DOI: 10.31038/GEMS.2025714

Abstract

Background: Previous studies have demonstrated that exposure to environmental tobacco smoke is associated with a reduction in fractional exhaled nitric oxide (FeNO) levels, elevation in eosinophil (EOS) counts and alterations in airway inflammation patterns, influencing the efficacy of glucocorticoid therapy for TH2 inflammation. No previous study has investigated the association of the extent of exposure to environmental tobacco smoke with FeNO levels. This study aimed to investigate the association of the extent of exposure to environmental tobacco smoke with FeNO level and EOS count.

Methods: In this retrospective cohort study, we included 12766 individuals from the National Health and Nutrition Examination Survey 2007–2012. The extent of exposure to environmental tobacco smoke was assessed by measuring serum cotinine levels. Participants were categorised into quintiles based on their cotinine levels. Logistic regression models were developed to evaluate the association of serum cotinine levels with FeNO levels and EOS count.

Findings: In the unadjusted and adjusted models, the highest quintile of serum cotinine levels (>105 ng/ml) was significantly negatively associated with FeNO levels. However, low-to-moderate quintiles of serum cotinine levels were not significantly associated with FeNO levels. Based on sensitivity analyses, the negative associations between the highest quintile of serum cotinine levels and FeNO levels remained consistent among participants with asthma, chronic bronchitis and respiratory symptoms within 7 days. Increased serum cotinine levels were significantly associated with increased EOS counts, which in turn were significantly associated with increased FeNO levels. EOS significantly mediated 7.59% of cotinine-associated reductions in FeNO levels.

Conclusions: Our findings indicated that high levels of tobacco smoke exposure are associated with a decrease in FeNO levels and an increase in EOS count. The smoking status should be considered when evaluating type 2 airway inflammation based on FeNO levels and EOS count.

Introduction

The subtypes of airway inflammation include neutrophilic, eosinophilic, mixed and oligocytic inflammation. Airway eosinophilic inflammation is defined as a blood eosinophil (EOS) count ≥300 cells·μL⁻¹ and/or a sputum EOS count ≥3% [1]. Airway eosinophilic inflammation is sensitive to inhaled corticosteroids (ICS) [2]. In particular, in patients with asthma and chronic obstructive pulmonary disease (COPD) exhibiting airway eosinophilic inflammation, treatment with ICS can ameliorate symptoms, reduce acute attack frequency and improve lung function [3]. Therefore, diagnosing airway eosinophilic inflammation is important. Fractional exhaled nitric oxide (FeNO) levels, along with blood EOS counts, are considered indicators of airway eosinophilic inflammation [4]. Moreover, FeNO levels are closely associated with an individual’s response to allergens, airway hyper-responsiveness and impaired lung function [5,6], thus enabling the diagnosis of airway eosinophilic inflammation. They are helpful for guiding ICS and IgE-targeted therapies for patients with COPD, asthma and chronic cough. Therefore, FeNO and eosinophils are of great significance in the management of airway diseases. Nitric oxide (NO) serves as an endogenous regulatory molecule whose production is regulated by NO synthase (NOS), which is predominantly produced by inducible NOS in bronchial epithelial cells. Exhaled NO levels can be measured by quantifying NO concentration in exhaled breath.

Smoking induces alterations in airway inflammation types, thereby affecting the efficacy of ICS therapy in patients with asthma and COPD. Consequently, investigating the influence of smoking on type 2 airway inflammation has become a focal point of research. Previous studies have grouped light and heavy smokers together, making it difficult to determine the specific extent of tobacco smoke exposure that leads to changes in airway inflammation types, thus resulting in contradictory research findings. No previous study has investigated the association of the extent of exposure to environmental tobacco smoke with FeNO levels. Furthermore, although FeNO and EOS are associated with airway eosinophilic inflammation and act through different pathways, it remains unknown whether EOS play a mediating role in reducing FeNO levels induced by tobacco smoke exposure. Cotinine is the primary metabolite of nicotine and is significantly positively correlated with the extent of tobacco smoke exposure [7]. The estimated elimination half-life of cotinine (approximately 15–20 h) is longer than that of nicotine [8]. Therefore, cotinine has been widely used as a biomarker for tobacco exposure [9-12], as explained in detail in the NHANSE database (https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/COTNAL_G.htm). In the current study, cotinine was used to determine the level of tobacco smoke exposure.

Materials and Methods

Study Design and Population

The National Health and Nutrition Examination Survey (NHANES) is a programme of studies designed to assess the health and nutritional status of adults and children in the United States (US). During each survey cycle, a sample of participants is selected from the US non-institutionalised civilian population using a complex, stratified, multistage probability cluster sampling design. We analysed data from the participants of the NHANES from 2007 to 2012. A total of 16,784 participants aged ≥18 years had available data on cotinine. The National Center for Health Statistics (NCHS) Institutional Review Board (Hyattsville, MD) approved the study protocols, and all participants provided written informed consent.

FENO

FENO was measured using Aerocrine NIOX MINO®, which features a dynamic flow restrictor that stabilises the flow rate at 50 ml/s. The NHANES protocol required two reproducible FENO measurements in accordance with the testing procedures recommended by the manufacturer and similar to those published by the American Thoracic Society and European Respiratory Society. If either or both of the first two valid FENO measurements are <30 ppb and the measurements differ by ≤2 ppb or if both measurements are >30 ppb and within 10% of each other, then the test was considered reproducible and complete. Two values below or above the limit of detection were also considered reproducible.

Cotinine

Serum samples were processed, stored and shipped to the Division of Laboratory Sciences, National Center for Environmental Health and Centers for Disease Control and Prevention for analysis. Serum cotinine level was measured via isotope dilution–high-performance liquid chromatography–atmospheric pressure chemical ionisation–tandem mass spectrometry (ID–HPLC–APCI–MS/MS). Briefly, the serum sample was spiked with methyl-D3 cotinine as an internal standard, and after an equilibration period, the sample was applied to a basified solid-phase extraction column. Cotinine was extracted from the column with methylene chloride; the organic extract was concentrated, and the residue was injected into a short C18 HPLC column. The eluant from these injections was monitored using APCI–MS/MS, and the m/z 80 daughter ion from the m/z 177 quasi-molecular ion was quantitated, along with additional ions for the internal standard, external standard and confirmation. Cotinine levels were calculated from the ratio of native to labelled cotinine in the sample based on a comparison with a standard curve.

