**5. Recent advances in anti-plaque agents: Chemoprophylactic agents, antimicrobial peptides, anti-quorum sensing approach and probiotics/replacement therapy**

Control of oral biofilms is essential for maintaining oral health and preventing dental caries, gingivitis and periodontitis. However, oral biofilms are not easily controlled by mechanical means and represent difficult targets for chemical control (Socransky, 2002). With the exception of chlorhexidine and fluoride, few of the existing oral prophylactic agents have significant effects (Petersen & Scheie, 1998; Wu & Savitt, 2002; Scheie, 2003). A likely explanation for this low efficiency is due to the fact that microorganisms organized in biofilms possess characteristics that differentiate them from planktonic cells, such as higher

which is positively charged, binds tightly to negatively charged bacteria membrane, causing

Fig. 2. Scanning electron micrograph of planktonic *S. mutans* UA159 cells exposed to chlorhexidine at 4.5 µg/ml. After 6h of CHX incubation, several wilted cells with spilled

*Inhibition of nucleic acids synthesis.* Some antibiotics interfere with the processes of DNA transcription and replication of microorganisms. Some drugs with this mode of action have limited use due to interference with DNA and RNA of mammals. Others, such as rifampin and quinolones, are more widely used in chemotherapy by having a higher degree of

*Inhibition of Synthesis of Essential Metabolites*. The enzymatic activity of a specific microorganism can be competitively inhibited by a substance (antimetabolites) that closely

Control of oral biofilms is essential for maintaining oral health and preventing dental caries, gingivitis and periodontitis. However, oral biofilms are not easily controlled by mechanical means and represent difficult targets for chemical control (Socransky, 2002). With the exception of chlorhexidine and fluoride, few of the existing oral prophylactic agents have significant effects (Petersen & Scheie, 1998; Wu & Savitt, 2002; Scheie, 2003). A likely explanation for this low efficiency is due to the fact that microorganisms organized in biofilms possess characteristics that differentiate them from planktonic cells, such as higher

**5. Recent advances in anti-plaque agents: Chemoprophylactic agents,**

intracellular material could be observed (15 kV and 13.000x of magnification).

resembles enzyme's normal substrate (Russell and Hugo, 1994).

**antimicrobial peptides, anti-quorum sensing approach and**

its disruption (Gilbert & Moore, 2005).

selective toxicity (Silver, 1967).

**probiotics/replacement therapy**

resistance to several antimicrobial agents; most studies so far use study models with planktonic cells, not reproducing the reality of the oral cavity. In addition, antimicrobials for oral use must have adequate diffusion in biofilms to be effective (Marsh, 2005).

Thus, many of these studies need to be revalidated, taking into account the oral environment. Recent approaches to the study of microbial gene expression and regulation in non-oral microorganisms have elucidated systems for transduction of stimuli in biofilms, such as two-component systems and quorum sensing (two-component and quorumsensing systems) that allow the coordinated gene expression in these structures. These studies based on understanding the regulation and expression in microbial biofilms can potentially benefit the development of new strategies for prevention and treatment of diseases caused by oral biofilms. Thus, the intervention should be directed at targets such as surface adhesion, colonization, co-adhesion, metabolism, growth, adaptation, maturation, climax community and detachment, and strategies must be based on surface modification, immunization, replacement therapy , interference with two-component systems and quorum sensing (Scheie, 2004).

These new drugs must be highly specific, have little ability to induce resistance in microorganisms and produce minimal effects on vital functions of human cells. In therapeutic approaches, the main target should be the mature and established biofilm. In this case, genes and proteins essential for viability of microorganisms represent the traditional targets for designing these antimicrobial drugs. Among these potential agents are included bacteriophages, inhibitors of the biosynthesis of fatty acids and antimicrobial peptides (Hancock, 1999, Payne et al. 2001; Sulakvelidze & Morris, 2001). In prophylactic approaches, the main targets are the pathogenic microorganisms directly involved in the formation of mono or multi-species biofilms. Promising targets for this purpose would be the two-component systems and quorum sensing, whose inference could be used to ensure the ecological balance in the biofilm, allowing the maintenance of health-related microbiota (Marsh, 2010). This approach would have a selective toxicity, since these systems are present in most microorganisms, but not in mammalian cells, which use other mechanisms of signal transduction.

Another important strategy is the modification of tooth surface or, more precisely, the film acquired from the enamel to prevent bacterial colonization and thus biofilm formation. The film acquired from enamel has binding sites for oral bacteria through specific and nonspecific binding mechanisms. An in vitro study showed that the combination of alkylphosphate and a nonionic surfactant changes the characteristics of tooth surface, making it less attractive for microorganisms. However, the clinical efficacy of these agents has been low, probably due to difficulties in obtaining the active components of these agents (Olsson, 1998).

Some properties of topical antimicrobial agents for oral use are essential to their success as high substantivity in the oral sites of biological action, low acute and chronic toxicity, and low permeability, being overall associated with their mechanism of action. Clinical activity of the antimicrobial agent depends on the drug formulation that must have a quick and efficient release vehicle. The supragingival plaque, film acquired from enamel and saliva may be primary sites of action for these agents, but the detailed understanding of these interactions is limited. These antimicrobials are retained by electrostatic bonds to carboxylic

Microbial Dynamics and Caries: The Role of Antimicrobials 213

functions or virulence. Efforts are being made for manufacturing active and passive vaccines, especially for tooth decay. In active immunization, an attenuated antigen induces a protective immune response when administered. In passive immunization, the ready

Studies on animals and humans using approaches with active and passive immunization have been successful, especially in passive immunization where there is impediment to recolonization of microorganisms related to dental caries (Koga et al. 2002; Smith, 2002) and also in periodontal disease (Booth et al., 1996). The vehicles for passive immunization, such as milk from immunized cows (Shimazaki et al., 2001) and transgenic plants (Ma et al., 1998), have been tested with promising results. Similarly, it was shown that recombinant chimeric microbial vectors that are non-virulent, but express antigens of *S. mutans* (Huang et al., 2001, Taubman et al., 2001) or *P. gingivalis* (Sharma et al., 2001) promoted protection

One of the issues that still need to be solved is about which immune system should be stimulated, if the systemic immune system or that associated with mucosal. In the case of an anti-caries vaccine, it would be more interesting the oral administration and based on induction of the immune system associated with mucosa. A vaccine against periodontal disease should probably involve the systemic immune system and that associated with mucosa. A major problem is that approaches to immunization are usually directed against epitopes of isolate bacteria; however, both tooth decay and periodontal disease are diseases whose etiologic agent consists of a multispecies microbiota (Marsh, 1994). Moreover, since microorganisms have the ability to form biofilms and adapt to this environment, this can lead to changes in antigenicity which could affect the durability of protection induced by

An alternative approach are a new class of antibiotics called of antimicrobial peptides (AMP) that can be used against these microorganisms (White et al., 1995, Yount & Yeaman, 2004; Hancock & Sahl, 2006; Gardy et al., 2009); however, a poor understanding of the fundamental principles of the action mechanisms and structure-activity relationship of these drugs (Shai, 2002; Bechinger, 2009) has reduced the development of MPAs that can be used clinically.

The potential advantages of using antimicrobial peptides as antimicrobial drugs are significant (Hamill et al., 2008, Easton et al., 2009). They have a broad spectrum of activity against many strains of Gram positive and negative bacteria, including strains resistant to other drugs, and are also active against some fungi. Moreover, their interactions with bacterial components do not involve binding sites to specific proteins and thus do not induce resistance. The AMP bioavailability is reduced because they cannot be taken orally; however, topical applications and injections are available. Recently, AMP has been tested in clinical trials for various applications in oral candidiasis (Demegen Pharmaceuticals, 2010), infections associated with catheters (Melo et al., 2006) and infections in implant surfaces (Kazemzadeh-Narbat et al., 2010). These clinically tested AMPs are derived from natural peptides, this fact being responsible for its biggest drawback, which is the high production cost compared to other chemical antibiotics. For this reason, there is a critical need for development of new AMPs, powerful, small and with simple composition. The potential for the rational design of these drugs is often limited due to little knowledge about the details of

against tooth decay and loss of alveolar bone in experimental animals.

antibody is administered (Sheie, 2004).

immunization.

its mechanism of action.

acids and phosphate and sulfate residues of proteins and glycoproteins in the oral mucosa, film acquired from enamel and plaque. The non-ionic antibacterials are retained by adsorption to lipophilic regions in these receptor sites. The ability of these antiplaque agents have to keep an optimal concentration in saliva over a long period, in addition to remaining in the bioactive form at the action sites, such as the teeth surfaces is extremely important and influence in the clinical effectiveness of these agents (Cummins & Creeth , 1992).

