context (preliminary surveys (not yet published) revealed negligible moss diversity in the study area (<5 species), making lichens a statistically more robust metric for air quality assessment.

2.3. Determination of Bioaccumulation Potential of Toxic Metals in the Lichen Xanthoria parietina and the Moss Funaria hygrometrica

2.3.1. Sample Collection and Preparation

As part of the assessment of air pollution in the Rimel Forest plantations, the lichen Xanthoria parietina was confirmed as the only cosmopolitan lichen species exhibiting both a wide ecological amplitude and a high tolerance to polluted and toxic environments [49]. Preliminary surveys across the three sampling sites revealed the high abundance and dominance of the moss Funaria hygrometrica, which was selected as the study area’s representative species due to its resilience to environmental stressors, invasive capacity, and widespread prevalence. While other moss species were present, their distribution and ecological dominance were comparatively limited.
For each sampling site and for each species (Xanthoria parietina and Funaria hygrometrica), three composite samples were collected on nine trunks of nine trees of individual Aleppo pines (Pinus halepensis). Sampling was conducted on the side that was exposed to the prevailing winds at a height of about 1.30 m from the ground surface. Each composite sample of Xanthoria parietina or Funaria hygrometrica was collected from within a 15 m radius centered on a predefined sampling point in each study plot. Each composite was composed of three subsamples, each taken from the trunk of a different tree, as previously described. Samples were collected by scraping with a sterilized spatula and then transferred to labeled paper bags. The choice of samples was mainly focused on lichen thalli and moss pads that were greater than 20 cm in size. It is important to note that the average growth rate of both Xanthoria parietina and Funaria hygrometrica is approximately 1 cm/year [50]. Only their green leafy parts (multi-year gametophytes) were used because they accumulate the highest amounts of toxic metals [14,39,51]. All samples were collected during spring, when high humidity and moderate temperatures promote optimal physiological activity in both bioindicators (lichens and mosses), minimizing the influence of external seasonal variability. This facilitates a reliable comparative assessment of their metal accumulation capacities under standardized environmental conditions [52]. In the laboratory, all samples of Funaria hygrometrica and Xanthoria parietina were dried at 60 °C for 72 h and crushed to create particles of smaller average size prior to analysis. The crushed samples were then stored in the dark at room temperature. Weighing was performed on an electronic analytical balance (BCE64 Entris® II, Sartorius, Goettingen, Germany) with an accuracy of 10−4 g and a weighing capacity of 60 g.

2.3.2. Concentrations of Toxic Metals

Quantification of Cd, Co, Cr, Cu, As, Ni, Pb, Zn, and Fe was conducted in the Laboratoire Chimie (Department of Wood and Forest Sciences, Université Laval, Quebec, QC, Canada). Concentrations of toxic metals were determined by inductively coupled plasma atomic absorption spectrometry (ICP-OES 5110, Agilent Technologies, Santa Clara, CA, USA) [53]. Calibration standards were prepared using 1000 µg/mL single-element stock solutions from Agilent Technologies, resulting in a multi-element calibration solution containing 5 µg/mL of each analyte in 1% HNO3 [54]. The limits of detection for each element were as follows: Cd: 5 µg/L, Co: 5 µg/L, Cr: 5 µg/L, Cu: 5 µg/L, As: 50 µg/L, Ni: 5 µg/L, Pb: 50 µg/L, Zn: 5 µg/L, and Fe: 5 µg/L [54]. In total, 1 g (dry mass) of plant material was dry-ashed in a porcelain crucible at a temperature of 505° C for 6 h [54] in a muffle furnace (Isotemp® 750-58, Fisher Scientific, Waltham, MA, USA). This method reduced the sample to a fine powder containing residual minerals from the original matrix. To bring the elements into solution, the residue was dissolved in 2 mL of nitric acid (HNO3, 30%, m/V) and 23 mL of demineralized water [54].

2.3.3. Metal Accumulation Index (MAI)

To evaluate differences in accumulation capacity between the two species, Xanthoria parietina and Funaria hygrometrica, a Metal Accumulation Index (MAI) was calculated for each trace metal element at each study site. This index is defined as follows [55]:
  M A I = C o n c e n t r a t i o n   o f   t h e   t o x i c   m e t a l   i n   X . p a r i e t i n a C o n c e n t r a t i o n   o f   t h e   t o x i c   m e t a l   i n   F .   h y g r o m e t r i c a
A value of MAI > 1 indicates that X. parietina accumulates more of the metal than F. hygrometrica, whereas a value of MAI < 1 indicates that it accumulates less than F. hygrometrica.

2.4. Statistical Analyses

Statistical analyses were performed in SPSS 29.0.2.0 (IBM, Armonk, NY, USA). One-way ANOVA was conducted on each variable after testing assumptions of normality and homoscedasticity. Pairwise comparisons of mean concentrations of toxic metals at the three sites in the two species (X. parietina and F. hygrometrica) were conducted with Tukey’s tests at a threshold level of 5%. Pearson correlation coefficients (r) and linear regressions were calculated to examine the relationship between the distance between each site and the ZIZ on the one hand and the concentration of each of the eight toxic metals and the air quality index (AQI) on the other hand. Spearman rank correlations (rs) and concordance coefficients (Kendall’s W) assessed the consistency of concentrations ranked among the elements and across sites. Perfect agreement among sets of rankings yields W = 1; when there is no agreement among rankings, W = 0.

3. Results

3.1. Lichen Diversity and (AQI)

The lichen surveys that were carried out on the three surveyed sites exhibited an overall diversity of 38 species, including four different types of lichen thalli, i.e., twenty-six crustose lichens, seven foliaceous, three fruticose, and two leprose growth forms (Table 1). This diversity ranges from 17 lichen species at the first site near the pollution source to 32 species at the farthest site, with 29 species being recorded at the second site (Table 1). Our results showed that lichen diversity gradually decreases along the transect towards the pollution source (Table 1). This diversity is reflected in the specific abundance recorded at Site 1, which is 3.65; diversity increases at Site 2, reaching a value of 4.82, and becomes higher at Site 3, which is farther from the ZIZ, with a lichen abundance of 7.66 (Table 1). This illustrates the relationship between lichen abundance and the proximity to the pollutant emission source, which is confirmed by a significant correlation coefficient (r = 0.97; p = 0.036). The highest Shannon index (H’) was recorded at Site 3 (H’ = 4.7), while Site 2 had an H’ of 3.88; Site 1 displayed the lowest index, equal to 3.76 (Table 1). These responses are confirmed by a highly significant correlation coefficient (r = 0.96; p = 0.002).
These results were confirmed by an increasing linear relationship between the AQI and the distance to the emission source, which were highly and positively correlated (r = 0.96; p ≤ 0.05) (Figure 2). At the first site, which is closest to the ZIZ pollution source, the lowest AQI was recorded (=36.5). Site 1 was dominated by crustose species, especially Lecidella elaoechroma and Lecanora expallens, together with foliaceous species, such as Xanthoria parietina (Table 1, Figure 2). Moving from the source of pollution at ZIZ to Sites 2 and 3, lichen diversity increases. The presence of new crustose and foliose species was noted, together with the appearance of fruticose and leprose species (Table 1). A notable exception in the study was Xanthoria parietina, which is a foliaceous lichen that appears to be a tolerant and resistant species found in abundance on the three sites (Table 1).