Other Variables of Interest

Age, sex and race/ethnicity were self-reported. Body mass index (BMI) was calculated using the height and weight measured at the mobile examination centre. Race and ethnicity were categorised as non-Hispanic Black, other Hispanic, non-Hispanic white and non-Hispanic other race, based on categories provided by NHANES investigators. Self-reported data on engaging in strenuous exercise within 1 h, consumption of NO-rich vegetables within 3 h , consumption of NO-rich meat within 3 h, use of oral or inhaled steroids within 2 days and development of respiratory symptoms within 7 days were collected using a computer-assisted personal interview system. Asthma and chronic bronchitis were defined according to self-reported diagnosis by a physician. A complete blood count was performed using the Beckman Coulter MAXM instrument in MECs, and all participants underwent blood cell analysis.

Selection of the Study Population

We conducted a cross-sectional study using aggregated data from three NHANES cycles (2007/2008, 2009/2010 and 2011/2012) in which serum cotinine was tested. A total of 16,784 adults completed the serum cotinine test during this survey period (Figure 1). Among them, 3280 were excluded due to missing data on exhaled NO (3227) and EOS (53). Additionally, 738 participants were excluded due to the following missing covariate data: asthma; chronic bronchitis; engaging in strenuous exercise within 1 h; consumption of NO-rich vegetables within 3 h; consumption of NO-rich meat within 3 h; use of oral or inhaled steroids within 2 days and development of cough, cold or respiratory illness within 7 days. Finally, 12766 participants were included in the study. The participants were categorised into five groups based on cotinine levels: Q1 (first quintile), Q2 (second quintile), Q3 (third quintile), Q4 (fourth quintile) and Q5 (fifth quintile).

Figure 1: Flow diagram of the study. Abbreviations: FeNO: fractional exhaled nitric oxide; EOS: eosinophils; ICS: inhaled corticosteroids; CI: confidence interval; OR: odds ratio; SD: standard deviation.

Statistical Analysis

Continuous variables of age, BMI at enrolment and laboratory findings were expressed as median (interquartile range) or mean ± standard deviation (SD). The remaining categorical variables were expressed as n (%). The participants were categorised into quintiles based on the cotinine levels provided by NHANSE: Q1 (0.011), Q2 (0.011–0.027), Q3 (0.027–0.104), Q4 (0.104–105) and Q5 (≥105). Quintiles based on cotinine levels can effectively reflect the distribution of tobacco smoke exposure levels among the participants. Participants in the highest quintile (Q5) were considered to have high levels of tobacco smoke exposure. Logistic regression models were used to investigate the odds ratios (ORs) and 95% confidence intervals (CIs) of FeNO levels according to serum cotinine levels (quintiles). In adjusted model 1, the adjusted covariates included cotinine, age, , BMI and ethnicity. In adjusted model 2, the adjusted covariates were asthma, chronic bronchitis, EOS, engaging in strenuous exercise within 1 h, consumption of NO-rich vegetables within 3 h, consumption of NO-rich meat within 3 h, use of oral or inhaled steroids within 2 days, development of respiratory symptoms within 7 days and covariates included in model 1. A sensitivity analysis was conducted using the logistic regression model among participants with such as chronic bronchitis , asthma, and respiratory symptoms within 7 days prior to testing. After weighting the data with the sample weights (full sample 2-year interview weight) obtained from the NHANS 2007–2012 demographics file, logistic regression analysis was performed to explore the relationship between tobacco exposure and FeNO levels in the US population. Additionally, logistic regression models were utilised to explore the association between cotinine levels (quintiles) in the participants and higher EOS counts (≥0.3 × 10³ cells/µl). Logistic regression models were also used to analyse the association between EOS counts in the participants and higher FeNO levels (>25 bbp). Correlation coefficients (Spearman’s rho and Kendall’s tau) were calculated to investigate the cotinine–EOS association. This study examined the proportion of mediation through EOS in the associations of cotinine levels and FeNO using the R (R4.2.1) based on the mediation method recommended by Hayes [13]. The data were analysed using R (R4.2.1) and SPSS version 21.0 (IBM Corp., Armonk, NY, USA). The statistically significant cut-off of the two-sided P-value was 0.05.

Results

Characteristics of the Participants

Table 1 describes the socio-demographic, anthropometric, race, primary disease and laboratory data of the participants. Approximately 17.5% (2446/13,945) of the participants had FeNO levels >25 bbp, and approximately 21.5% (3194/13,945) had EOS counts ≥0.3 × 10³/µl. The median age of the participants was 43 (20, 60) years. In total, 2844 (20.4%) participants had cough, cold or respiratory illness within the past 7 days, 1721 (12.4%) had asthma, and 459 (4.1%) had chronic bronchitis. In order to ensure that the survey results can represent the entire US population, we weighted the data. The characteristics of the US adults were showed in the Table S4.

Table 1: Characteristics and laboratory data of the participants according to cotinine levels (n = 12766).

Association of Cotinine Levels with FeNO Levels

Table 2 presents the risk of higher FeNO levels (>25 bbp) associated with serum cotinine levels categorized into quintiles among participants. In the unadjusted models, participants with the highest quintile of serum cotinine levels (>105 ng/ml) showed decreased FeNO levels compared with those with the lowest quintile of serum cotinine levels (0.011 ng/ml) (OR, 0.24 [0.20, 0.29]). There were no significant differences in FeNO levels of participants between Q3 and the lowest quantile of cotinine levels (21.6% vs 19.5%) as well as between Q2 and the lowest quantile of cotinine levels (21.7% vs 19.5%) (Table 2). After adjusting for potential confounders, similar results were observed in models 1 and 2. In model 2, a 1 SD increase in cotinine level was associated with lower FeNO levels (OR, 0.47 [0.42, 0.517]) (Table 2). Sensitivity analyses performed among participants with asthma, recent respiratory symptoms, and chronic bronchitis yielded consistent findings (Tables S1-S3). After weighting the sample, the negative association between cotinine and FeNO levels remained consistent across the US population (Table S5).