The analysis of retention characteristics and antimicrobial properties of clinically proven antiplaque agents suggest that they act multifunctionally and at multiple sites. Thus, they reduce growth and metabolism of bacteria in plaque, saliva and tooth surface, but also reduce the adhesion of potential settlers. Two generic routes to increase the antiplaque activity of these agents have received attention. Firstly the use of a combination of antimicrobial agents with similar but complementary activities uses only one route and mode of action. A second potential route is the use of a polymer that serves as auxiliary retention of only one antimicrobial used (Cummins, 1991b).

The total oral retention, salivary profile and agent concentrations on the plaque, film acquired from enamel and oral mucosa are not only indicators of biological activity in vivo, but they serve as potential indicators for this activity. This means that the increased release of a specific agent in vivo is not predictive of its increased clinical efficacy (Cummins, 1991b). The increased activity on the site or sites of biological action combined with agent's residence time in the oral cavity are the best predictors of agent's clinical activity (Creeth & Cummins, 1992).

Replacement therapy has been suggested as a strategy for replacement of pathogenic microorganisms modified to become less virulent. Some requirements for this type of approach are important, such as: the replaced organism must not cause disease by itself; it must persistently colonize and must possess a high degree of genetic stability. DNA technology has enabled to produce potential candidates for replacement therapy in the prevention of dental caries. Among these, there is the super-colonizing strain of *S. mutans*. This strain produces mutacin, which allows it to replace the wild-type strain efficiently. It lacks the enzyme lactate dehydrogenase and therefore is unable to produce lactate (Hillman et al., 2000). Other ureolitic recombinant strains have been constructed and are capable of hydrolyzing urea to ammonia, thereby offsetting the environment acidification (Clancy et al., 2000).

A possible future approach would be to use genetically modified microorganisms for releasing molecules that could interfere with pathways such as signal transduction of twocomponent and quorum sensing. However, it is important to emphasize that there is the possibility of a genetically modified strain subsequently undergoes transformation in oral biofilms and then becomes a pathogenic opportunistic strain (Scheie, 2004).

Immunization against oral diseases as dental caries and periodontal disease has been extensively studied in recent decades (Koga et al. 2002; Smith, 2002). The goal would be inhibiting or reducing the virulence of some microbial etiological agents. Several molecules involved in various stages of the pathogenesis of caries and periodontal disease could be susceptible to immune intervention and serve as targets for production of vaccines. Thus, it would be possible to eliminate microorganisms of the oral cavity with antibodies able to block adhesins or receptors involved in adhesion, or metabolically modify important

acids and phosphate and sulfate residues of proteins and glycoproteins in the oral mucosa, film acquired from enamel and plaque. The non-ionic antibacterials are retained by adsorption to lipophilic regions in these receptor sites. The ability of these antiplaque agents have to keep an optimal concentration in saliva over a long period, in addition to remaining in the bioactive form at the action sites, such as the teeth surfaces is extremely important and

The analysis of retention characteristics and antimicrobial properties of clinically proven antiplaque agents suggest that they act multifunctionally and at multiple sites. Thus, they reduce growth and metabolism of bacteria in plaque, saliva and tooth surface, but also reduce the adhesion of potential settlers. Two generic routes to increase the antiplaque activity of these agents have received attention. Firstly the use of a combination of antimicrobial agents with similar but complementary activities uses only one route and mode of action. A second potential route is the use of a polymer that serves as auxiliary

The total oral retention, salivary profile and agent concentrations on the plaque, film acquired from enamel and oral mucosa are not only indicators of biological activity in vivo, but they serve as potential indicators for this activity. This means that the increased release of a specific agent in vivo is not predictive of its increased clinical efficacy (Cummins, 1991b). The increased activity on the site or sites of biological action combined with agent's residence time in the oral cavity are the best predictors of agent's clinical activity (Creeth &

Replacement therapy has been suggested as a strategy for replacement of pathogenic microorganisms modified to become less virulent. Some requirements for this type of approach are important, such as: the replaced organism must not cause disease by itself; it must persistently colonize and must possess a high degree of genetic stability. DNA technology has enabled to produce potential candidates for replacement therapy in the prevention of dental caries. Among these, there is the super-colonizing strain of *S. mutans*. This strain produces mutacin, which allows it to replace the wild-type strain efficiently. It lacks the enzyme lactate dehydrogenase and therefore is unable to produce lactate (Hillman et al., 2000). Other ureolitic recombinant strains have been constructed and are capable of hydrolyzing urea to ammonia, thereby offsetting the environment acidification (Clancy et

A possible future approach would be to use genetically modified microorganisms for releasing molecules that could interfere with pathways such as signal transduction of twocomponent and quorum sensing. However, it is important to emphasize that there is the possibility of a genetically modified strain subsequently undergoes transformation in oral

Immunization against oral diseases as dental caries and periodontal disease has been extensively studied in recent decades (Koga et al. 2002; Smith, 2002). The goal would be inhibiting or reducing the virulence of some microbial etiological agents. Several molecules involved in various stages of the pathogenesis of caries and periodontal disease could be susceptible to immune intervention and serve as targets for production of vaccines. Thus, it would be possible to eliminate microorganisms of the oral cavity with antibodies able to block adhesins or receptors involved in adhesion, or metabolically modify important

biofilms and then becomes a pathogenic opportunistic strain (Scheie, 2004).

influence in the clinical effectiveness of these agents (Cummins & Creeth , 1992).

retention of only one antimicrobial used (Cummins, 1991b).

Cummins, 1992).

al., 2000).

functions or virulence. Efforts are being made for manufacturing active and passive vaccines, especially for tooth decay. In active immunization, an attenuated antigen induces a protective immune response when administered. In passive immunization, the ready antibody is administered (Sheie, 2004).

Studies on animals and humans using approaches with active and passive immunization have been successful, especially in passive immunization where there is impediment to recolonization of microorganisms related to dental caries (Koga et al. 2002; Smith, 2002) and also in periodontal disease (Booth et al., 1996). The vehicles for passive immunization, such as milk from immunized cows (Shimazaki et al., 2001) and transgenic plants (Ma et al., 1998), have been tested with promising results. Similarly, it was shown that recombinant chimeric microbial vectors that are non-virulent, but express antigens of *S. mutans* (Huang et al., 2001, Taubman et al., 2001) or *P. gingivalis* (Sharma et al., 2001) promoted protection against tooth decay and loss of alveolar bone in experimental animals.

One of the issues that still need to be solved is about which immune system should be stimulated, if the systemic immune system or that associated with mucosal. In the case of an anti-caries vaccine, it would be more interesting the oral administration and based on induction of the immune system associated with mucosa. A vaccine against periodontal disease should probably involve the systemic immune system and that associated with mucosa. A major problem is that approaches to immunization are usually directed against epitopes of isolate bacteria; however, both tooth decay and periodontal disease are diseases whose etiologic agent consists of a multispecies microbiota (Marsh, 1994). Moreover, since microorganisms have the ability to form biofilms and adapt to this environment, this can lead to changes in antigenicity which could affect the durability of protection induced by immunization.

An alternative approach are a new class of antibiotics called of antimicrobial peptides (AMP) that can be used against these microorganisms (White et al., 1995, Yount & Yeaman, 2004; Hancock & Sahl, 2006; Gardy et al., 2009); however, a poor understanding of the fundamental principles of the action mechanisms and structure-activity relationship of these drugs (Shai, 2002; Bechinger, 2009) has reduced the development of MPAs that can be used clinically.

The potential advantages of using antimicrobial peptides as antimicrobial drugs are significant (Hamill et al., 2008, Easton et al., 2009). They have a broad spectrum of activity against many strains of Gram positive and negative bacteria, including strains resistant to other drugs, and are also active against some fungi. Moreover, their interactions with bacterial components do not involve binding sites to specific proteins and thus do not induce resistance. The AMP bioavailability is reduced because they cannot be taken orally; however, topical applications and injections are available. Recently, AMP has been tested in clinical trials for various applications in oral candidiasis (Demegen Pharmaceuticals, 2010), infections associated with catheters (Melo et al., 2006) and infections in implant surfaces (Kazemzadeh-Narbat et al., 2010). These clinically tested AMPs are derived from natural peptides, this fact being responsible for its biggest drawback, which is the high production cost compared to other chemical antibiotics. For this reason, there is a critical need for development of new AMPs, powerful, small and with simple composition. The potential for the rational design of these drugs is often limited due to little knowledge about the details of its mechanism of action.