3.2. Bioaccumulation Potential of Toxic Metals

At Site 1, which was closest to the industrial zone, tissue concentrations (µg/g) of toxic metals in the two species followed very similar trends, i.e., a pattern of almost perfect agreement in their ordering (Spearman correlation: rs = 0.983, p < 0.001). The lichen X. parietina was ordered as Fe (6289) > Zn (132) > Pb (30) > Cr (22) > Cu (20) > Ni (13) > As (4) > Co (2) > Cd (0.5). Concentrations in the moss F. hygrometrica were ordered as Fe (4762) > Zn (140) > Pb (22) > Cr (20) = Cu (20) > Ni (11) > As (3) > Co (1.4) > Cd (0.5). Element concentrations in the lichen ranged from 86% (Zn) to 141% (Pb) of those in the moss tissues (Figure 3 and Figure 4).
At Site 2, most concentrations (µg/g) of toxic metals decreased in both species. In the lichen, these changes ranged from 75% (Ni) to 125% (Pb) of Site 1 concentrations (µg/g). Furthermore, elements in X. parietina followed an order similar to those of Site 1: Fe (6052) > Zn (111) > Pb (34) > Cu (25) > Cr (19) > Ni (9.8) > As (3) > Co (2) > Cd (0.5). In the moss element, concentrations at Site 2 ranged from 60% (Ni) to 105% (Pb) of Site 1 concentrations (µg/g) and followed a similar ordering: Fe (3649) > Zn (91) > Cr (22) > Pb (20) > Cu (15) > Ni (6.8) > As (2.4) > Co (1.4) > Cd (0.5) (Figure 3 and Figure 4).
At Site 3, which was the furthest from the industrial zone, concentrations of toxic metals within X. parietina followed the same order as for those in Site 1: Fe (4685) > Zn (217) > Pb (41) > Cr (15) > Cu (14) > Ni (8) > As (2) > Co (1.5) > Cd (0.5). Site 3 concentrations in the lichen ranged from 40% (Cu) to 174% (Pb, Zn) of Site 1. For moss tissue accumulations at Site 3, the elements were ordered as Fe (2506) > Zn (167) > Pb (34) > Cu (15) > Cr (9.8) > Ni (6.4) > As (2) > Co (1) > Cd (0.5) (Figure 3 and Figure 4). Site 3 concentrations of elements in the moss ranged from 60% (Ni) to 213% (Pb) of Site 1. Within the Rimel Forest, our study showed increasing concentrations of toxic metals, mainly Fe, Zn, Pb, Cr, and Cu, in the two species, especially moving closer to ZIZ (Figure 3). It was noted that Fe consistently exhibited the highest tissue concentrations compared to the other toxic metals (Figure 3).
Ordering among the nine elements exhibited almost perfect agreement across sites for both species (Lichen: W = 0.992, χ2r = 23.38, df = 8, p = 0.0029; moss: W = 0.978, χ2r = 23.47, df = 8, p = 0.0028). The working hypothesis posited that pollutant levels (metal concentrations) and tissue uptake would decline with increasing distance from the emissions source. Concentrations of each element were ranked according to increasing site distance; however, no consistent decrease in concentrations was observed in either the lichen (slight agreement: W = 0.118, χ2r = 2.12, df = 2, p = 0.346) or the moss (fair agreement: W = 0.281, χ2r = 5.06, df = 2, p = 0.080). These results are not surprising given that they included all measured elements, which either declined or increased with distance from the source; moreover, their concentrations differed by four orders of magnitude. Subsequent analysis focused separately on the behaviors of the five elements with the highest concentrations, which differed by only two orders of magnitude (Figure 3), and the four elements with the lowest concentrations (Figure 4).
The separate examination of toxic metal accumulation with high concentrations (Fe, Zn, Pb, Cr, and Cu) by the two species is thus justified. In moving away from the ZIZ emission source, a significant decrease (p ≤ 0.05) in the concentrations of Fe (from 6289 to 4685 µg/g), Cr (from 22 to 14 µg/g), and Cu (from 20 to 8 µg/g) was recorded for X. parietina (Figure 5). Similarly, a significant decrease in Fe (from 4762 to 2506 µg/g), Cr (from 27 to 10 µg/g), and Cu (from 19.6 to 16 µg/g) was observed for F. hygrometrica (Figure 5). These trends translate to negative correlation coefficients (r) (Figure 5). The opposite trend was observed for Pb concentrations (from 22 to 47 µg/g) in F. hygrometrica and Pb (from 31 to 54 µg/g) and Zn (from 129 to 226 µg/g) in X. Parietina. These toxic metals were significantly higher (p ≤ 0.05) at distances far from the emitting source (Figure 5), which translates to positive correlation coefficients (r). Furthermore, the two species also accumulate other toxic metals (Ni, As, Co, Cd) but at low concentrations. Indeed, these metals showed a significant decrease in their concentration (p ≤ 0.05) as the distance from the ZIZ emission source increased (Figure 6). These variations are reflected by negative correlation coefficients (r).
However, the southeastern portion of the Rimel Forest (Site 3) is located close to daily traffic on the road leading to the Mediterranean Sea. Indeed, the prevailing northwest winds favor the propagation of airborne toxic metal particles originating from many light vehicles moving toward the south of the forest. At Site 1, adjacent to the industrial zone, the MAI values for Fe, Zn, Pb, Cr, Cu, Cd, Co, As, and Ni were 1.2, 0.9, 1.4, 0.8, 1.03, 1.03, 1.2, 1.3, and 1.1, respectively. At Site 2, these values were 1.7, 1.3, 1.7, 0.8, 1.6, 1.0, 1.5, 1.14, and 1.5. At Site 3, the most distant from the industrial zone, the MAI values reached 1.7, 1.1, 1.2, 1.4, 0.9, 1.0, 1.3, 1.02, and 1.2. Overall, X. parietina exhibited higher accumulation capacity for most metals at all three sites. Exceptions occurred for Zn and Cr at Site 1, Cr at Site 2, and Cu at Site 3, where F. hygrometrica showed greater accumulation. Cd was accumulated equally by both species at Sites 2 and 3. In lichen and moss, metals accumulated from Site 1 to peak at Site 2 before declining at Site 3 (moderate agreement: W = 0.520, χ2r = 5.20, df = 2, p = 0.074). In lichen, element accumulation rates had consistent ordering among sites (almost perfect agreement: W = 0.822, χ2r = 9.87, df = 4, p = 0.042); in moss, elements exhibit much less consistent ordering among sites (moderate agreement: W = 0.539, χ2r = 6.47, df = 4, p = 0.167).