Table 2: Adjusted ORs and 95% CIs for the association of cotinine levels with the risk of high FeNO level (n = 12766).

#P > 0.05 Abbreviations: FeNO: fractional exhaled nitric oxide; EOS: eosinophils; CI: confidence interval; OR: odds ratio; SD: standard deviation.

Logistic regression model 1 included covariates of cotinine, age, sex, BMI, race and EOS count. Logistic regression model 2 included covariates of use of oral or inhaled steroids within 2 days, development of respiratory symptoms within 7 days, consumption of NO-rich food within 3 h, engaging in strenuous exercise within 1 h, asthma, chronic bronchitis and covariates in model 1.

Association of Cotinine Levels with EOS Levels

Compared with participants with the lowest quintile of cotinine levels, those with the highest quintile of cotinine levels had higher EOS count (≥0.3 × 10³ cells/µl) (OR 1.82 [1.61, 2.06]). The ORs were 1.87 (1.64, 2.13) and 2.39 (2.09, 2.74) in models 1 and 2, respectively (Table 3). However, no statistically significant difference in EOS counts was observed between Q3 and the lowest quintile of cotinine levels and between Q2 and the lowest quintile of cotinine levels. This study revealed that higher cotinine levels were positively associated with EOS count in all models (Table 3). A 1 SD increase in cotinine levels was associated with elevated EOS count (ORs of 1.14, 1.14 and 1.24 in the unadjusted model, model 1 and model 2, respectively). Correlation analyses revealed significant positive correlations between cotinine and EOS levels (Spearman’s rho: r = 0.074, P < 0.0001; Kendall’s tau: r = 0.097, P < 0.0001).

Table 3: Adjusted ORs and 95% CIs for the association of cotinine levels with EOS count (n = 12766).

#P > 0.05 Abbreviations: FeNO: fractional exhaled nitric oxide; EOS: eosinophils; ICS: inhaled corticosteroids; CI: confidence Interval; OR: odds ratio.

Logistic regression model 1 included covariates of cotinine, age, sex, BMI, race and FeNO level. Logistic regression model 2 included covariates of use of oral or inhaled steroids within 2 days, development of respiratory symptoms within 7 days, consumption of NO-rich food within 3 h, engaging in strenuous exercise within 1 h prior to testing, asthma, chronic bronchitis and covariates in model 1.

Association of EOS Count with FeNO Levels

This study revealed a positive association between EOS count and FeNO levels. The participants were categorized into quintiles based on the EOS counts (Table 4). The results showed that a 1 SD increase in EOS count was significantly associated with higher FeNO levels (>25 bbp) (OR 1.55 [1.48, 1.62], 1.53 [1.46, 1.60], 1.35 [1.29, 1.43] and 1.43 [1.15, 1.59] in the unadjusted model, model 1, model 2 and model 3, respectively).

Table 4: Adjusted ORs and 95% CIs for the association of EOS count with FeNO levels (n = 12766).

#P > 0.05 Abbreviations: FeNO: fractional exhaled nitric oxide; EOS: eosinophils; ICS: inhaled corticosteroids; CI: confidence interval; OR: odds ratio; SD: standard deviation

Multivariate linear regression model 1 included covariates of EOS, age, sex, BMI, race and cotinine level. Multivariate linear regression model 2 included covariates of use of oral or inhaled steroids within 2 days, development of respiratory symptoms within 7 days, consumption of NO-rich food within 3 h, engaging in strenuous exercise within 1 h prior to testing, asthma, chronic bronchitis and covariates in model 1.

Mediation Analyses

As shown in Table 5, significantly mediated effects by EOS were observed on the association of cotinine levels with FeNO levels. Increased EOS count significantly mediated 13% of the cotinine-associated reduction in FeNO levels. Mediation analyses were conducted using the R programming language.

Table 5: Mediated effects by EOS on the association of cotinine levels with FeNO levels (n = 12766).

Abbreviations: FeNO: fractional exhaled nitric oxide; EOS: eosinophils; CI: confidence interval; OR: odds ratio; SD: standard deviation.

Discussion

This study revealed that participants with the highest quintile of cotinine levels (≥105 ng/ml) exhibited decreased FeNO levels compared with those with the lowest quintile of cotinine levels (0.11 ng/ml, indicating no tobacco exposure). Compared with participants with the lowest quantile of cotinine levels, no significant difference was observed in FeNO levels in those with Q2, Q3 and Q4 cotinine levels (P > 0.05). Sensitivity analyses conducted among participants with asthma, recent respiratory symptoms and chronic bronchitis revealed consistent findings. Similar results were obtained across the US population after the data were weighted. A positive association between high tobacco exposure and EOS count was observed. EOS mediated the cotinine-associated decrease in FeNO levels. Chronic airway inflammation and acute airway inflammation are associated with increased FeNO levels [14-17]. Additionally, exercise [18-20] and consumption of NO-rich foods [21-22] can cause changes in FeNO levels. Therefore, in this study, we included variables such as engaging in strenuous exercise within 1 h, consumption of NO-rich foods within 3 h, asthma, chronic bronchitis and with respiratory symptoms within 7 days as covariates in the model.