Microbial Dynamics and Caries: The Role of Antimicrobials 215

number of *S. mutans* in the mouth (Caglar et al., 2008) in order to decrease the incidence of tooth decay. Other bacterium normally found in the mouth, and important in this sense, is *Streptococcus salivarius*, which produces a bacteriocin that inhibits anaerobic Gram-negative bacteria and that in vivo was shown also to reduce the level of halitosis (Burton et al., 2006). In conclusion, microbiota analysis methods independent of culture have allowed to understanding the diversity of the oral microbiota. So far, most studies have focused on the microbiota composition in the disease, but a better understanding of this microflora in health is required and also as probiotic organisms are capable of restoring and maintaining health in such environment. Thus, future studies are still needed, especially for analyzing the interactions between species and how to use this knowledge to develop new products

Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. (2005). Defining the normal bacterial

Ahmer BMM. (2004). Cell-to cell signaling on Escherichia coli and Salmonella enterica. *Mol*

Al-Hashimi I, Levine MJ. (1989). Characterization of in vivo salivary-derived enamel

Bassler BL. (2002). Small talk. Cell-to-cell communication in bacteria. *Cell*, Vol. 109, p. 421-

Bechinger B. (2009). Rationalizing the membrane interactions of cationic amphipathic

Bjarnsholt T, Jensen PO, Burmolle M, Hentzer M, Haagensen JA, Hougen HP, et al. (2005).

Booth V, Ashley FP, Lehner T (1996). Passive immunization with monoclonal antibodies

Bowden GH, Hardie JM, Slack GL. (1975). Microbial variations in approximal dental plaque.

Bowden GH. (1999). Controlled environment model for accumulation of biofilms of oral

Bradshaw DJ, Marsh PD, Schilling KM, Cummins D. (1996). A modified chemostat system to study the ecology of oral biofilms. *J Appl Bacteriol*, Vol. 80, No. 2, pp. 124-30. Bradshaw DJ, Marsh PD, Watson GK, Allison C. (1998). Role of Fusobacterium nucleatum

communities during aeration. *Infect Immun*, Vol. 66, No. 10, pp. 4729-32. Broadley SJ, Jenkins PA, Furr JR, Russell AD. (1995). Potentiation of the effects of

ethambutol. *Journal of Medical Microbiology*, Vol. 43, pp. 458–460.

antimicrobial peptides by their molecular shape. *Current Opinion in Colloid &*

*Pseudomonas aeruginosa* tolerance to tobramycin, hydrogen peroxide and polymorphonuclear leukocytes is quorum-sensing dependent. *Microbiology*; Vol.

against *Porphyromonas gingivalis* in patients with periodontitis. *Infect Immun,* Vol.

and coaggregation in anaerobe survival in planktonic and biofilm oral microbial

chlorhexidine diacetate and cetylpyridinium chloride on mycobacteria by

flora of the oral cavity. *J Clin Microbiol*, Vol. 43, n. 11, pp. 5721-32.

for prevention and treatment of oral diseases.

*Microbiol*, Vol. 52, p.933-945.

151:(Pt 2), pp. 373−83.

64, pp. 422-427.

pellicle. *Arch Oral Biol*, Vol. 34, n. 4, pp. 289-95.

*Interface Science*, Vol. 14, pp. 349–355.

*Caries Res*, Vol. 9, n. 4, pp. 253-77.

bacteria. *Methods Enzymol*, Vol. 310, pp. 216-24.

**7. References**

424.

Since tooth decay is an infection, it would be logical to treat the disease with antibiotics or antimicrobials, such as antimicrobial peptides. However, most of these agents are not selective, have broad spectrum of action on the microorganisms such as chlorhexidine, iodopovidine, fluoride, penicillin or other antimicrobial/antibiotics. Importantly, the agents described above does not sterilize the oral cavity, since it is exposed to the external environment where there are many microbes, it is not a sterile space. Thus, the use of broadspectrum agents for treating dental caries can suppress the infection, but will never eliminate it entirely (Luoma et al., 1978). In this context, due to the limitations of traditional strategies in the management of dental caries, a "probiotic" approach of the disease is necessary. The term "probiotic" used here means that mechanisms are used to selectively remove only the pathogen responsible for disease in an attempt to keep the oral ecosystem intact. Most efforts in this sense are derived from studies that have attempted to genetically modify strains of *Streptococcus mutans*, turning them into strains that in addition to not producing acids, still competing for the same ecological niche that wild strain of *S. mutans*  (Hillman, 2002).

In theory and experimentally in laboratory animals, when this substitute organism is introduced, it completely shifts the wild *S. mutans* causing the disease. This action stops the decay process and also prevents the re-emergence of disease-causing organisms, eliminating the possibility of re-infection, since the "normal microbiota is complete." Another way to remove pathogens is developing specific antimicrobials for certain targets (Eckert et al., 2006). The basic principle is developing a cheap molecule that targets only the organism of interest, in this case *S. mutans, S sobrinus*, or other pathogens.

In the case of the oral cavity and tooth decay, this system is attractive from the perspective of eliminating all pathogens, thus preventing the re-growth of the original infection. There are also laboratory and clinical evidence demonstrating that when the biofilm's bacterial ecosystem is free of *S. mutans*, this bacterium finds it difficult to be reintroduced due to competitive inhibition with other microorganisms (Keene & Shklair, 1974; Shi, 2005). A criticism to probiotic approaches is that they target only one of the pathogens involved with the disease, being not directed at other pathogens that may be involved with the beginning of the process, as the case of dental caries.


**Identifying Sources and Assessing Potential Risk of Exposure to Heavy Metals and Hazardous Materials in Mining Areas: The Case Study of Panasqueira Mine (Central Portugal) as an Example**

#### **Carla Candeias, Eduardo Ferreira da Silva, Paula F. Ávila and João Paulo Teixeira**

**Abstract:** The Sn-W Panasqueira mine, in activity since the mid-1890s, is one of the most important economic deposits in the world. Arsenopyrite is the main mineral present as well as rejected waste sulphide. The long history is testified by the presence of a huge amount of tailings, which release considerable quantities of heavy metal(loid)s into the environment. This work assesses soil contamination and evaluates the ecological and human health risks due to exposure to hazardous materials. The metal assemblage identified in soil (Ag-As-Bi-Cd-Cu-W-Zn; potentially toxic elements (PTEs)) reflects the influence of the tailings, due to several agents including aerial dispersion. PTEs and pH display a positive correlation confirming that heavy metal mobility is directly related to pH and, therefore, affects their availability. The estimated contamination factor classified 92.6% of soil samples as moderately to ultra-highly polluted. The spatial distribution of the potential ecological risk index classified the topsoil as being of a very high ecological risk, consistent with wind direction. Non-carcinogenic hazard of topsoil, for children (1–6 years), showed that for As the non-carcinogenic hazard represents a high health risk. The carcinogenic risks, both for children and adult alike, reveal a very high cancer risk mostly due to As ingestion.

Reprinted from *Geosciences.* Cite as: Candeias, C.; da Silva, E.F.; Ávila, P.F.; Teixeira, J.P. Identifying Sources and Assessing Potential Risk of Exposure to Heavy Metals and Hazardous Materials in Mining Areas: The Case Study of Panasqueira Mine (Central Portugal) as an Example. *Geosciences* **2014**, *4*, 240-268.

#### **1. Introduction**

Mine tailings, with considerable amounts of sulfides, left in the vicinity of environmentally sensitive locations, constitute one of the greatest threats to the surrounding environment. These materials when exposed to air and water are oxidized through chemical, electrochemical, and biological reactions, forming ferric hydroxides and sulfuric acid, leading to the generation of acid mine drainage with high contents of metals and sulfates, related to the alteration of sulfides, the equilibrium of which depends on their solubility [1–5].

Soil is prone to contamination both from hydrological and atmospheric sources. When soil is the receptor of tailings drainage, originating from metal mining and smelting, this waste disposal causes a major impact, and poses serious environmental concerns [6]. As a direct result of the mining activities, soil is generally, affected over a considerable area. The soil fine fraction is usually enriched in metals, due to the relative large surface area of fine particles that facilitate adsorption and metal binding to iron and manganese oxides and to organic matter [7,8]. Wind-blown dust originating from polluted soil is responsible for the aerial dispersion of trace metals [7]. Exposure to these hazardous elements may have different pathways, e.g., through the direct ingestion of soils and dust, ingestion of vegetables grown on contaminated soil or dust adhering to plants or dust inhalation. According to several authors [9–14] the studies dealing with the bioavailability and bioaccessibility of metal(loid) contaminants in highly-polluted soil are extremely useful to understand the possible effect on biota, and particularly on human health due to the exposure to these contaminants [12,15].