4. Discussion

The present study highlights significant changes in the composition of the lichen community in the Rimel Forest across three monitoring sites (Table 1). These changes provide valuable insights into the spatial distribution of air pollutant loading, confirming previous findings regarding the correlation between air pollution and lichen diversity [56]. High lichen diversity was documented in Mediterranean semi-arid zones, represented by four major thallus growth forms, i.e., crustose, foliose, fruticose, and leprose (Table 1). These results make it possible to assess the impact of industrial emissions on air quality and can serve as a basis for environmental monitoring strategies and risk management that are related to air pollution. Furthermore, the ecological parameters that were analyzed (lichen richness, specific abundance, H’, and AQI) showed significant increases with increasing distance from the pollutant emission source. This indicates higher pollution levels near the ZIZ, as evidenced by high correlation coefficients between the calculated indices (AQI, H’) and the distance that was traveled by the pollutants (Table 1, Figure 2). The first site demonstrated the dominance of crustose lichens and the presence of some foliose species but the complete absence of fruticose and leprose species (Table 1). This progressive decline in lichen diversity along the transect toward the ZIZ emission source is consistent with previous research [57,58]. Fruticose and foliose lichens attach to the substrate by a basal holdfast, a point that is much smaller than the surface area of the entire thallus. In sitting above the substrate, they are highly sensitive to exposure to atmospheric pollutants and play a crucial role in atmospheric deposition, unlike crustose lichens, which are deeply embedded in the substrate and more resistant to pollution [59]. Crustose lichens are considered to be reliable indicators of significant atmospheric pollution levels [60]. Their lower photosynthetic activity compared to fruticose and foliose species further enhances their tolerance to air pollution [61]. The results indicated an AQI value ranging between 25 and 37.5 at the first site (Figure 2), suggesting moderate air pollution, according to [48]. However, at the third site, which is farther from the ZIZ pollution source, an improvement in AQI was observed. A value of 76.6 was attained (Figure 2), which was indicative of low air pollution [48]. The composition of the lichen community, the Shannon index H’, and the AQI estimates confirm the hypothesis that air quality at the three sites is directly influenced by their proximity to the pollution source. In this study, we aimed to characterize the air quality of the Rimel Forest through the accumulation of toxic metal concentrations in the two bioindicators, X. parietina and F. hygrometrica. The results highlighted high concentrations of Fe, Zn, Pb, Cu, Cr, and Ni in X. parietina and F. hygrometrica (Figure 3 and Figure 4). While our methodology prioritized lichen-based assessment due to local ecological constraints, we recognize significant methodological trade-offs. Contemporary research demonstrates taxon-specific sensitivities where Świsłowski and Samecka-Cymerman [62] documented greater toxic metal accumulation in mosses (Sphagnum fallax and Pleurozium schreberi) versus Dicranum polysetum across Polish urban gradients. Similarly, Capozzi et al. [63] reported Pseudoscleropodium purum exhibited enhanced Cd, Cr, and Cu sequestration capacity over Evernia prunastri in Italian urban matrices]. Indeed, through previous research, the concentrations of toxic metals within different species of lichens and mosses have shown significant correlations with the concentrations of toxic metals in the atmosphere, which characterize polluted urban regions that are affected by industrial emissions and road traffic [13,20,64]. Similar findings have been documented across Mediterranean regions, underscoring consistent patterns of atmospheric metal contamination. El Rhzaoui et al. (2015) reported elevated concentrations of Cu, Cr, Zn, and Pb, with Pb being the dominant contaminant in Xanthoria parietina sampled near urban and roadside forest sites [49]. Comparable contamination patterns (Cu, Fe, Ni, Cd, Pb) in Xanthoria parietina have also been observed in the Tuscany region, Italy [65], and in Elmasburnu Natural Park, Turkey [66], highlighting transregional anthropogenic pressures in Mediterranean ecosystems. According to the World Health Organization (WHO), the air quality regulatory limits for heavy metals are set at 0.007 × 10−3 µg/m3 for As, 0.005 µg/m3 for Cd, 0.003 × 10−4 µg/m3 for Cr, 0.003 × 10−2 µg/m3 for Ni, 0.24 µg/m3 for Cu, 2 µg/m3 for Zn, and 0.5 µg/m3 for Pb [67]. The measured concentrations in the study species indicated a high level of contamination in the Rimel Forest.
The results demonstrated a significant increase in Fe, Cu, Cr, Ni, As, and Co in X. parietina and F. hygrometrica from the third to the first site (Figure 5 and Figure 6). The elevated Fe and Cr concentrations near the first site may be attributed to emissions from the nearby petrochemical industrial complex (Figure 1) [68,69] and road traffic, given that daily vehicular movement on the Bizerte-Tunis Highway contributes to contamination [70]. Cu, a well-known tracer for gasoline emissions [71], was also found in high concentrations near the first site. Conversely, at the third site, Zn and Pb concentrations were significantly higher (Figure 5 and Figure 6), likely due to elevated Pb levels that primarily reflect mixed anthropogenic sources: (i) vehicular emissions (leaded fuel combustion, brake wear, and resuspended road dust) [72] from the adjacent high-traffic corridor and (ii) maritime operations (ship fuel, port activities) [73]. This multi-source attribution accounts for the site’s dual exposure to terrestrial and marine transport networks. Prevailing wind patterns also may have influenced toxic metals deposition, as has been previously observed by [31]. The presence of agricultural land near the third site could also explain Zn and Pb contamination. The interplay between pollution source distance, site location, and extrinsic climatic and topographical conditions significantly affects toxic metal concentrations and overall contamination levels [44,74,75].
X. parietina and F. hygrometrica are effective bioindicators, given that they rely entirely on atmospheric deposition for their nutrition. Due to their lack of root systems, protective cuticles, and filtration mechanisms, they are highly sensitive to atmospheric pollution and are capable of bioaccumulating toxic metals to levels exceeding their physiological demands without suffering major physiological damage. The study showed that toxic metal concentrations were generally higher in X. parietina compared to F. hygrometrica (Figure 3 and Figure 4) despite similar climatic and topographical conditions. This discrepancy may be due to species-specific phenology. Mosses exhibit growth patterns that are closely linked to humidity and precipitation, which enhance their exposure to atmospheric deposition and, consequently, their bioaccumulation capacity. In contrast, lichens grow continuously throughout the year, regardless of climatic conditions, making them reliable long-term bioindicators of atmospheric pollution. This study underscores the importance of using bioindicators like X. parietina and F. hygrometrica to complement traditional physicochemical sensors. Their use enhances the spatial and temporal resolution of atmospheric toxic metals’ impacts on human health and ecosystems, providing valuable data for pollution mitigation strategies. Nevertheless, synergistic applications show particular promise, where Marié et al. [76] established the moss Orthotrichum diaphanum and Parmotrema pilosum as complementary hyperaccumulators for Fe and Cu in industrial atmospheres, while Salo et al. [77] demonstrated the moss Sphagnum papillosum and the lichen Hypogymnia physodes provide differential but complementary Zn, Ni, and Pb detection thresholds in Finnish traffic corridors. This complementarity extends to forest ecosystems, where Kłos et al. [78] quantified the moss Pleurozium schreberi and the lichens Hypogymnia physodes as co-indicators capturing distinct elemental niches (Cd and Pb vs. Cu and Zn) across Polish forests [78]. Given that air pollution is a major global health concern linked to lung cancer (As, Cd, Cr, Ni), respiratory irritation (Cu, Zn), and developmental disorders (Pb poisoning) [67,79,80], biomonitoring should be integrated into air quality management programs.