Some studies have indicated that smoking can lead to a decrease in FeNO levels [23,24] and alter airway inflammation types. These studies confirm our research findings. However, another study showed no remarkable difference in FeNO levels between smokers and non-smokers [8]. Previous studies have categorised participants into smokers, former smokers and non-smokers but failed to assess the extent of tobacco exposure. Consequently, various studies may yield conflicting conclusions. Furthermore, previous studies employed small samples that lacked representativeness. In our research, we utilised a nationally representative large sample of the adult population in the US to explore the association of tobacco exposure with FeNO levels. We employed serum cotinine level as a reliable measure to evaluate the extent of exposure to environmental tobacco smoke. Participants were categorised into quintiles based on cotinine levels: Q1 (0.011–0.0185), Q2 (0.0185–0.075), Q3 (0.075–125), Q4 (125–309) and Q5 (≥309). This approach allows us to understand the distribution of tobacco exposure levels among participants across different quintiles. We can effectively understand the trend in the effect of cotinine levels on FeNO levels by investigating the regression relationship across different quantiles. Our study demonstrated that high exposure to tobacco smoke is associated with lower FeNO levels. Ashley et al. [25] used data from the NHANES 2007–2012 to investigate the association of tobacco exposure with FeNO levels in non-smoking adolescents and found that tobacco exposure was associated with lower FeNO levels, consistent with the results of the current study. The research population in their study was a specific cohort of non-smoking adolescents. Our study further explored the effects of smoking on EOS count and the mediating role of EOS, providing a reference for the mechanism by which tobacco exposure leads to lower FeNO levels.

EOS and FeNO have been utilised as indicators of type 2 airway inflammation as well as for identifying patients experiencing asthma exacerbations [26,27], guiding corticosteroid therapy during the exacerbation of COPD ²⁸ and determining the suitability of ICS therapy regimens [29-32]. Their importance in COPD treatment is paramount [33,34]. Considering that smoking can influence airway inflammation, there has been increasing interest in exploring the association between blood EOS counts and smoking habits. Current smoking is significantly associated with EOS counts ≥210 cells·μl⁻¹ (OR, 1.72). Colak et al. [35] reported that a history of smoking is associated with a blood EOS count ≥300 cells·μl⁻¹; however, the association between current cumulative tobacco exposure and EOS count remains uncertain. Our study revealed that EOS counts were elevated in participants with Q4 and Q5 serum cotinine levels. In a study involving the Copenhagen general population, Pedersen et al. [36] showed that high cumulative and daily tobacco consumption in current smokers was associated with substantial increases in EOS counts in a dose-dependent manner. However, their minimum reference value was <10 g/day of tobacco consumption. The reference for our study was the absence of tobacco exposure.

Although EOS and FeNO serve as markers for type 2 airway inflammation, they represent different aspects of this condition [37-40]. FeNO level reflects airway IL‐13 activity, whereas blood EOS count reflects systemic IL‐5 activity [22]. FeNO level is correlated with increased induced sputum levels of airway type 2 cytokines, chemokines and alarmins. In contrast, blood EOS counts are only correlated with serum IL‐5 levels in the sputum [41]. Tobacco exposure may cause a decrease in FeNO levels and an increase in EOS count through different signalling pathways. The exact mechanism underlying this effect remains unclear. The mediated analysis in the current study showed that EOS counts mediated the cotinine-associated reduction in FeNO levels, But more research is needed to confirm this. First, the effect of tobacco exposure on type 2 airway inflammation was corroborated, and the range of tobacco exposure levels that influence changes in airway inflammation types was further analysed. Second, the potential mechanism underlying the alterations in EOS counts and FeNO levels induced by tobacco exposure was investigated, revealing that EOS mediated the cotinine-associated reduction in FeNO levels. This result provides valuable insights for further elucidation of related mechanisms. Third, previous studies [23-24] have employed past and current smoking as indicators for classifying tobacco exposure. Such an approach is considered overly general and overlooks second-hand smoke exposure. In contrast, our study substituted serum cotinine levels to assess tobacco exposure levels, yielding results with increased accuracy. Finally, relevant adjustments were made by incorporating potential confounders.

Inevitably, this study had certain limitations. First, the effect of long-term smoking accumulation and duration of quitting smoking on EOS count was not considered (21). Environmental pollutants are associated with FeNO levels [42-45]. Furthermore, allergic rhinitis, eosinophilic esophagitis, atopic rhinitis and food allergy are associated with elevated levels of FeNO [46-50]. However, the NHANES 2007–2012 database lacks data on environmental pollution, allergic rhinitis, eosinophilic esophagitis, atopic dermatitis and food allergies. Second, smoking induces neutrophilic inflammation. The effect of smoking-related neutrophil inflammatory factors on FeNO levels requires further investigation. Third, disease history relies on self-reporting, which is susceptible to individual subjectivity. Finally, the utilisation of retrospective data may lead to data loss, measurement errors and inaccuracies. In conclusion, this study demonstrated that tobacco exposure can cause a decrease in FeNO levels and an increase in EOS counts. The smoking status should be considered when evaluating type 2 airway inflammation using FeNO and EOS count. The reduction in FeNO levels due to tobacco exposure is partially mediated by EOS. High levels of tobacco exposure can lead to a distinct type of airway inflammation characterised by elevated EOS count but decreased FeNO levels. This airway inflammation type should be classified as a subtype separate from typical airway eosinophilic inflammation. These findings provide clinicians with a scientific basis for the diagnosis, treatment and management of patients with airway inflammation.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Funding

This study was supported by Scientific Research Fund Project of Hunan Provincial Health Commission of China (No. D202303028856).

Data Availability

The data underpinning this article were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. The datasets utilized and analyzed in the present study are available from the corresponding author upon reasonable request.

Authors’ Contributions

Xingfang Hou, Shufen Hou, Chenggong Hou, Xuelian Chen, and Yuling Tang conducted this study. Xingfang Hou was responsible for the conceptualization, methodology, investigation, formal analysis, preparation of the original draft, provision of resources, and visualization. Shufen Hou and Chenggong Hou contributed to data curation and investigation. Yuling Tang and Xuelian Chen provided supervision, reviewed the manuscript, and managed project administration. Xingfang Hou, Yuling Tang, and Xuelian Chen were designated as guarantors of the paper, ensuring the integrity of the work from inception to publication. All authors reviewed and approved the final manuscript.