Among the purposes of environmental analysis are the determination of the geochemical background and natural concentrations of the chemical constituents in environment-background monitoring, as well as to determine the concentration of harmful pollutants in environment-pollution monitoring [16]. The sorption-desorption soil characteristics generally control the mobility and availability of heavy metals [17]. Heavy metal availability in soil depends on a number of factors, including Soil Organic Matter (SOM) and pH [18]. Soil pH plays the most important role in determining metal speciation, solubility from mineral surfaces, movement and bioavailability of metals [19–21]. Several laboratory experiments have shown that heavy metal mobility and availability have a negative correlation with pH [22]. Further, [23–25] documented that metal mobility and availability increases with the decrease of soil pH, thus enhancing the uptake of heavy metals by plants and, thereby, posing a threat to human health [26].

Exposure to increasing amounts of metal(loid)s in environmental and occupational settings is a reality worldwide, affecting a significant number of individuals. Most metal(loid)s are very toxic to living organisms and even those considered as essential can be toxic when in excess. They can disturb important biochemical processes, constituting an important threat for human health. Major health effects include development retardation, endocrine disruption, kidney damage, immunological and neurological effects, and several types of cancer [27]. The identification of potential threats to human health and natural ecosystems is useful information [16]. The quantification of all the types of risks and the determination of the total risk of metal(loid)s to the exposed population through oral intake, inhalation and dermal contact [28] is also very important. Risk assessment is typically a multistep process of identifying, defining, and characterizing potentially adverse consequences of exposure to hazardous materials [28]. According to the Toxic Substances Portal [29], Ag, As, Cd, Cu, W and Zn are known to be toxic to humans, while arsenic and cadmium are classified as human carcinogens. Some studies also consider that Bi causes acute toxicity, and large doses can be fatal [30,31]. However, as Bi is considered to be one of the less toxic heavy metals, it is not included in this analysis.

In a previous paper from the same authors [32], several variables (Ag, As, Bi, Cd, Cu, W and Zn) showed moderate to strong correlation in the Panasqueira topsoil. This indicates an anthropogenic origin, especially linked to aerial transportation and deposition and/or to a geogenic origin. The main goals of the present study are: (a) establishment of the relationship between Potentially Toxic Elements (PTEs) with depth and soil pH; (b) assessment of soil contamination using a contamination factor for each pollutant; and (c) determination and evaluation of the ecological and human health risks due to exposure to hazardous materials.

#### **2. Materials and Methods**

#### *2*.*1*. *Study Area*

The active Panasqueira mine, exploited since the last decade of the 19th century, is located in Central Portugal (UTM (Universal Transverse Mercator) 29N, P 4445620.79, M 606697.31; Figure 1). It is considered to have the largest Sn-W deposit of Western Europe [33]. The geology has been extensively studied by many researchers [34–44]. Briefly, the Panasqueira deposit is a classic example of postmagmatic hydrothermal ore deposit, which is associated with Hercynian plutonism [36,41]. The paragenesis is complex with four stages of mineral formation identified: 1st stage, the oxide silicate phase (quartz, wolframite; cassiterite); 2nd phase, the main sulphide phase (pyrite, arsenopyrite, pyrrhotite, sphalerite, chalcopyrite); 3rd stage, the pyrrhotite alteration phase (marcasite, siderite, galena, Pb-Bi-Ag sulphosalts); and 4th stage, the late carbonate phase (dolomite, calcite) [38–49]. The Panasqueira deposit contains significant amounts of wolframite, arsenopyrite, chalcopyrite and cassiterite [34].

The long history of exploitation is testified by the presence of a huge amount of tailings and other debris (Figure 1b). The piles (Rio ~1.2 million m3 ; Barroca Grande ~7.0 million m3 ) and the mud dams (Rio ~0.7 million m3 ; Barroca Grande ~1.2 million m3 ) are exposed to atmospheric conditions, and are being altered by chemical, physical and geotechnical activities. On the top of the Rio tailings, an arsenopyrite stockpile (~9400 m3 ) was deposited and remained exposed until 2006 [41].

Topography ranges in altitude from 350 to 1080 m [47], with deep valleys of about 9%–25% inclination, constraining the soil into a very thin layer. Climate is severe, with dry and hot summers and very cold, rainy and windy winters. The annual precipitation ranges between 1200 and 1400 mm with frequent snow falls, particularly above 700 m altitude. The mean annual temperature is 12 °C, ranging from 0 °C during the winter to 30 °C in the summer. The streams are generally dry in the summer and flooded in the winter. The prevailing wind in the area is NW-SE, with mean wind speeds of 4.22 m/s (*h* = 10 m), 5.55 m/s (*h* = 40 m) and 6.21 m/s (*h* = 80 m) (Figure 1c) [48–50]. The small villages around the mine have a historical dependence on soil and water use drinking water, agriculture, cattle rearing, fishing and forestry.

#### *2*.*2*. *Field Sampling and Sample Preparation*

Soil samples were collected according to a predefined grid (spaced ~400 m—Figure 1b). Two types of soil samples were collected at each sampling site: 122 topsoil (0–15 cm) samples and 116 subsoil samples collected below 15 cm depth. The difference in the total number of samples of each soil type is due to the presence at six sites of incipient and thin lithic soil derived from a substrate of metasediments. Topsoil samples were collected for the characterization of superficial contamination derived from the tailings, and subsoil samples to assess the extent of contamination at depth and simultaneously to identify geogenic markers. Approximately 50% of all samples were collected in duplicate. To establish the local geochemical background, 47 unaffected soil samples (Bk) were also collected outside the contaminated area (Casegas area located NE, out of the influence of the Barroca Grande prevailing winds). The coordinates of each sample were determined by GPS and georeferenced with UTM (Universal Transverse Mercator) coordinates. All soil samples were collected after clearing the soil surface of superficial debris and vegetation, and placed in polyethylene bags. Samples were dried in a thermostatically controlled oven at 40 °C, disaggregated in a porcelain mortar, sieved (<2 mm), homogenized, split into aliquots and the analytical aliquot pulverized to <170 m in a pre-cleaned mechanical agate mill for chemical analysis.

**Figure 1.** (**a**) Synthetic map of Portugal showing the location of Panasqueira mining area; (**b**) details of the study area, main geological units and soil samples grid - geological map adapted from [34,41,45]; (**c**) wind rose of the prevailing winds on the top of the mine (Barroca Grande tailing); and (**d**) on the top of a mountain 800 m north of the mine [46].

#### *2*.*3*. *Chemical Analysis*

Soil samples were submitted for multi-element analysis at the ACME Analytical Laboratories, which is an ISO 9002 Accredited Lab (Vancouver, Canada). A sample weight of 0.5 g was leached in hot (95 °C) aqua regia (HCl-HNO3-H2O), and concentrations were determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for Ag, As, Bi, Cd, Cu, W and Zn (detection limits of Ag, Bi, Cd, Cu, W < 0.1 mg·kgí1 ; As < 0.5 mg·kgí1 ; Zn < 1 mg·kgí1 ).

Accuracy and analytical precision were determined using analytical results of certified reference materials (standards C3 and G-2) and duplicate samples in each analytical batch. The results were within the 95% confidence limits of the recommended values given for the certified materials. The Relative Standard Deviation (RSD) was between 5% and 10%.

Soil pH: numerous studies have verified that soil pH has a great effect on metal bioavailability [16,51,52]. The pH gives an indication of the acidity and alkalinity of soil. Many chemical reactions are pH dependent, and knowledge of the pH enables the prediction of the extent and speed of chemical reactions [53]. The procedure adopted for the determination of pH was modified from [54]. A suspension of soil was made up in five times its volume of a 0.01 mol/L solution of calcium chloride (CaCl2) in water. The pH was measured using a calibrated pH-meter.

SOM: plays an important role in determining the fate of inorganic, as well as organic compounds in natural soil [55–58]. SOM can be roughly determined by measuring weight loss before and after ashing at 430 °C. Results are typically accurate to 1%–2% for soil with over 10% organic matter [53]. The procedure adopted was modified from [53]. Approximately 5 g of each sieved sample (<2 mm) was placed in a crucible and dried at 105 °C for 24 h. After cooling in a glass desiccator its weight was determined in a mass balance (resolution 0.001 g). The difference in weight gives the water content of each soil sample. The crucibles were then placed in a muffle furnace at 430 °C for 20 h. After cooling in a glass desiccator, the weight was measured on the same mass balance. The difference from the dry state gives the soil organic content (%).