5. Conclusions and Research Needs

Reducing air pollution in urban areas is essential for protecting public health. Monitoring air quality and implementing pollution reduction measures can substantially improve the well-being of urban populations. This study demonstrates a marked decline in air quality within Rimel Forest, strongly correlated with proximity to an industrial zone. Lichen diversity decreased substantially at the most polluted site, dominated by pollution-tolerant crustose species, with low ecological indices (Shannon index H’ = 3.76; air quality index [AQI] = 25–37.5, indicating moderate-to-high pollution). In contrast, the least impacted site exhibited higher species richness and improved air quality (AQI = 76.6). Trace metal bioaccumulation peaked at the industrial–proximal site, with concentrations of 6.289 µg/g Fe (industrial particulates), 140 µg/g Zn (vehicular emissions), and 30 µg/g Pb (maritime activity), underscoring anthropogenic influences. The use of native bioindicators is a powerful tool for environmental monitoring, offering a dynamic and responsive approach to assessing ecosystem health and changes. The two native species that were tested in this study for spatial variability of air quality monitoring demonstrated a strong potential for the bioaccumulation of atmospheric heavy metals (Cd, Co, Cr, Cu, As, Ni, Pb, Zn, Fe). It is also important to highlight that heavy metal concentrations increase as one approaches the emission source. Indeed, lichen diversity gradually decreases in close proximity to pollution sources. The concentrations of Fe, Zn, and Pb reached 6289 µg/g, 140 µg/g, and 30 µg/g, respectively, at the most polluted site. Our results indicate that X. parietina has a higher bioaccumulation potential for most heavy metals compared to F. hygrometrica. This study highlights the effectiveness of X. parietina for biomonitoring atmospheric heavy metal concentrations. Furthermore, this research opens interesting perspectives regarding the application of lichen reintroduction techniques, which could be particularly useful in areas where these organisms are rare or absent. Furthermore, expanding these studies to other industrial and urban regions will facilitate comparisons of spatial pollution patterns and contribute to a broader understanding of air quality variations. Exploring the interactions between air pollution and climate change is also crucial, given that shifting climatic conditions may influence heavy metal deposition and bioindicator responses. Finally, establishing correlations between bioindicator findings and public health data would refine the assessment of population exposure risks and support the development of informed public health policies. By addressing these research directions, environmental monitoring frameworks can be strengthened, contributing to pollution reduction efforts and ultimately safeguarding both ecosystems and human health. A proposed future study aims to expand the scope by incorporating additional environmental matrices, such as tree tissues (wood and needles), litter, and soil, as complementary bioindicators. This integrated approach enables a comprehensive assessment of contamination dynamics, capturing both historical deposition patterns and contemporary ecosystem responses.