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DOI: 10.31038/GEMS.2025713

Abstract

Raman studies on a large cassiterite sample from Zinnwald, E-Erzgebirge/Germany, brought some surprising results to light. To these belong the ¹³C-rich diamonds and graphite, as well as other minerals, first and foremost as high-pressure and high-temperature orthorhombic cassiterite. Because there are also ¹²C-rich diamonds in the root zones in a crystal present, especially in a large cassiterite crystal from Ehrenfriedersdorf, we assume at least two distinct pulses with varying isotopes of carbon (¹²C versus ¹³C) in the supercritical fluids (SCGF) coming from the earth’s mantle. First came ¹²C-rich and later ¹³C-rich supercritical fluids. If so, other isotopes can also effectively be separated in supercritical fluids.

Keywords

Raman spectroscopy, ¹³C-rich diamond, Orthorhombic cassiterite, Variscan tin deposits, Supercritical fluids, Isotope separation

Introduction

A presentation about the 800 years of mining activity and 450 years of geological research in the Erzgebirge/Krǔsné hory region given by Breiter (2014) [1] shows, among other things, the extensive tin exploration and the origin and relationship of tin deposits with granite magmatism. According to this classic work by many scientists, there are no questions about the genesis of this type of ore deposit. It seems that all problems are solved, which is not the case. Thomas (2024a and 2024b) [2,3] has, however, shown that the origin of the Variscan tin deposits must be newly scrutinized. The first doubts came from the intensive work on the tin deposit Ehrenfriedersdorf presented in Schütze et al. (1983) [4]. However, their conclusions are not conclusive, at least speculative. The first concrete proof came from Thomas (2024a) [2]. In this publication, we will show that the proofs of mantle participation via supercritical fluids or melts up to now are no exceptions. We classify the supercritical fluids or melts according to Ni et al. (2024) [5] as supercritical geofluids (SCGF).

Sample Materials Microscopy and Raman Spectroscopy: Methodology

Sample Material

A sample from Zinnwald (Figure 1) clearly shows two parts of cassiterite composed of an opaque part (2/3 in volume) and a transparent cassiterite-brown nearby pale part (1/3 in volume). This cassiterite contains fluid inclusions that homogenized at about 386°C (see Thomas 1982) [6] in the liquid phase (with 15 equivalent % NaCl). In the black part, no fluid inclusions are present.

Figure 1: Cassiterite sample (Sn-23) from Zinnwald, E-Erzgebirge/Saxony. All black parts are orthorhombic cassiterite (about 2/3 in volume). The transparent brown zones contain tetragonal cassiterite parts.

The pale part of cassiterite contains many small black to colorless (~10 µm in diameter) spherical crystals of graphite and diamond. The black part of that cassiterite contains pyrrhotine and pyrite, as well as diamond and graphite inclusions, which are relatively stable against hydrothermal activity. The sample is from the Mining Academy Freiberg. At this place, it is essential to emphasize that graphite-like material in Variscan cassiterites is typical. A description of another cassiterite sample used in this short contribution is from Ehrenfriedersdorf (Sn-70), described in Thomas 2024a [3].

Microscopy and Raman Spectroscopy

We performed all microscopic and Raman spectroscopic studies with a petrographic polarization microscope (BX 43) with a rotating stage coupled with the EnSpectr Raman spectrometer R532 (Enhanced Spectrometry, Inc., Mountain View, CA, USA) in reflection and transmission. The Raman spectra were recorded in the spectral range of 0–4000 cm⁻¹ using an up-to-50 mW single-mode 532 nm laser, an entrance aperture of 20 µm, a holographic grating of 1800 g/mm, and spectral resolution ranging of 4 cm−1. Generally, we used an objective lens with a magnification of 100x: the Olympus long-distance LMPLFLN100x objective (Olympus, Tokyo, Japan). The laser power on the sample is adjustable down to 0.02 mW. The Raman band positions were calibrated before and after each series of measurements using the Si band of a semiconductor-grade silicon single-crystal. The run-to- run repeatability of the line position (based on 20 measurements each) is ±0.3 cm⁻¹ for Si (520.4 ± 0.3 cm⁻¹) and 0.4 cm⁻¹ for diamond (1332.7 cm⁻¹ ± 0.4 cm⁻¹ over the range of 80–2000 cm⁻¹). The FWHM = 4.26 ± 0.42 cm⁻¹. FWHM is the Full-Width at Half Maximum. We also used a water-clear natural diamond crystal (Mining Academy Freiberg: 2453/37 from Brazil) as a diamond reference (for more information, see Thomas et al. 2022 [7] and 2023 [8]).

Results

Diamond in Cassiterite

During the microscopic study of the cassiterite sample Sn-23 from Zinnwald, we found (besides fluid inclusions) many spherical mineral inclusions. Often, these inclusions were, according to Raman spectroscopy, diamond and/or graphite. Figure 2 shows such typical inclusion (insert right above in Figure 1) and the accompanying Raman spectrum. Conspicuously is the Raman doublet at 1309 and 1514 cm⁻¹, which is characteristically for a very ¹³C-rich diamond (see Blank et al. 2016) [9]).

Figure 2: Raman spectrum of lonsdaleite in pale-colored cassiterite (Sn-23). The photomicrograph shows the ¹³C-rich diamond crystal (30 µm deep) in the cassiterite matrix as well as ¹³C-rich graphite (G-band at about 1514 cm^-1). The Raman spectrum was taken with 5.0 mW laser power on the sample (15 minutes exposure) – see Blank et al. 2016 [9].

Because this type of diamond and graphite is currently untypical, we have performed further Raman measurements. The results on 18 different diamond inclusions and the belonging graphite are in Table 1 compiled.

1. See Methodology
2. According to Gutierrez et 2014 [10] and Thomas et al. (2021) [11].
3. Gr – graphite (about 73 µm deep)

Table 1: Results on diamond and graphite in the cassiterite (Sn-23) from Zinnwald and references.

Besides the diamonds with a marked G-band at about 1522 cm⁻¹, there are also diamonds without such a G-band (Figure 3).

Figure 3: ¹³C-rich diamond in cassiterite (Sn-23) from Zinnwald without graphite band. The Raman spectrum was taken with 1.0 mW laser power on the sample (15 minutes exposure).