#### *2*.*4*. *Data Treatment of Data*

Pearson's product-moment linear correlation coefficient matrix (*r*) was estimated in order to determine the extent of the relationship between the PTEs, pH and SOM [53]. The normality of statistical distribution of all data was verified by the Kolmogorov-Smirnov test (Į = 0.05) and Q-Q plots. The non-normal data were subjected to a non-parametric test or converted logarithmically to ensure the validity of the results. The statistical analysis was performed using Six Sigma Statistica® (Stat Software Inc, Tulsa, OK, USA) and IBM® SPSS® Statistics software (IBM, New York, NY, USA).

Analysis of Variance (ANOVA) was carried out in order to assess the relationship between the Potentially Toxic Elements and the independent variables (depth, pH and SOM), by a two-way ANOVA test, according to the following expression:

$$Z\_{\rm ljk} = \mu + \infty\_{\rm l} + \beta\_{\rm l} + \gamma\_{\rm lj} + \varepsilon\_{\rm ljk} \tag{l}$$

where, *Z* = the *k*th observation of the PTE taken at *j*th depth (*j* = 1 or 2) and *i*th soil property (*i* = 1 or 2), = the overall mean estimated, Į = the depth effect, ȕ = the soil property (pH or SOM), Ȗ = the interaction between depth and the soil property, and İ = the residual error [59–61].

Log10 transformation was applied to PTEs in order to convert the data to a normal or near-normal distribution and homogeneity of variances (Levene's test, *p <* 0.05). For the two-way analysis of variance, samples were classified according to pH values (very acid (3.0, 4.0), acid (4.0, 6.0) and neutral (6.0, 7.0)) and SOM ((3.6, 10.0) and (10.0, 39.0) in %). It should be noted that the SOM classes established, were only defined for statistical purposes. In the cases where there were only two samples with neutral pH, this particular class was not considered further.

Contamination Factor and Modified Degree of Contamination was estimated using the method proposed by [62] in sediment pollution studies, but is also applied to soil studies [63,64]. It is based on the calculation, for each pollutant, of a contamination factor (*CFi*) which is the ratio obtained by dividing the mean concentration of each metal in soil (*Ci*) by its corresponding baseline or background value (estimated from samples collected outside the area influenced by the mining activities) according to [65], and as explained in [6], *i*.*e*., the background value, *Cb* (mg kgí1 ), for the studied elements is as follows: Ag = 0.05; As = 22; Bi = 0.3; Cd = 0.01; Cu = 28; W = 0.35; Zn = 58) [64] ( Table 1):

$$\mathcal{C}F\_l = \mathcal{C}\_l / \mathcal{C}\_b \tag{2}$$

**Table 1.** Classification and description of the Contamination factor (*CF*) [62] and the Modified contamination degree (*mCd*) [66].

In [66] is presented a modified and generalized form of the [62] equation for the calculation of the overall degree of contamination (*mCd*) for each sample as below:

$$m\mathcal{C}\_d = \left(\sum\_{l=1}^{7} CF\_l\right) / 7 \tag{3}$$

where *CFi* is the contamination factor computed for each of the seven pollutants (Ag, As, Bi, Cd, Cu, W, Zn) considered in this study. In [66] is defined seven *mCd* degrees as shown in Table 1.

Potential ecological risk factor and risk index (*PERI*): is defined as the sum of the risk factors, which quantitatively defines the potential ecological risk of a contaminant in a sample, *i*.*e*.:

$$PERI\_l = \sum\_{l=1}^{7} EF\_l \quad = \sum\_{l=1}^{7} CF\_l \cdot TF \tag{4}$$

where *PERIi* is the Potential Ecological Risk Index for each sample (*i*); *EFi* is the monomial potential ecological risk factor; *CFi* is the single contamination factor (Equation (2)); and *TF* is the heavy metal toxic-response factor for each element. Soil toxic-response factors were computed for the seven selected elements according to the toxic factor requirements proposed by [62]. For this estimation there were used reference guide values of igneous rock types, soil, freshwater and land plants proposed by [67], and the land animals reference values proposed by [68]. The TF values obtained were: Zn = 1 < Cu = 2 < As = 5 < W = 15 < Bi = 20 < Cd = 30 < Ag = 35. In [62] are defined five *EF* classes and four *PERI* degrees, as shown in Table 2.

**Table 2.** Monomial potential ecological risk factor (*EF*) and Potential ecological risk index (*PERI*) classification levels [62].

Human health risk assessment calculations were based on the assumption that residents, both children and adults, are directly exposed to soil through three main pathways (a) ingestion; (b) dermal absorption and (c) inhalation of soil particles present in the air [69–72]. Ingestion of soil (i) occurs by eating soil particles and/or licking contact surfaces (e.g., hands). It is assumed that children present a higher ingestion rate, due to hand-to-mouth intake. Dermal absorption (ii) occurs through exposed skin, while soil is inhaled (iii) both by mouth and nose during breathing. Particles <10 m (PM10) are the more relevant in this process, although larger fractions of inhaled soil are, probably, decomposed in the gastrointestinal track. It is assumed that all contaminants are absorbed, both by the gastrointestinal tract or the lung [72]. Equations (5)–(7) were used to estimate the chronic daily intake of each exposure route considered [28,69,70,73], supplemented by specific quantitative information (Table 3):

$$CDI\_{\rm{lng}} = \mathcal{C}\_{\rm{soll}} \times \frac{\rm{IngR} \times EF \times ED}{BW \times AT} \times 10^{-6} \tag{5}$$

$$\mathcal{CDI}\_{\rm drum} = \mathcal{C}\_{\rm soil} \times \frac{\mathcal{SA} \times \mathcal{SAF} \times DA \times EF \times ED}{BW \times AT} \times 10^{-6} \tag{6}$$

$$\%CDI\_{\text{lnh}} = \mathcal{C}\_{\text{soil}} \times \frac{\text{Inh}R \times EF \times ED}{PEF \times BW \times AT} \tag{7}$$

**Table 3.** Variables for estimation of soil residential risk in the Panasqueira mining area.

The carcinogenic and non-carcinogenic side effects for each PTE were computed individually, as toxicity calculation uses different computational methods [76]. For each element and pathway, the non-cancer toxic risk was estimated by computing the Hazard Quotient (*HQ*, also known as non-cancer risk-Equation (8)) for systemic toxicity [73]. If *HQ* exceeds unity, it indicates that non-carcinogenic effects might occur. To estimate the overall developing hazard of non-carcinogenic effects, it is assumed that toxic risks have additive effects. Therefore, it is possible to calculate the cumulative non-carcinogenic hazard index (*HI*), which corresponds to the sum of HQ for each pathway (Equation (9)) [69,77]. Values of *HI* < 1 indicate that there is no significant risk of non-carcinogenic effects. While, values of HI > 1 imply that there is a probability of occurrence of non-carcinogenic effects, and are enhanced with increasing HI values [73]. The toxicity levels for each element were taken from The Risk Assessment Information System (RAIS) [28]. The probability of an individual developing any type of cancer over a lifetime, as a result of exposure to the carcinogenic hazards, was computed for each pathway according to Equation (10) [78]. The carcinogenic risk was estimated by the sum of total cancer risk (Equation (11)). A cancer risk below 1 × 10í6 is considered insignificant. The result of 1 × 10í6 is classified as the carcinogenic target risk. If the cancer risk is above 1 × 10í4 it is qualified as unacceptable [69,79,80]:

$$HQ = \frac{CD I\_{\text{pathway}}}{R\_{\text{f}}D} \tag{8}$$

$$HI = \sum HQ = HQ\_{\rm lng} + HQ\_{\rm drm} + HQ\_{\rm lhn} \tag{9}$$

$$\text{Risk}\_{\text{pathway}} = \mathcal{C}DI\_{\text{pathway}} \times \mathcal{CSF}\_{\text{pathway}} \tag{10}$$

$$\begin{split} \text{Risk} &= \sum \text{Risk}\_{\text{pathway}} = \text{Risk}\_{\text{ling}} + \text{Risk}\_{\text{drm}} + \text{Risk}\_{\text{lhn}} \\ &= \; CDI\_{\text{lng}} \times CFS\_{\text{lng}} + CDI\_{\text{lnh}} \times IUR + \frac{CDI\_{\text{drm}} \times CF\_{\text{lng}}}{ABS\_{\text{gl}}} \end{split} \tag{11}$$

Reference toxicity values were estimated as given in RAIS [28].

Spatial Estimation of extrapolated concentration values of chemical elements and contamination factors for the plotting of maps was based on geostatistics by extracting the necessary parameters for Ordinary Kriging from the variograms of each variable. Variograms were constructed and modelled with Surfer® (v. 8, Golden Software Inc, Golden, CO, USA), and the kriged estimations by Ordinary Kriging were also performed with Surfer®.