Author Contributions

Conceptualization, M.B., M.S.L. and Z.B.; methodology, M.B., Z.B., M.S.L., D.P.K. and M.A.; software, M.B., M.S.L. and Z.B; validation, M.B., M.S.L., M.A., D.P.K. and Z.B.; formal analysis, M.B.; M.S.L. and Z.B.; investigation, M.B.; M.S.L. and Z.B.; data curation, M.B., M.S.L. and Z.B; writing—original draft preparation, M.B., Z.B. and M.S.L.; writing—review and editing, M.B., Z.B., M.S.L., M.A. and D.P.K.; supervision, M.S.L., D.P.K. and Z.B.; funding acquisition, D.P.K. and Z.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University of Carthage, Ministry of Higher Education and Scientific Research, Tunisia, the National Institute of Research in Rural Engineering, Water and Forests (INRGREF), Tunisia, and University Laval, Quebec, QC, Canada.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We are thankful for the chemical, professional, and technical managers of the various laboratories of the forest ecology laboratory of INRGREF (Tunis, Tunisia) and the Department of Wood and Forest Sciences of the Faculty of Forestry, Geography and Geomatics of University Laval (Quebec, QC, Canada) for their valuable technical support to this project. We are thankful to William F.J. Parsons for English editing.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Swisłowski, P.; Vergel, K.; Zinicovscaia, I.; Rajfur, M.; Waclawek, M. Mosses as a biomonitor to identify elements released into the air as a result of car workshop activities. Ecol. Indic. 2022, 138, 108849. [Google Scholar] [CrossRef]
  2. Phaenark, C.; Niamsuthi, A.; Paejaroen, P.; Chunchob, S.; Cronberg, N.; Sawangproh, W. Comparative Toxicity of Heavy Metals Cd, Pb, and Zn to Three Acrocarpous Moss Species using Chlorophyll Contents. Trends Sci. 2022, 20, 4287. [Google Scholar] [CrossRef]
  3. Chaligava, O.; Shetekauri, S.H.; Badawy, W.M.; Frontasyeva, M.V.; Zinicovscaia, I.; Shetekauri, T.; Kvlividze, A.; Vergel, K.; Yushin, N. Characterization of Trace Elements in Atmospheric Deposition Studied by Moss Biomonitoring in Georgia. Arch. Environ. Contam. Toxicol. 2020, 80, 350–367. [Google Scholar] [CrossRef]
  4. Alengebawy, A.; Abdelkhalek, S.T.; Qureshi, S.R.; Wang, M. Heavy Metals and Pesticides Toxicity in Agricultural Soil and Plants: Ecological Risks and Human Health Implications. Toxics 2021, 9, 42. [Google Scholar] [CrossRef]
  5. Stankovic, J.D.; Sabovljevic, A.D.; Sabovljevic, M.S. Bryophytes and heavy metals: A review. Acta Bot. Croat. 2018, 77, 109–118. [Google Scholar] [CrossRef]
  6. Gribacheva, N.; Gecheva, G.; Zhiyanski, M.; Pavlova-Traykova, E.; Yaneva, R. Active and passive moss monitoring of trace elements in urban and mountain areas, Bulgaria. For. Ideas 2021, 27, 309–317. [Google Scholar]
  7. Lazo, P.; Kika, A.; Qarri, F.; Bekteshi, L.; Allajbeu, S.; Stafilov, T. Air Quality Assessment by Moss Biomonitoring and Trace Metals Atmospheric Deposition. Aerosol Air Qual. Res. 2022, 22, 220008. [Google Scholar] [CrossRef]
  8. Stafilov, T.; Šajn, R.; Veličkovski-Simonović, S.; Tănăselia, C. Moss biomonitoring of air pollution with potentially toxic elements in the Kumanovo Region, North Macedonia. J. Environ. Sci. Health 2022, 57, 694–708. [Google Scholar] [CrossRef]
  9. Wolterbeek, B. Biomonitoring of trace element air pollution: Principles, possibilities and perspectives. Environ. Pollut. 2002, 120, 11–21. [Google Scholar] [CrossRef]
  10. Hasheminejad, S.; Moradi, H.; Soleimani, M. Potential of Pinus eldarica Medw. tree bark for biomonitoring polycyclic aromatic hydrocarbons in ambient air. Sci. Rep. 2024, 14, 6259. [Google Scholar] [CrossRef]
  11. Sakila, V.; Manohar, S. Real-time air quality monitoring in Bull Trench Kiln-based Brick industry by calibrating sensor readings and utilizing the Serverless Computing. Expert. Syst. Appl. 2024, 237, 121397. [Google Scholar] [CrossRef]
  12. Al-Alam, J.; Millet, M.; Khoury, D.; Rodrigues, A.; Akoury, E.; Tokajian, S.; Wazne, M. Biomonitoring of PAHs and PCBs in industrial, suburban, and rural areas using snails as sentinel organisms. Environ. Sci. Pollut. Res. 2024, 31, 4970–4984. [Google Scholar] [CrossRef] [PubMed]
  13. Cavazzin, B.; MacDonell, C.; Green, N.; Rothwell, J.J. Air pollution biomonitoring in an urban-industrial setting (Taranto, Italy) using Mediterranean plant species. Atmos. Pollut. Res. 2024, 15, 102105. [Google Scholar] [CrossRef]
  14. Frontasyeva, M.; Harmens, H.; Uzhinskiy, A.; Chaligava, O. Mosses as Biomonitors of Air Pollution: 2015/2016 Survey on Heavy Metals, Nitrogen and POPs in Europe and Beyond; PatriNat: Paris, France, 2020; p. 136. [Google Scholar]
  15. Zinicovscaia, I.; Chaligava, O.; Yushin, N.; Konstantin Vergel, G.; Hramco, C. Moss Biomonitoring of Atmospheric Trace Element Pollution in the Republic of Moldova. Arch. Environ. Contam. Toxicol. 2022, 82, 355–366. [Google Scholar] [CrossRef]
  16. Calas, A.; Schreck, E.; Viers, J.; Avellan, A.; Pages, A.; Dias-Alves, M.; Gardrat, E.; Behra, P.; Pont, V. Air quality, metalloid sources identification and environmental assessment using (bio)monitoring in the former mining district of Salsigne (Orbiel Valley, France). Chemosphere 2024, 357, 141974. [Google Scholar] [CrossRef]
  17. Tremper, A.H.; Agneta, M.; Burton, S.; Higgs, D.E.B. Field and Laboratory Exposures of Two Moss Species to Low Level Metal Pollution. J. Atmos. Chem. 2004, 49, 111–120. [Google Scholar] [CrossRef]
  18. Nascimbene, R.; Benesperi, P.; Giordani, M.; Grube, L.; Marini, C.; Mayrhofer, H. Les lichens des cheveux des forêts d’altitude peuvent-ils aider à détecter l’impact du changement global dans les Alpes? Diversité 2019, 11, 45. [Google Scholar] [CrossRef]
  19. Ravera, S.; Benesperi, R.; Bianchi, E.; Brunialti, G.; Di Nuzzo, L.; Frati, L.; Giordani, P.; Isocrono, D.; Nascimbene, J.; Vallese, C.; et al. Lobaria pulmonaria (L.) Hoffm.: The Multifaceted Suitability of the Lung Lichen to Monitor Forest Ecosystems. Forests 2023, 14, 2113. [Google Scholar] [CrossRef]
  20. Barandovski, L.; Stafilov, T.; Šajn, R.; Bačeva Andonovska, K.; Frontasyeva, M.; Zinicovscaia, I. Assessment of Atmospheric Deposition of Potentially Toxic Elements in Macedonia Using a Moss Biomonitoring Technique. Sustainability 2024, 16, 748. [Google Scholar] [CrossRef]
  21. Rajfur, M.; Stoica, A.L.; Swisłowski, P.; Stach, W.; Ziegenbalg, F.; Mattausch, E.M. Assessment of Atmospheric Pollution by Selected Elements and PAHs During 12-Month Active Biomonitoring of Terrestrial Mosses. Atmosphere 2024, 15, 102. [Google Scholar] [CrossRef]
  22. Lamano Ferreira, M.; Portela Ribeiro, A.; Rakauskas, F.; Bollamann, H.A.; Theophilo, C.Y.S.; Moreira, E.G.; Aranha, S.; Santos, C.J.; Giannico, V.; Elia, M.; et al. Spatiotemporal monitoring of subtropical urban forests in mitigating air pollution: Policy implications for nature-based solutions. Ecol. Indic. 2024, 158, 111386. [Google Scholar] [CrossRef]
  23. Takano, A.; Rybak, J.; Veras, M.M. Bioindicators and human biomarkers as alternative approaches for cost-effective assessment of air pollution exposure. Front. Environ. Eng. 2024, 3, 1346863. [Google Scholar] [CrossRef]
  24. Crisan, F. The significance of epiphytic lichens as bioindicators of air pollution for human health. Proc. Rom. Acad. 2023, 25, 167–174. [Google Scholar]