Figure 4 shows a Raman spectrum of ¹³C-rich diamond with an outlined ¹³C-rich graphite G band at 1527 cm⁻¹.

Figure 4: Raman spectrum of ¹³C-rich diamond in cassiterite (Sn-23) from Zinnwald (30 mW on sample). The Raman band at 1527 cm^-1 is the G band from the ¹³C-rich graphite (see Gutierrez et al. (2014) [10]).

Figure 5 shows the relationship between the laser energy on the sample and the band position of the first-order diamond band. We see clearly that the values at the low energy (0.92 mW) represent the best values for the estimation of the ¹³C concentration. The data in Figure 5 shows a linear correlation: Band position = 1310.53 + 0.16871 * mW. The extrapolation to the lowest value of 0.92 mW results in a value of 1310.7 cm⁻¹. According to Anthony and Banholzer (1992), the first-order Raman peak position has a ¹³C content of the diamond of about 50%. For a natural diamond that is very high, and if we assume that this diamond represents the quasi-frozen state from the deep, it follows, according to Schiferl et al. (1997) [12], a minimum pressure of about 7 GPa.

Figure 5: Correlation of the Raman shifts with the laser energy used on the sample

Orthorhombic Cassiterite Bearing ¹³C-rich Diamond

The relatively large cassiterite crystal aggregate (Figure 1) from Zinnwald/Erzgebirge/Germany, sample Sn-23, contains large parts of different orthorhombic cassiterites. Tetragonal cassiterite is not present or only in traces in the whole sample Sn-23. It is well known that the polymorphs of cassiterite can easily be transformed into another (Balakrishnan et al., 2022) [13]. Therefore, different polymorphs can be present side by side, which makes the differentiation difficult. Figure 6 is an example of a more tetragonal cassiterite (with dominant indications of the Pbcn-type: 75.0, 124.8, 245, and 472.6 cm⁻¹). The strong Raman band at 75.0 cm⁻¹ is untypical for tetragonal cassiterite (see Figure 5 in Thomas 2024b) [3].

Figure 6: Raman spectrum of light cassiterite from the edge of sample (Sn-23)

Figure 7 shows the Raman spectrum of more dark cassiterite from the center of the plate (Sn-23 from Zinnwald). The strong band at 75.0 cm⁻¹ corresponds, according to Thomas 2024b, to a pressure of about 10.5 GPa.

Figure 7: Raman spectrum of dark cassiterite from the center of the crystal plate (Sn-23 from Zinnwald)

Figure 8: Raman spectrum of diamond in orthorhombic cassiterite from Ehrenfriedersdorf – sample Sn-70 (size 4 x 2 cm). The Raman band at 1284 cm^-1 corresponds to an almost isotopic pure ¹³C diamond, which is according to Enkovich et al. 2016 at 1283.1 cm^-1. The G-band is at 1519 cm^-1.

The very strong Raman band 121.4 cm⁻¹ (122.7 ± 1.02 cm⁻¹; n = 6) results in a pressure of 21.9 GPa (see also Helwig et al. 2003 [14] and Thomas 2024b [3]). By the mixture of different parts of high-pressure and high-temperature SnO₂ polymorphs of rutile-type→CaCl₂– type → pyrite-type → ZrO₂ orthorhombic phase I → cotunnite- type (Balakrishnan et al. (2022) [13] and Shieh et al. (2006) [15]) demonstrate that high-pressure phases (CaCl₂– and cotunnite-type) are essential pieces of evidence for the transport of this ore mineral from mantle depths to the crust region. The presence of ¹³C-rich diamonds in all parts of this Zinnwald cassiterite sample (Sn-23) supports this statement. Noteworthy is also the general presence of graphite and traces of Fe, Ta, Nb, Ti, Mn, Fe, and Zr (Betechtin, 1964) [16], which make the determination of the polymorphs of cassiterite a little bit difficult by the shift of the Raman bands.

Interpretation

The clear evidence of ¹³C-rich diamonds in orthorhombic cassiterite from Zinnwald demonstrates clearly that a lot of cassiterite or tin comes directly from the mantle range. The old genetic thinking about the origin of the Variscan tin deposits of the Erzgebirge/Germany alone from the surrounding granite is, therefore, questionable.

Up to now, we have found mainly ¹²C-rich diamonds in cassiterite (Thomas 2024a, 2024b – [2,3] and Thomas and Rericha 2025) – [17] from Ehrenfriedersdorf in the Central Erzgebirge/Germany, in the cotunnite-type cassiterite from Krupka (Krušné hory Mining District/ Czech Republic, and the Slavkovský les, North Bohemia (Czech Republic). Figures 2 and 4, as well as Table 1, clearly show that the diamond in the here-discussed case is ¹³C-rich because the typical G band of graphite lies at significantly lower values. That is also valid for the main crystal of cassiterite Sn-70 from Ehrenfriedersdorf in the central Erzgebirge.

Table 2 shows the measured data on the ¹³C-rich diamond in cassiterite from Ehrenfriedersdorf, Central Erzgebirge, Germany, as well as the data for isotope pure diamond and graphite according to Enkovich et al. (2016) – [18] and Gutierrez et al. (2014) – [10].

Table 2: Raman bands of ¹³C- and ¹²C-rich diamonds and graphite, according to Gutierrez et al. (2014) [10] and Enkovich et al. (2016) [18]. The values for the diamonds in cassiterite from the Sauberg mine near Ehrenfriedersdorf (Sn-70) are based on this work (6 crystals).

From a first approximation, according to Enkovich et al. (2016) [18], the ¹²C-richer cassiterite Sn-70 has a value of ^12.6C (^12.5C has an isotopically mixed 1:1 composition). The finding of clear proofs for ¹³C-rich diamond and graphite in cassiterite from Zinnwald forces the assumption of two different pulses of supercritical fluid (SCGF): the first one is in ¹²C enriched, and the second one is in ¹³C enriched. In the Sauberg mine near Ehrenfriedersdorf, we found diamonds in a cassiterite crystal that were very rich in ¹³C. However, the root zone of the same crystal dominates in ¹²C-rich diamonds (Thomas 2024a) [2].