#### **3. Results and Discussion**

#### *3*.*1*. *Distribution of Ag, As, Bi, Cd, Cu, W and Zn in Soil Samples*

Previous studies carried out in the Panasqueira area [6,43,44], allowed, through the topsoil concentrations, the characterization of the anthropogenic soil contamination due to the presence of tailings and open air impoundments.

Descriptive statistical parameters (median and range) for pH, SOM, Ag, As, Bi, Cd, Cu, W and Zn are summarized in Table 4. The individual element values are compared with the corresponding local geochemical background levels, and also with reference values from the literature.

All samples, both topsoil and subsoil, have a pH lower than 7. In particular, topsoil pH values range from 3.2 to 6.6; 35.9% of topsoil samples have pH values between 3.0 and 4.0; 59.2% are between 4.0 and 5.0; 4.2% from 5.0 to 6.0, and 1.7% from 6.0 to 7.0, *i*.*e*., generally very acid to acid according to the United States Department of Agriculture classification [81]. As shown in Figure 2 (pH (a)), it is possible to identify that the areas with higher values are located around the villages Barroca Grande, S. Francisco de Assis, Rio, Barroca, Dornelas do Zêzere and S. Martinho. Around Rio and Barroca Grande tailings, the soil pH is above the 75th percentile (<4.1). The subsoil pH ranges from 3.6 to 6.2: 23.0% of the subsoil samples have pH values between 3.0 and 4.0; 73.5% are between 4.0 and 5.0; 2.7% from 5.0 to 6.0, and 0.9% from 6.0 to 7.0, *i*.*e*., also generally very acid to acid. The highest pH value found in subsoil samples was located in Rio village (Figure 2 pH (b)) on the north section of the Rio tailings. In the surroundings of S. Francisco de Assis, Barroca Grande and Dornelas do Zêzere villages, it is possible to identify some agricultural soil with pH > 4.6. Possibly these slightly higher pH values are due to land agricultural beneficiation techniques. Nevertheless, the soil of the present study area is overall classified as very acid to acid.

**Figure 2.** Spatial distribution of pH and soil organic matter (SOM, %) and Ag (mg kgí1 ) for topsoil (**a**) and subsoil (**b**). Percentile class limits at P5, P10, P25, P50, P75, P90, P95 and P97.5. In blue are represented the main water courses, in grey the villages and in white the main tailings and dams. The green dashed lines represent the Ag background levels.

**Table 4.** Median, minimum and maximum concentrations values of topsoil and subsoil samples from Panasqueira mine and surrounding environment compared with international data. Casegas area, considered as representative of geochemical background (Bk).

Notes: SOM, soil organic matter; Sk, Skewness; Bk, background values, calculated according the [65] paradigm and confirmed by the [82] methods; a Ontario reference values [83]; b Normal Ranges in Soils [16,84,85]; c Mean World Soil [67,86]; d Median values for Portuguese Soil [87].

The topsoil SOM values range from 5.7% to 39.0% (see Table 4 and Figure 2 SOM (a)), while the subsoil SOM values vary from 3.6% to 19.5% (see Table 4 and Figure 2 SOM (b)). Comparing the topsoil and subsoil pH and SOM maps (Figure 2 and Table 4) is possible to observe that areas with the highest SOM values are related to areas with the lowest pH values.

To evaluate the presence of possible local anomalies the Ag (Figure 2), As, Bi, Cd (Figure 3), Cu, W, Zn (Figure 4) median and maximum values, observed in topsoil, were compared with the corresponding results in subsoil samples. Although the results tabulated in Table 4 show that Ag, As, Bi, Cu, W, and Zn present highest median and maximum values for topsoil when compared with subsoil, the paired samples *t*-test indicates that there are significant differences between soil samples at both depths for the elements As, Bi and W. Additionally, the results clearly show that there are higher contents in soil samples at both depths, when compared with the local geochemical background. The behavior of the topsoil element concentrations may reflect the influence of the Barroca Grande and Rio tailings and open air impoundments, possibly caused by aerial transport and deposition.

Topsoil samples, as shown in Table 5, have many strong to very strong correlation coefficients, *i*.*e*., As/Ag, As/Bi, As, Cd, As/Cu, As/W, Ag/Bi, Ag/Cu, As/W, Bi/Cd, Bi/Cu, Bi/W, Cd/Cu, Cd/W, Cd/Zn, Cu/W. The strongest correlations are between Ag/Cu (0.94), As/Ag (0.90) and Bi/W (0.90). There is also a positive intermediate correlation (*r* = 0.31 to 0.70) between PTEs and pH. Several authors claim that heavy metal mobility holds a positive correlation with pH [18,88,89]. It is well known that pH affects heavy metal availability, since it is the major factor in controlling the ability of soil to retain heavy metals in an exchangeable form [21]. With low SOM the pH values may become relatively more important for the partitioning of metals. Most elements exhibit a weak negative correlation with SOM. For subsoil samples the PTEs correlation also presents some strong correlation coefficients (Table 5), but they are overall lower than in topsoil. The stronger correlations are between As/Bi (*r* = 0.75) and Bi/Cu (*r* = 0.69). At this depth, pH is also the most significant soil property, presenting weak to intermediate positive correlations with the PTEs, except W.

**Figure 3.** Spatial distribution of As, Bi, Cd (mg·kgí1 ) values for topsoil (**a**) and subsoil (**b**). Percentile class limits at P5, P10, P25, P50, P75, P90, P95 and P97.5. The green dashed lines represent the elements background levels. In blue are represented the main water courses, in grey the villages and in white the main tailings and dams.

**Figure 4.** Spatial distribution of Cu, W, Zn (mg·kgí1 ) values for topsoil (**a**) and subsoil (**b**). Percentile class limits at P5, P10, P25, P50, P75, P90, P95 and P97.5. The green dashed lines represent the elements background levels. In blue are represented the main water courses, in grey the villages and in white the main tailings and dams.

**Table 5.** Pearson's linear product-moment correlation coefficients for Ag, As, Bi, Cd, Cu, W, Zn, pH and SOM in topsoil (*n =* 122) and subsoil (*n =* 116) samples.

Notes: \*\* Correlation is significant at the 0.01 level (2-tailed); \* Correlation is significant at the 0.05 level (2-tailed).

In order to identify the most important factor controlling the different PTEs spatial differences concentrations, a two-way ANOVA was performed [90]. In this study, two models were selected to group the variables, (a) depth-SOM and (b) depth-pH. The results of the test between dependent (PTEs) and independent (depth and SOM) variables (Table 6) showed that soil depth accounts for significant variations between the group means: Bi (*p* = 0.035), Cd (*p* = 0.015) and W (*p* = 0.009). The independent variable SOM shows significant variations in the concentration of As (*p* = 0.021), Bi (*p* = 0.068), W (*p* = 0.013) and Zn (*p* = 0.021). There is no significant interaction between depth and SOM. While the independent variable pH (Table 7) shows that there are significant variations with depth for most PTEs (Ag *p* = 0.071; As *p* = 0.027; Bi *p* = 0.001; Cd *p* <0.000; W *p* <0.000), except for Cu (*p* =0.371). pH presents a significant variation for Ag (*p* = 0.031), W (*p* = 0.001) and Zn (*p* <0.001). The interaction between pH\*depth shows no significant variations (*p* >0.050), except on the concentration of Cu (*p* = 0.038).

#### *3*.*2*. *Quantitative Assessment of Soil Contamination*

In this study, a simplified approach to assess soil contamination based on comparing the measured concentrations in the Panasqueira soil with the geochemical background values for Casegas was adopted. Table 8 shows the results of contamination factors (*CF*) and the modified degree of contamination (*mCd*) for the selected elements in topsoil and subsoil, and also in Casegas soil.

**Table 6.** Two-way ANOVA results between the dependent (PTEs) and independent (depth, SOM) variables. PTEs were subjected to log-normal transformation (As, As, Cd, Cu, W and Zn with Į = 0.050; Bi with Į = 0.075).

According the topsoil median values of the contamination factor, Bi, Cd and W present an extremely high degree of contamination, while Ag and As a high contamination factor. The results demonstrated that *mCd* values vary from the minimum 1.2 in both topsoil and subsoil to the maximum of 150 in topsoil, with median values varying from 4 in subsoil to 6 in topsoil. The cumulative frequency distribution indicates that only 7.4% of the soil samples were classified as no to low degree of pollution, with *mCd* values < 2.0, and the remaining soil samples (92.6%) registered moderate to ultra-high degree of pollution, with *mCd* values equal or greater than 2.0 (27.9% between 2 *mCd* < 4; 27.1% between 4 *mCd* < 8; 19.7% between 8 *mCd* < 16; 15.6% between 16 *mCd* < 32 and 2.5% between 32 *mCd*). The enrichment is more pronounced in topsoil (subsoil *mCd* values ranged 1.2–26.4).