  25. Lin, D.; Meng, J.W.; Li, M.N.; Wu, Q.F.; Wang, L.P.; Li, X.J.; Song, J.J.; Zhao, L.C.; Xu, P.; Xia, Y.; et al. Active biomonitoring of atmospheric element deposition using Evernia mesomorpha in Tangshang, China. Ecol. Environ. Res. 2023, 22, 1191–1205. [Google Scholar] [CrossRef]
  26. Abas, A.; Sulaiman, N.; Adnan, N.R.; Aziz, S.A.; Nawang, W.N. Utilisation du lichen (Dirinaria sp.) Comme bioindicateur de métaux lourds en suspension dans l’air dans certaines zones industrielles en Malaisie. Environ. Asia 2019, 12, 85–90. [Google Scholar]
  27. Frati, L.; Brunialti, G. Recent Trends and Future Challenges for Lichen Biomonitoring in Forests. Forests 2023, 14, 647. [Google Scholar] [CrossRef]
  28. Allajbeu, S.; Yushin, N.S.; Qarri, F.; Duliu, P.L.; Frontasyeva, M.V. Atmospheric deposition of rare earth elements in Albania studied by the moss biomonitoring technique, neutron activation analysis and GIS technology. Environ. Sci. Pollut. Res. 2016, 23, 14087–14101. [Google Scholar] [CrossRef]
  29. Abas, A. A systematic review on biomonitoring using lichen as the biological indicator: A decade of practices, progress, and challenges. Ecol. Indic. 2021, 121, 107197. [Google Scholar] [CrossRef]
  30. Degola, F.; De Benedictis, M.; Petraglia, A.; Massimi, A.; Fattorini, L.; Sorbo, S.; Basile, A.; Di Toppi, L.S. A Cd/Fe/Zn-Responsive Phytochelatin Synthase is Constitutively Present in the Ancient Liverwort Lunularia cruciata (L.) Dumort. Plant Cell Physiol. 2014, 55, 1884–1891. [Google Scholar] [CrossRef]
  31. Agnan, Y.; Probst, A.; Séjalon-Delmas, N. Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas. Ecol. Indic. 2017, 72, 99–110. [Google Scholar] [CrossRef]
  32. Correa-Ochoa, M.A.; Vélez-Monsalve, L.C.; Saldarriaga-Molina, J.C.; Jaramillo-Ciro, M.M. Evaluation of the Index of Atmospheric Purity in an American tropical valley through the sampling of corticulous lichens in different phorophyte species. Ecol. Indic. 2020, 115, 106355. [Google Scholar] [CrossRef]
  33. Adie, P.A.; Kor, A.A.; Oklo, A.D.; Ikese, O.C. Funaria hygrometrica moss as Bio-indicator of Atmospheric Pollution of Polycyclic Aromatic Hydrocarbons (PAHs) in Makurdi-Nigeria: Occurrence and Sources. Int. J. Res. Sci. Innov. 2021, 8, 29–35. [Google Scholar] [CrossRef]
  34. Benítez, Á.; Medina, J.; Vásquez, C.; Loaiza, T.; Luzuriaga, Y.; Calva, J. Lichens and Bromeliads as Bioindicators of Heavy Metal Deposition in Ecuador. Diversity 2019, 11, 28. [Google Scholar] [CrossRef]
  35. Ancora, S.; Dei, R.; Rota, E.; Mariotti, G.; Bianchi, N.; Bargagli, R. Altitudinal variation of trace elements deposition in forest ecosystems along the NW side of Mt. Amiata (central Italy): Evidence from topsoil, mosses and epiphytic lichens. Atmos. Pollut. Res. 2021, 12, 101200. [Google Scholar] [CrossRef]
  36. Carrillo, W.; Calva, J.; Benítez, Á. The Use of Bryophytes, Lichens and Bromeliads for Evaluating Air and Water Pollution in an Andean City. Forests 2022, 13, 1607. [Google Scholar] [CrossRef]
  37. General Directorate of Forests. National Forest Inventory—Summary Report; Ministry of Agriculture: Tunis, Tunisia, 1998; p. 120. [Google Scholar]
  38. Arif, A. National Institute of Meteorology 1990–2015. Available online: http://www.meteo.tn (accessed on 5 June 2025).
  39. Barandovski, L.; Stafilov, T.; Šajn, R.; Frontasyeva, M.; Bačeva Andonovska, K. Atmospheric Heavy Metal Deposition in North Macedonia from 2002 to 2010 Studied by Moss Biomonitoring Technique. Atmosphere 2020, 11, 929. [Google Scholar] [CrossRef]
  40. Agnan, Y.; Séjalon-Delmas, N.; Probst, A. Comparing early twentieth century and present-day atmospheric pollution in SW France: A story of lichens. Environ. Pollut. 2013, 172, 139–148. [Google Scholar] [CrossRef]
  41. Legendre, P.; Legendre, L. Numerical Ecology, 3rd ed.; Elsevier: Amsterdam, The Netherlands, 2012. [Google Scholar]
  42. Kirschbaum, U.; Wirth, V. Les Lichens Bio-Indicateurs: Les Reconnaître, Évaluer la Qualité de L’Air; Editions Ulmer: Paris, France, 1997; p. 128. [Google Scholar]
  43. Poličnik, H.; Simončič, P.; Batič, F. Monitoring air quality with lichens: A comparison between mapping in forest sites and in open areas. Environ. Pollut. 2008, 151, 395–400. [Google Scholar] [CrossRef]
  44. Koroleva, Y.; Abdo, S.; Kaniki, A.; Kanda, J.M.; Alleman, L.Y. Mapping and Spatial Prediction of Atmospheric Deposition in Moss Samples: A Study in the Kaliningrad Region, Russia. Arab. J. Geosci. 2023, 16, 651. [Google Scholar]
  45. Clauzade, J.; Roux, J.P. La végétation de la région méditerranéenne. Ecol. Mediterr. 1985, 11, 1–20. [Google Scholar]
  46. Tiévant, M. Flore et végétation des Alpes françaises. Ecol. Mediterr. 2001, 27, 33–47. [Google Scholar]
  47. Pichler, G.; Muggia, L.; Candotto Carniel, F.; Grube, M.; Kranner, I. How to build a lichen: From metabolite release to symbiotic interplay. New Phytol. 2023, 238, 1362–1378. [Google Scholar] [CrossRef] [PubMed]
  48. Norme No. VDI-Richtlinien 3799, Blatt 1; Verein Deutscher Ingenieure (VDI). Messen von Immissionswirkungen—Ermittlung und Beurteilung Phytotoxischer Wirkungen von Immissionen mit Flechten—Flechtenkartierung zur Ermittlung des Luftgütewertes (LGW). Engl. VDI/DIN-Kommission Reinhaltung der Luft (KRdL)—Normenausschuss: Berlin, Germany, 1995.
  49. El Rhzaoui, G.E.; Divakar, P.K.; Crespo, A.M.; Tahiri, H.; El Alaoui-Faris, F.E. Xanthoria parietina as a biomonitor of airborne heavy metal pollution in forest sites in the North East of Morocco. Lazaroa 2015, 36, 31–41. [Google Scholar] [CrossRef]
  50. Occelli, F.; Cuny, M.A.; Devred, I. Étude de l’imprégnation de l’environnement de trois bassins de vie de la région Nord-Pas-de-Calais par les éléments Traces Métalliques. Pollut. Atmosphérique 2013, 220, 15–25. [Google Scholar] [CrossRef]
  51. Yushin, N.; Chaligava, O.; Zinicovscaia, I.; Vergel, K.; Grozdov, D. Mosses as Bioindicators of Heavy Metal Air Pollution in the Lockdown Period Adopted to Cope with the COVID-19 Pandemic. Atmosphere 2020, 11, 1194. [Google Scholar] [CrossRef]
  52. Nimis, P.L.; Scheidegger, C.; Wolseley, P.A. (Eds.) Monitoring with Lichens—Monitoring Lichens (NATO Science Series: IV—Earth and Environmental Sciences); Kluwer Academic Publishers: Dordrecht, The Netherlands, 2002; Volume 7, p. 408. ISBN 1-4020-0429-X. [Google Scholar]
  53. Agilent Technologies. Agilent 5100 and 5110 ICP-OES User’s Guide, 6th ed.; Agilent Technologies: Santa Clara, CA, USA, 2018. [Google Scholar]
  54. Centre D’expertise en Analyses Environnementale du Québec. Détermination des Métaux Assimilables et du Phosphore: Méthode par Spectrométrie de Masse à Source Ionisante au Plasma D’argon, MA. 200—Mét-P ass. 1.0, Rév. 2; Ministère du Développement Durable, de l’Environnement et de la Lutte Contre les Changements Climatiques du Québec: Québec, QC, Canada, 2014; p. 15.
  55. Liu, Y.J.; Zhu, Y.G.; Ding, H. Lead and cadmium in leaf of deciduous trees in Beijing, China: Development of a metal accumulation index (MAI). Environ. Pollut. 2007, 145, 387–390. [Google Scholar] [CrossRef]
  56. Loppi, S. Les lichens en tant que sentinelles de la pollution atmosphérique dans les régions alpines reculées (Italie). Environ. Sci. Pollut. Res. 2014, 21, 2563–2571. [Google Scholar] [CrossRef]
  57. Giordani, P.; Brunialti, G.; Calderisi, M.; Malaspina, P.; Frati, L. Diversité bêta et similitude des communautés de lichens comme un signe des temps. Lichenologist 2018, 50, 371–383. [Google Scholar] [CrossRef]