Discussion

The presence of orthorhombic cassiterite up to the cotunnite polytype, as well as the frequent occurrence of ¹²C- and ¹³C-rich diamonds in different minerals, here in cassiterite, forces a re- thinking of the old genetic concept of the formation of the Variscan tin deposits in the Erzgebirge/Germany and the Krušné hory Mining District/Czech Republic. Furthermore, if so, other isotopes can also effectively be separated in supercritical fluids (SCGF). Also, another point is essential: with the widespread SCGFs in the whole Variscan Erzgebirge region, an enormous amount of water comes from the mantle into the crustal region.

Acknowledgment

For the samples, I thank Professor Ludwig Baumann (1929-2008) from the Mining Academy Freiberg, who initiated my interest in the genetic aspects of the Variscan tin deposits, too. Paul Davidson (Hobart, Tasmania) and Jim D. Webster (AMNH; New York) stimulated my critical thinking regarding supercritical fluids. The nearby daily discussion with Adolf Rericha (Falkensee) forced my intense Raman work.

References

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10. Gutierrez G, Le Normand F, Aweke F, Muller D, Speisser C, et al. (2014) Mechanism of thin layers graphite formation by ¹³C implantation and Appl Sci. 4: 180-194.
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DOI: 10.31038/GEMS.2025712

Abstract

The Lorendian distribution, also known as the Cauchy distribution, is commonly used in statistical physics and geostatistics to describe here the distribution of some aspects of elements. When applied to the study of tin in the Ehrenfriedersdorf deposit, the Lorendian distribution provides valuable insights into the spatial distribution and concentration patterns of tin within this historic mining area. Furthermore, the Lorendian distribution shows that Sn is extraordinarily soluble and concentrated in the fluid/melt on the critical point corresponding to 25.7% H₂O of the pegmatite solvus curve (T vs. H₂O in the melt) and in the immediate vicinity of this point, determined by maximal concentration I_c at the critical point x_c and the width (half width at half maximum (HWHM) of the curve in the melt-H₂O system). The strong Lorendian relationship between Sn and water and especially the strong relationship to the critical point (C.P.) of the silicate melt shows convincing that Sn is related to the supercritical fluid coming from mantle depth, also instigated by the occurrence of diamond, lonsdaleite, ¹³C-rich graphite, and orthorhombic cassiterite, e.g., as cotunnite-type cassiterite. We also show that other elements (Be, B, P, Cl, Zn, As, Cs, Sn, Ta, and W) have similar behavior in water-rich silicate melt systems.

Keywords

Lorentzian curve, Sn distribution, Solvus curve, Rb and Cs enrichment, Sauberg mine, Ehrenfriedersdorf/Germany

Background

The origin of tin deposits and the nature of the Sn transport in the past are often discussed controversially (e.g., Kosals 1976 [1], Liu et al. 2020 [2]). Before Liu et al. (2020) [2], the discussion was mostly restricted to the solubility of SnO₂ in water-rich solutions up to 400°C. Ehrenfriedersdorf, located in the Central Erzgebirge region of Germany, has a rich history of tin mining dating back to the medieval period [3]. The deposits around them have very complex geology and mineralization patterns, which make them an ideal candidate for applying advanced analytical and statistical methods such as the Lorentzian distribution to analyze their tin content and its origin. The surrounding granite was discussed, in the past, exclusively as the source of Sn (primarily cassiterite). At the beginning of the intensive analytical study of melt inclusions in granite and pegmatite quartz from the Ehrenfriedersdorf, starting in 1995 [4], we often observed high runaway trace element data that could not interpreted. With time, the amount of such runaway data increases significantly. A correlation with other elements was not possible at that time [5]. Beginning with the determination of water in glasses and melt inclusions (Thomas, 2000) [6], the situation improved from year to year (see also Thomas 2024a) [7]. It appears fast that many main and trace elements in melt inclusions show strong correlations with the water in them. Thomas et al. (2019 and 2022) – [8,9] showed that many elements correlate with water of the melt inclusions according to Gaussian and manly Lorentzian curves. Furthermore, it was revealed that often, the critical point (C.P.) of the solvus curves (correlation of the water concentration in the silicate melt and the homogenization temperature) and the sharp peak of the Lorentzian curve coincide. The connection between the solvus curve and the Lorentzian curve is illustrated by Thomas and Rericha (2024a) [10] in Figure 2b in there.

Characteristics of the Lorentzian Distribution for Tin

The Lorentzian curve (according to Hendrik Antoon Lorentz (1853-1928), a Dutch physicist) is a mathematical function that describes a peak (here the maximal Sn concentration in ppm) centered at a specific position (here the critical water concentration (25.7 %) in the silicate melt) and is characterized by w, which is the half-width at half maximum (HWHM).

The Lorentzian curve is, in our case:

I(x) = Ic * (w/2)²/((x-x_c)² + (w/2)²)                                 (1)

With:

I(x) height of the Sn concentration (ppm) at the water concentration x (%),

I_c     maximal concentration of the Sn (ppm) at x_c (center), identical with the C.P. of the complete system,

w    width is the half-width at the half-maximum (HWHM). x water concentration (%),

x_c    water concentration (%) at the critical point (solvus and Lorentzian curves),

y₀ offset is the shift of the curve from its original position along the y-axis), which is the Clarke value for Sn (3 ppm according to Rösler and Lange, 1975 [11],

A area – is the result of integrating the Lorentzian curve over all values of x,

R²     correlation coefficient.

The following Figure 1 shows the typical form of the Lorentzian distribution of tin (in ppm) versus water concentration (in %) in melt inclusions from the pegmatite system in the Sauberg tin deposit near Ehrenfriedersdorf. For the Lorentzian fitting, we used the Origin 6.1 program. As we can see from Table 1, using equation (1), the calculated Lorentzian curve is identical.

Figure 1: Tin distribution in melt inclusions in quartz from the granite-pegmatite system (Sauberg mine near Ehrenfriedersdorf). The maximal Sn concentration is 16300 ppm at a water concentration of 25.7%.