**Table 8.** Contamination Factors (*CF*) and Modified Degree of Contamination (*mCd*) using geochemical background values. Casegas area is considered as representative of geochemical background.

Casegas is considered as an uncontaminated site, since it is outside the mine area and the influence of airborne polluted dust. The maximum *mCd* value of 13.1, represents a very high contamination degree, which is mostly due to the Cd and W contamination factors. In this case, however, the high *mCd* value is most likely due to geogenic sources, as this is a naturally enriched and mineralized zone.

The calculated *mCd* values make possible the assessment of the spatial distribution of the modified degree of contamination. The first step is the determination of the spatial structure of the new variables, and the experimental variograms were used to model the data using exponential models, the extracted parameters are for topsoil: main direction = 90°; Nugget effect (*C*0) = 30; Sill-Nugget effect (*C*1) = 330; range of influence (length) = 1200 m; anisotropy ratio = 1.81; and for subsoil: main direction = 90°; *C*0 = 0; *C*1 = 19; length = 900 m; anisotropy ratio = 2.90. Estimation of the spatial distribution was then achieved by Ordinary Kriging and the respective map plotted. In Figure 5 it is possible to observe the *mCd* spatial distribution revealing areas with very high values.

Figure 5 shows that soil samples collected near the Barroca Grande tailings (A), Rio tailings (C) and the mud impoundments stand out clearly, because the soil is enriched in Ag, As, Bi, Cd, Cu, W and Zn; all soil samples from both depths taken from Barroca Grande exceed the As, Bi, Cd and W baseline values for Portugal, while at Rio only Zn in some samples (25%) has concentrations that are lower than the guide value. According to [42], the Barroca Grande tailings and open impoundments have high As, Cd, Cu, Pb, W, and Zn concentrations (mean content in the more coarse tailings material As = 7142 mg·kgí1 ; Cd = 56 mg·kgí1 ; Cu = 2501 mg·kgí1 ; Pb = 172 mg·kgí1 ; Sn = 679 mg·kgí1 ; W = 5400 mg·kgí1 and Zn = 1689 mg·kgí1 and mean content in the impoundment material (rejected from the mill operations) As = 44,252 mg·kgí1 ; Cd = 491 mg·kgí1 ; Cu = 4029 mg·kgí1 ; Pb = 166 mg·kgí1 ; Sn = 454 mg·kgí1 ; W = 3380 mg·kgí1 and Zn = 3738 mg·kgí1 ). The mineralogy of these tailings consists of mainly quartz, muscovite, kaolinite, illite-montmorillonite, montmorillonite-vermiculite, and chlorite, and also arsenopyrite, wolframite, and natrojarosite.

The dams at Barroca Grande may pose a significant potential threat, due to the fine-grained nature of the materials, and their location with respect to the Casinhas stream that cross S. Francisco de Assis village. The X-Ray Diffraction (XRD) analysis of the impoundment material revealed the presence of scorodite, arsenopyrite, quartz, sphalerite, hematite, and muscovite. These tailings and impoundment material are metal-enriched at such a level, likely to be toxic to the ecosystem [42]. The concentrations exceed the values defined for the 90th percentile of the South Portuguese Zone (As 157 mg·kgí1 ; Cu 108 mg·kgí1 ; Ni 62 mg·kgí1 ; Pb 117 mg·kgí1 ; Zn 134 mg·kgí1 ), which is indicative of enrichment in trace metals. This Ag-As-Bi-Cd-Cu-W-Zn association is quite logical and is linked to the Panasqueira ore-paragenetic association. The highest *mCd* values identified near Barroca Grande (A), São Francisco de Assis (B) and Rio (C) confirms that mechanical and chemical dispersion from Barroca Grande and Rio tailings and mud impoundments occurs. Most of the samples (90% or higher of total samples) collected from Barroca Grande (A), São Francisco de Assis (B) and Rio (C) villages, exhibit *mCd* values >8.0, clearly indicting a very high degree of contamination.

**Figure 5.** Spatial distribution of the modified degree of Contamination (*mCd*) for topsoil (**a**) and subsoil (**b**). Values were estimated on the basis of the concentration factors of Ag, As, Bi, Cd, Cu, W and Zn (A, Barroca Grande; B, S. Francisco de Assis; C, Rio).

The Panasqueira tailings impoundments have been and are affected by surface water flows (from heavy rainfall events) that have eroded the tailings from their original location and transported the materials downstream to residential areas (namely to S. Francisco de Assis). However, the superficial flat of the tailings have dried and are susceptible to wind erosion. The relative rates of water and wind erosion and transport, in Barroca Grande, São Francisco de Assis and Rio, suggest that wind processes have similar, and in many cases greater, impact on loss and local redistribution of soil in ecosystems than an eventual erosional soil enrichment.

#### *3*.*3*. *Potential Ecological Risk Factor and Risk Index*

The topsoil and subsoil samples results for individual element potential pollution factor (*EF*) and potential ecological risk (*PERI*) are presented in Table 9. For both soil sample depths, As, Cu and Zn show a low potential ecological risk, with median values <40 (see Table 2). Tungsten also exhibits a low risk in subsoil, but a high risk in topsoil, while Bi and Ag show a moderate risk at both depths. Cadmium presents a very high ecological risk in topsoil and a high risk on subsoil. Using the median values, the topsoil risk factor is ranked as: Cd > W > Ag > Bi > As >> Cu > Zn, while for subsoil the ranking is: Cd > Ag > Bi > As > Cu = W > Zn. These results suggest a very high environmental risk, especially for Cd.

In order to estimate the global potential ecological risk in the study area, the PERI was computed. The median values classify soil samples at both depths with a very high risk (Table 9). The cumulative analysis shows that the soil samples at both depths do not display a low risk index (<150), and only 7.4% of topsoil present a moderate risk index. *PERI* classified 92.6% of topsoil samples as high to very high ecological risk. The same occurs for 61.2% of subsoil samples, which should be considered as an extensive hazard. Figure 6 displays the *PERI* spatial distribution. Topsoil has a wide area classified with a very high ecological risk, which is consistent with the wind direction, the water courses and the actual and previous exploration and beneficiation locations (Figure 6a). Subsoil also presents a very high risk index in the same topsoil areas, but with smaller area expression. These results are consistent with those mapped by the individual elements, Ag, As, Bi, Cd, Cu, W and Zn (Figures 2–4), and the modified degree of contamination (*mCd*; Figure 5), with the same affected areas.

**Table 9.** Statistical results of the single element potential pollution factor (*EF*) and potential ecological risk (*PERI*) for topsoil and subsoil samples.

**Figure 6.** Spatial distribution of Potential Ecological Risk Index (*PERI*) for topsoil (**a**) and subsoil (**b**) in the study area.

#### *3*.*4*. *Human Health Risk Assessment*

Both non-carcinogenic hazard (*HQ*) and carcinogenic risk (Riskpathway) of topsoil in the Panasqueira mine and surrounding area, through the different pathways (ingestion, dermal and inhalation), were estimated according to the human health risk assessment model [28]. The cumulative hazard index (*HI*) and total risk (Risktotal) were also characterized for multi-pathway routes in resident population.

The non-carcinogenic effects considered the most conservative exposure condition—children (1–6 years old). The potentially toxic elements defined in this study (Ag, Cd, Cu, W and Zn), apart from As, do not present a non-carcinogenic hazard for children in the Panasqueira area (maximum *HI*child-Ag,Cd,Cu,W,Zn 0.37). The As non-carcinogenic hazard median values, estimated for the different exposure routes were *HQ*ing-As (2.75) >> *HQ*drm-As (0.23) >> *HQ*inh-As (0.00). *HI*child-As values ranged between 0.78 and 69.50, with a median value of 2.98 § *HQ*ing-As (2.70), due to the ingestion hazard quotient ranging between 0.72 and 64.10. These results (<1—safe level) indicate that there is a cause for concern for the non-cancer health effects for children living in the Panasqueira study area, mainly due to As oral ingestion, with *HQ*ing-As showing median values above one (Figure 7).