  58. Brunialti, G.L.; Frati, C.; Malegori, P.; Giordani, P.; Malaspina, P. Des équipes différentes produisent-elles des résultats différents en matière de biosurveillance à long terme des lichens? Diversité 2019, 11, 43. [Google Scholar] [CrossRef]
  59. Daillant, O. Lichens et accumulation des métaux lourds. Taureau. Informer. Cul. Fr. Lichénologie 2003, 28, 31–43. [Google Scholar]
  60. Van Haluwyn, C.; Lerond, M. Les lichens et la qualité de l’air: Évolution méthodologique et limites. Bulletin de la Société Botanique de France. Actual. Bot. 1986, 133, 81–112. [Google Scholar]
  61. Seed, L.; Wolseley, P.; Gosling, L.; Davies, L.; Power, S.A. Modelling relationships between lichen bioindicators, air quality and climate on a national scale: Results from the UK OPAL air survey. Environ. Pollut. 2013, 182, 437–447. [Google Scholar] [CrossRef] [PubMed]
  62. Świsłowski, P.; Nowak, A.; Rajfur, M. Comparison of Exposure Techniques and Vitality Assessment of Mosses in Active Biomonitoring for Their Suitability in Assessing Heavy Metal Pollution in Atmospheric Aerosol. Environ. Toxicol. Chem. 2022, 41, 1429–1438. [Google Scholar] [CrossRef]
  63. Capozzi, F.; Sorrentino, M.C.; Granata, A.; Vergara, A.; Alberico, M.; Rossi, M.; Spagnuolo, V.; Giordano, S. Optimizing Moss and Lichen Transplants as Biomonitors of Airborne Anthropogenic Microfibers. Biology 2023, 12, 1278. [Google Scholar] [CrossRef]
  64. Brunialti, G.; Frati, L. Biomonitoring of nine elements by the lichen Xanthoria parietina in Adriatic Italy: A retrospective study over a 7-year time span. Sci. Total Environ. 2007, 387, 289–300. [Google Scholar] [CrossRef]
  65. Scerbo, R.; Ristori, T.; Possenti, L.; Lampugnani, L.; Barale, R.; Barghigiani, C. Lichen (Xanthoria parietina) biomonitoring of trace element contamination and air quality assessment in Pisa Province (Tuscany, Italy). Sci. Total Environ. 2002, 286, 27–40. [Google Scholar] [CrossRef]
  66. Çobanoğlu, G.; Kaan, T. Biomonitoring of Atmospheric heavy metals in native lichen Xanthoria parietina around Salda Lake (Burdur-Turkey), a special environmental protection area. Air Qual. Atmos. Health 2024, 17, 2789–2800. [Google Scholar] [CrossRef]
  67. International Agency for Research on Cancer (IARC). Monographs on the Identification of Carcinogenic Hazards to Humans: Arsenic and Arsenic Compounds. Available online: https://publications.iarc.fr/Book-And-Report-Series/Iarc-Monographs-On-The-Identification-Of-Carcinogenic-Hazards-To-Humans/Arsenic-Metals-Fibres-And-Dusts-2012 (accessed on 16 April 2025).
  68. Boonpeng, C.; Polyiam, W.; Sriviboon, C.; Jhampasri, T.; Watthana, S.; Sangvichien, E.; Boonpeng, K. Accumulation of inorganic polluants and photosynthetic responses of transplanted lichens at distances away from an industrial complex. Thai J. Bot. 2017, 9, 181–191. [Google Scholar]
  69. Shvetsova, M.S.; Kamanina, I.Z.; Frontasyeva, M.V.; Madadzada, A.I.; Zinicovscaia, I.I.; Pavlov, S.S.; Vergel, K.N.; Yushin, N.S. Active Moss Biomonitoring Using the “Moss Bag Technique” in the Park of Moscow. Phys. Part. Nucl. Lett. 2019, 16, 994–1003. [Google Scholar] [CrossRef]
  70. Han, Y.; Du, P.; Cao, J.; Posmentier, E.S.; Wang, Q.; Li, T.; Zhang, R. Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China. Sci. Total Environ. 2021, 355, 176–186. [Google Scholar]
  71. Cardoso-Gustavson, P.; Fernandes, F.F.; Alves, E.S.; Pereira Victorio, M.; Baesso Moura, B.; Domingos, M.; Albuquerque Rodrigues, C.; Portella Ribeiro, A.; Carvalho Nievola, C.; G Figueiredo, A.M. Tillandsia usneoides: A successful alternative for biomonitoring changes in air quality due to a new highway in São Paulo, Brazil. Environ. Sci. Pollut. Res. 2016, 23, 1779–1788. [Google Scholar] [CrossRef] [PubMed]
  72. Chrastný, V.; Šillerová, H.; Vítková, M.; Francová, A.; Jehlička, J.; Kocourková, J.; Aspholm, P.E.; Nilsson, L.O.; Berglen, T.F.; Jensen, H.K.B.; et al. Unleaded gasoline as a significant source of Pb emissions in the Subarctic. Chemosphere 2018, 193, 230–236. [Google Scholar] [CrossRef] [PubMed]
  73. Lombardi, G.; Cincinelli, A.; Martellini, T.; Katsoyiannis, A. Atmospheric deposition of trace metals in coastal urban environments: Sources, transport and trends. Environ. Pollut. 2017, 222, 240–250. [Google Scholar] [CrossRef]
  74. Di Palma, A.; Capozzi, F.; Spagnuolo, V.; Giordano, S.; Adamo, P. Atmospheric particulate matter intercepted by moss-bags: Relations to moss trace element uptake and land use. Chemosphere 2017, 176, 361–368. [Google Scholar] [CrossRef]
  75. Vitali, M.; Antonucci, A.M.; Owczarek, M.; Guidotti, M.; Astolfi, M.L.; Manigrasso, M.; Protano, C. Évaluation de la qualité de l’air dans différents scénarios environnementaux par la détermination de métaux lourds typiques et de polluants organiques persistants chez le lichen indigène Xanthoria parietina. Pollut. Environ. 2019, 254, 113013. [Google Scholar] [CrossRef]
  76. Marié, D.C.; Martino, L.; Chaparro, M.A.; D’Angelo, C.; Lavornia, J.M.; Böhnel, H.N. A moss species for magnetic biomonitoring the airborne particle pollution. Boletín Soc. Geológica Mex. 2024, 76, A040324. [Google Scholar]
  77. Salo, H.; Bućko, M.S.; Vaahtovuo, E.; Limo, J.; Mäkinen, J.; Pesonen, L.J. Biomonitoring of air pollution in SW Finland by magnetic and chemical measurements of moss bags and lichens. Geochem. Explor. 2012, 115, 69–81. [Google Scholar] [CrossRef]
  78. Kłos, A.; Ziembik, Z.; Rajfur, M.; Dołhańczuk-Śródka, A.; Bochenek, Z.; Bjerke, J.W.; Tømmervik, H.; Zagajewski, B.; Ziółkowski, D.; Jerz, D.; et al. Using moss and lichens in biomonitoring of heavy-metal contamination of forest areas in southern and north-eastern Poland. Sci. Total Environ. 2018, 627, 438–449. [Google Scholar] [CrossRef]
  79. Integrated Risk Information System (IRIS). Available online: https://www.epa.gov/iris (accessed on 19 March 2025).
  80. Kasongo, J.; Alleman, L.Y.; Kanda, J.M.; Kaniki, A.; Riffault, V. Metal-bearing airborne particles from mining activities: A review on their characteristics, impacts and research perspectives. Sci. Total Environ. 2024, 951, 175426. [Google Scholar] [CrossRef]
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Figure 1. Location of the monitoring sites (S1, S2, S3) within the Rimel Forest plantations (northern Tunisia). The geographical maps were made using Google Earth Pro software, version 7.3.6. The 25-year wind rise (1 January 1990 to 31 December 2015) shows the relative frequency of winds (in %) according to direction (radi rings) and velocity (color). Wind data were acquired at the Météo-Bizerte station. Note: Site 1 is close to the petrochemical zone, 3.7 km from the emission sources. Site 2 is in an intermediate zone at around 7.8 km. Site 3 is in the area that is least exposed to pollution and furthest from the industrial zone at 12.9 km.
Figure 1. Location of the monitoring sites (S1, S2, S3) within the Rimel Forest plantations (northern Tunisia). The geographical maps were made using Google Earth Pro software, version 7.3.6. The 25-year wind rise (1 January 1990 to 31 December 2015) shows the relative frequency of winds (in %) according to direction (radi rings) and velocity (color). Wind data were acquired at the Météo-Bizerte station. Note: Site 1 is close to the petrochemical zone, 3.7 km from the emission sources. Site 2 is in an intermediate zone at around 7.8 km. Site 3 is in the area that is least exposed to pollution and furthest from the industrial zone at 12.9 km.
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Figure 2. Positive linear relationship between the air quality index (AQI) and the distance from the source of pollution. Squares represent measured values. The solid line indicates the linear regression. Error bars represent ± standard deviation (SD) calculated from replicate measurements (n = 9, mean ± SD).
Figure 2. Positive linear relationship between the air quality index (AQI) and the distance from the source of pollution. Squares represent measured values. The solid line indicates the linear regression. Error bars represent ± standard deviation (SD) calculated from replicate measurements (n = 9, mean ± SD).
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Figure 3. Box-and-whisker plots of toxic metal (Fe, Zn, Pb, Cu, Cr) concentrations (µg/g dry mass) in X. parietina and F. hygrometrica for air quality within three surveyed sites (S1, S2, S3) from the Rimel Forest plantations, (n = 9, mean ± SD). Means followed by different letters statistically differ at p ≤ 0.05, according to Tukey HSD (Honestly Significant Difference) tests. Uppercase letters are for X. parietina, and lowercase letters are for F. hygrometrica. A box plot is a standardized graphical representation summarizing data distribution through key statistics like the median (central tendency), the interquartile range (representing the middle 50% of the data), and potential outliers.
Figure 3. Box-and-whisker plots of toxic metal (Fe, Zn, Pb, Cu, Cr) concentrations (µg/g dry mass) in X. parietina and F. hygrometrica for air quality within three surveyed sites (S1, S2, S3) from the Rimel Forest plantations, (n = 9, mean ± SD). Means followed by different letters statistically differ at p ≤ 0.05, according to Tukey HSD (Honestly Significant Difference) tests. Uppercase letters are for X. parietina, and lowercase letters are for F. hygrometrica. A box plot is a standardized graphical representation summarizing data distribution through key statistics like the median (central tendency), the interquartile range (representing the middle 50% of the data), and potential outliers.
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Figure 4. Box-and-whisker plots of toxic metal (Ni, As, Co, Cd) concentrations (µg/g dry mass) in X. parietina and F. hygrometrica for air quality within three surveyed sites (S1, S2, S3) from the Rimel Forest, (n = 9, mean ± SD). Means followed by different letters statistically differ at p ≤ 0.05, according to Tukey HSD (Honestly Significant Difference) tests. Uppercase letters are for X. parietina, and lowercase letters are for F. hygrometrica. A box plot is a standardized graphical representation summarizing data distribution through key statistics like the median (central tendency), the interquartile range (representing the middle 50% of the data), and potential outliers.
Figure 4. Box-and-whisker plots of toxic metal (Ni, As, Co, Cd) concentrations (µg/g dry mass) in X. parietina and F. hygrometrica for air quality within three surveyed sites (S1, S2, S3) from the Rimel Forest, (n = 9, mean ± SD). Means followed by different letters statistically differ at p ≤ 0.05, according to Tukey HSD (Honestly Significant Difference) tests. Uppercase letters are for X. parietina, and lowercase letters are for F. hygrometrica. A box plot is a standardized graphical representation summarizing data distribution through key statistics like the median (central tendency), the interquartile range (representing the middle 50% of the data), and potential outliers.
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Figure 5. Linear relationships between concentrations of toxic metals (Fe, Zn, Pb, Cu, Cr) (µg/g dry mass) in X. parietina and F. hygrometrica and distance from the pollution source.
Figure 5. Linear relationships between concentrations of toxic metals (Fe, Zn, Pb, Cu, Cr) (µg/g dry mass) in X. parietina and F. hygrometrica and distance from the pollution source.
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Figure 6. Linear relationships between the concentrations of toxic metals (Ni, As, Co, Cd) (µg/g dry mass) and distance from the source of pollution.
Figure 6. Linear relationships between the concentrations of toxic metals (Ni, As, Co, Cd) (µg/g dry mass) and distance from the source of pollution.
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Table 1. Distribution of lichen species in the study sites; presence (1); absence (0).
Table 1. Distribution of lichen species in the study sites; presence (1); absence (0).
Lichen SpeciesThallus TypeSite 1Site 2Site 3
Bacidia rubella (Hoffm.) A. Massal.Crustose111
Bactrospora patellarioides (Nyl.) Almq.Crustose100
Caloplaca cerina Ehrh. ex Hedw.Crustose110
Caloplaca ferruginea Huds.Crustose001
Chrysothrix candelaris (L.) J.R. LaundonLeprose011
Dendrographa decolorans (Turner & Borrer) Ertz &Tehler.Crustose111
Diploicia canescens (Dicks.) A. Massal.Crustose011
Dirina ceratoniae (Ach.) Fr.Crustose111
Evernia prunastri (L.) Ach.Fruticose001
Flavoparmelia caperata (L.) HaleFoliose001
Hyperphyscia adglutinata (Flörke) H. Mayrhofer & PoeltCrustose011
Lecania naegeli (Hepp.) Diedrich & van den BoomCrustose111
Lecanora argentata (Ach.) MalmeCrustose010
Lecanora expallens Ach.Crustose011
Lecanora chlarotera Nyl.Crustose111
Lecanora compallens Herk & AptrootLeprose011
Lecanora horiza (Ach.) Linds.Crustose011
Lecanora lividocinerea Bagl.Crustose011
Lecanora strobilina (Spreng). kieff.Crustose011
Lecidella elaoechroma (Ach.) M. ChoisyCrustose111
Micarea prasina Fr.Crustose111
Ocellomma picconianum (Bagl.) Ertz & TehlerCrustose110
Opegrapha atra Pers.Crustose011
Opegrapha celtidicola JattaCrustose100
Opegrapha herbarum Mont.Crustose011
Opegrapha niveoatra (Borrer) J.R.LaundonCrustose111
Opegrapha vulgata (Ach.) Ach.Crustose111
Parmelina tiliacea (Hoffm.) HaleFoliose001
Parmotrema hypoleucinum (J. Steiner) HaleFoliose001
Parmotrema perlatum (Huds.) M.ChoisyFoliose001
Physcia adscendens H. OlivierFoliose111
Physcia tenella (Scop.) DC.Foliose111
Pyrrhospora quernea (Dicks.) Krộb. Crustose011
Ramalina farinacea (L.) Ach.Fruticose001
Ramalina lacera (With.) J.R. LaundonFruticose011
Rinodina pruinella Bagl.Crustose011
Thelopsis isiaca Stizenb.Crustose110
Xanthoria parietina (L.) Th.Fr.Foliose111
Specific richness 172932
Species abundance 3.654.827.66
Shannon index (H’) 3.763.884.7
AQI 36.54476.6
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Bousbih, M.; Lamhamedi, M.S.; Abassi, M.; Khasa, D.P.; Bejaoui, Z. Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations. Environments 2025, 12, 191. https://doi.org/10.3390/environments12060191

AMA Style

Bousbih M, Lamhamedi MS, Abassi M, Khasa DP, Bejaoui Z. Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations. Environments. 2025; 12(6):191. https://doi.org/10.3390/environments12060191

Chicago/Turabian Style

Bousbih, Malek, Mohammed S. Lamhamedi, Mejda Abassi, Damase P. Khasa, and Zoubeir Bejaoui. 2025. "Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations" Environments 12, no. 6: 191. https://doi.org/10.3390/environments12060191

APA Style

Bousbih, M., Lamhamedi, M. S., Abassi, M., Khasa, D. P., & Bejaoui, Z. (2025). Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations. Environments, 12(6), 191. https://doi.org/10.3390/environments12060191

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