**Figure 7.** Comparative boxplot distribution of the Non-carcinogenic Hazard Quotient for children of As for Ingestion (*HQ*ingAs), Inhalation (*HQ*ihnAs), Dermal contact (*HQ*drmAs) routes, the Cumulative Hazard Index (*HI*As) and the sum of the Non-carcinogenic Hazard Quotient of the other defined PTEs (Ag, Cd, Cu, W and Zn) for Ingestion (*HQ*ingPTE(-As)), Inhalation (*HQ*ihnPTE(-As)), Dermal contact (*HQ*drmPTE(-As)) routes and the Cumulative Hazard Index (*HI*PTE(-As)) for topsoil samples (tailing samples were removed) in the Panasqueira area. The extremes and outliers were removed.

The carcinogenic risks adjusted both to children and adult were studied for the identified PTEs in this study. The median values of the different exposure routes and total risk were estimated. Their mean distribution were Risktotal (2.62 × 10í4 ) § RiskAs (2.62 × 10í4 ) § Risking (2.39 × 10í4 ) § RiskingAs (2.39 × 10í4 ) >> Riskdrm (2.26 × 10í5 ) § RiskdrmAs (2.26 × 10í5 ) >> Riskinh (1.32 × 10í7 ) § RiskinhAs (1.32 × 10í7 ) (Figure 8). The high significance of As to cumulative carcinogenic elements risk is due to the very low risk displayed by the other elements. Furthermore, the representation of the exposure routes are distributed as: (a) Risking § RiskingAs ranged 3.97 × 10í5 –3.53 × 10í3 , with a median value of 1.52 × 10í4 ; (b) Riskdrm § RiskdrmAs ranged 3.76 × 10í6 –3.34 × 10í4 , with median value of 1.44 × 10í5 ; and (c) Riskinh § RiskinhAs ranged 2.21 × 10í8 –1.96 × 10í6 , with median value of 8.38 × 10í8 . The cumulative pathway Risktotal § RiskAs ranged 4.35 × 10í5 –3.87 × 10í3 , with a median Risktotal of 1.66 × 10í4 mostly due to RiskingAs. Intake of As may cause cancer in several human organs through ingestion, and lung and skin cancer through inhalation [28]. Moreover, the Risking represents 91.29% of cumulative risk from exposure routes, while Riskdrm (8.69%) and Riskinh (0.05%) have less significance. Similar results were obtained in other studies [77,91–94]. The cumulative median Riskdrm is > 1 × 10í6 for all samples, of which 69.67% > 1 × 10í5 , and the cumulative exposure route median Risking is > 1 × 10í5 for all samples, being 72.13% > 1 × 10í4 . These results show that there is a very high As ingestion cancer risk. The samples with higher hazard are located in and around the villages of the study area, being the more representative results found nearby the large tailing piles and open air impoundments (Figure 9). Although the median Riskdrm value is lower and between 1 × 10í6 and 1 × 10í4 , and is considered as acceptable, it cannot, however, be negligible in the Panasqueira mining area, once all samples exceed the target value (1 × 10í6 ). While the sum of exposure pathways is RiskCd = 1.09 × 10í10 (<1 × 10í6 ), it should be noted that it is the cumulative toxic metal and kidney index that is the main target for Cd toxicity [26,95].

**Figure 8.** Comparative boxplot distribution of the Carcinogenic Risk adjusted to both children and adult for total PTEs (Ag, As, Cd, Cu, W and Zn) for Ingestion (Risking), Inhalation (Riskinh), Dermal contact (Riskdrm) routes, the Cumulative Risk (Risktotal) and the Carcinogenic Risk of As for Ingestion (RiskingAs), Inhalation (RiskinhAs), Dermal (RiskdrmAs) routes and the Cumulative Risk (RiskAs) for topsoil samples (tailing samples were removed) of the Panasqueira area. The extremes and outliers were removed.

As the risk analyses showed that ingestion is the most important pathway, further studies should be carried out in order to estimate the metal bioaccessibility in human receptors, such as a simplified *in vitro* physiologically based extraction test (PBET), allowing the estimation of the percentage of the bioaccessible orally ingested fraction of each metal from soil samples [77,80,96]. Additional investigations must be made on different sample media, such as vegetables, rhizosphere, irrigation and drinking water and street dust in the local villages under influence of mining activities (such as S. Francisco de Assis and Barroca) and also in non-exposed villages considered as reference areas (such as Casegas and Unhais-o-Velho). These studies will provide the necessary data and information for the determination of the influence of the Panasqueira mine activities, through more than 110 years, on the surrounding population, and should be complemented by biological studies [97].

**Figure 9.** Spatial distribution of the Carcinogenic risk adjusted for both children and adult considering (**a**) ingestion; (**b**) dermal contact; (**c**) inhalation routes; and (**d**) the cumulative Risk.

#### **4. Conclusions**

The mining and beneficiation process generates huge quantities of waste materials from the ore extraction and milling operations, which accumulate in tailings and open impoundments and are largely responsible for the high levels of metals(loid)s released into the surrounding environment. At the Panasqueira mine, the mining activities have produced sulphide-rich mine wastes that are responsible for the high levels of metals at Barroca Grande and Rio tailings. The oxidation of these sulphides may give rise to the mobilization and migration of trace metals from the mining wastes into the soil. Heavy metal soil contamination is an outstanding example of environmental risk. Metal(loid)s, such as As, Cd or Pb, for example, are toxic for humans, as well as for animals, and can even lead to death if ingested in large doses, or over large periods of time. Exposure to hazardous elements may have different pathways, such as ingestion, dermal absorption and inhalation of soil particles present in the air.

Collection of surface soil permits the characterization of anthropogenic contamination, caused by the presence of tailings and open air impoundments. Such soil, classified globally as very acid to acid, occurs in areas with very low soil pH values, which are related to areas with the highest SOM values. The metal assemblage identified in these soil types (Ag, As, Bi, Cd, Cu, W and Zn) show highest values in topsoil samples (0–15 cm), when compared with the values of the corresponding subsoil samples below 15 cm depth. Soil samples from both depths have higher contents when compared to the estimated local geochemical background. These results show the influence of the metal-rich mine wastes, stored in Barroca Grande and Rio tailings and in the open impoundments, as the main source of pollution of the surrounding environment, possibly due to aerial transport and deposition.

Very strong correlations were estimated between the PTEs and between PTEs and pH. The strongest correlations were Ag/Cu, As/Ag and Bi/W. The PTEs and pH positive correlation, confirm the findings of other studies that heavy metal mobility holds a positive correlation with pH, affecting their availability.

The ANOVA results, showed significant variations with depth, and the majority of the PTEs (Ag, As, Bi, Cd and W) and pH exhibit a significant variation for Ag, W and Zn.

According to the calculated contamination factor (*CF*) and the modified degree of contamination (*mCd*), 7.4% of the soil samples were classified as having no to low degree of pollution with *mCd* < 2.0 and 92.6% registered moderate to ultra-high degree of pollution with *mCd* 2.0. The spatial distribution of the *mCd* reveals areas with a very high degree of contamination. The highest *mCd* values were identified near Barroca Grande, São Francisco de Assis and Rio, and confirm the mechanical and chemical dispersion from tailings.

The risk factor estimated for individual element potential pollution factor and potential ecological risk, revealed at both soil sample depths that As, Cu and Zn have a low potential ecological risk, W low risk in subsoil, but a very high risk in topsoil samples; Ag moderate to considerable ecological risk and Cd is the worst element, exhibiting a very high environmental risk. The estimated potential ecological risk (*PERI*) classifies samples from both depths with a very high risk. In the *PERI* spatial distribution, topsoil has a wide area classified with a very high ecological risk, consistent with wind direction, water courses and current and previous exploration and beneficiation locations.

The non-carcinogenic hazard effects, determined for children (1–6 years old), indicated that the PTEs (Ag, Cd, Cu, W and Zn) do not present a non-carcinogenic hazard for children, while the As value is high, representing a risk for the non-cancer health effects for children in the Panasqueira area. The carcinogenic risk for both children and adults, revealed a very high cancer risk due to As ingestion, which may cause cancer to several human organs cancer through ingestion, and lung and skin cancer through inhalation.

Further studies should be carried out in order to estimate the metal bioaccessibility in human receptors.

#### **Acknowledgments**

This research is financially supported by FCT—Fundação para a Ciência e a Tecnologia (grants SFRH/BD/63349/2009).

#### **Author Contributions**

The original idea of the study was designed on a brainstorm meeting by all authors. E. Ferreira da Silva and J. P. Teixeira were responsible for recruitment and follow-up of study participants. All authors participated on the field work. C. Candeias and P. F. Ávila were responsible for data cleaning and analyses. C. Candeias drafted the manuscript with collaboration of all authors and it was revised by all. All authors read and approved the final manuscript.

#### **Conflicts of Interest**

The authors declare no conflict of interests.

#### **References**