Figure 1 shows the Lorentzian distribution of Sn vs. H₂O, and in Table 1 are the resulting data summarized. The center (25.7 % H₂O) is the position of the peak’s center, which corresponds to the critical point of the solvus curve and the maximum (height) of the Sn concentration (here, 16400 ppm Sn). Width is the half-width at half-maximum (HWHM). The offset refers to the displacement of the Lorentzian curve from its original position along the x-axis (H₂O concentration) corresponding to 644 ppm.

Table 1: Lorentzian fit of Sn, determined in silicate melt inclusions from the pegmatite system of the Sauberg mine near Ehrenfriedersdorf (46 measuring points). Each point is the mean of 5 to 10 single measurements. The values in the second data row are calculated using Equation 1.

The offset of the calculated data is, of course, zero – the value of 603 ppm Sn results from the average difference between measured and calculated Sn values. By the way, equation (1) of the Lorentzian curve can be advantageously used for the analyses of analytically determined outlayers. The offset for the calculated case is zero. However, the difference between measured and calculated Lorentzian curves results in a value of 603 ppm Sn. The error (standard deviation) of the offset is ± 348 ppm Sn and results mainly from the values at the tails. The offset represents the concentration of Sn in the surrounding rocks, introduced by the supercritical fluids/melts.

Interpretation of the Results

The application of the Lorentzian distribution to the analytically determined tin and water concentrations in the Ehrenfriedersdorf deposit reveals a significant clustering of tin-rich zones. The location parameter “center” indicates the central area of the highest Sn concentration of 16300 ppm and corresponds with the solvus crest (C.P.) of the water-silicate system, which lies for Sn at 25.7 % H₂O in the silicate melt. The low value for the C.P. of the solvus is beside the water content in the melt, the result of the sum of further compounds (Be, B, F, P, rare alkalis, and others) in the volatile-rich melt. The position of the critical point varies a little bit by the variation of the critical elements and compounds. For Be, BO₃, PO₄, Cl, Zn, As, Cs, Sn, Ta, and WO₄, the result from 355 measurements for the critical point lies at 26.9 ± 1.30 % H₂O in the silicate melt (see Table 2 and Thomas et al. 2022 [9], and Thomas and Rericha 2023) [12]. Besides H₂O, B, rare alkalis (Rb, Cs), and P, fluorine likewise has a determining meaning for the position of the solvus crest as well as the maximum of the Gaussian and Lorentzian curves in the granite-pegmatite system in the Ehrenfriedersdorf district.

Table 2: Lorentzian fit of elements and compounds, determined in silicate melt inclusions from the pegmatite system of the Sauberg mine near Ehrenfriedersdorf. Each point (n) is the mean of 5 to 10 single measurements. For the calculation, we used the Origin 6.1 program. The units are in Table 1.

Cs in granite from Ehrenfriedersdorf; n – the sum of measurements.

In contrast, the scale parameter “width” (2.3 % water for Sn) provides information on the spread and extent of these high- concentration zones related to the C.P. and the influx of the supercritical fluid at this point. The “offset” is the shift in direction y (Sn-concentration) of the Lorentzian curve from the original position along the x-axis (water concentration). The value of the offset (644 ppm Sn) interprets we as the minimum enrichment of Sn against the value of tin in the surrounding Sn-poor granite system, which is, according to Hösel (1994) [13], ≤ 200 ppm Sn (as a result of redistribution and ore deposit formation. The “height” represents the solved maximal Sn concentration in the supercritical fluids (SCGF). Note that the SCGF also carries solid minerals like orthorhombic cassiterite, diamond, and graphite. The interpretation of the “area” is a bit difficult. The area under the Lorentzian curve represents, in our case, the total concentration of Sn, here 57799 ppm² in the studied granite-pegmatite system. If we take the Clarke value of Sn for granites, which is three ppm (Rösler and Lange, 1975) [11], then we have an exceptional enrichment of tin in the considered granite-pegmatite system of nearby 20.000 fold. That is remarkably high. Alone, the Sn concentration comes directly with a supercritical fluid, which is about the 5500-fold amount of the Clarke number, is dramatically high. The regular presence of carbon (as C, CO₂, CH₄ ) in the SCGF shows plausibly that the Sn transport occurs as Sn²⁺, probably as hydroxocomplexes like ([Sn(OH)_x]^n-) or as Sn⁴⁺ in more complicated compounds as (Na, K)[Sn(OH, Cl, F)₃CO₃] (Kosals, 1976). Rb and Cs can take the place of Na and K (see Thomas and Rericha, 2023) [14]. Cs increase the number of the H₂O molecules in the surrounding OH shell (Pietsch, 1938) [15] up to 13. It is necessary to stress that SCGFs show very different solvency caused by density- dependent solubility, enormously changed polarizability, strongly increased diffusion rates and reactivity, and extremely low viscosity.

One point is here also important: if, for example, one element is in two compounds with different anions present, then the formation of overlapping Lorentzian curves with different x_c values is possible (see Figure 3 in Thomas and Rericha, 2024b) [16,17]: the distribution of Be in two distinct species, beryllonite versus hambergite with x_c1 at 25.5 % H₂O and x_c2 at 31.0 % H₂O. From the Lorentzian curve type for some elements, it follows clearly that their solubility is not directly related to the water concentration in the melt. The highest solubility is clearly associated with the H₂O concentration, representing the critical point (C.P.). At this point, the melt inclusion-bearing quartz grows very fast and traps “samples” of the surrounding medium as melt inclusions. Through fast processes, the boundary layer composition changes steadily (fast diffusion in a low viscous SCGF). Therefore, the composition of the single melt inclusions changes too (see, e.g., Borisova et al., 2012 [18], Table 2 in them, representing two different melt inclusions lying together in one growth zone). Lorentzian distributions of elements are not only water-rich silicate melt-related. Another example is the relationship of Cs, Rb, and B in the Ehrenfriedersdorf granite. Figure 2 shows such a plot. As Table 3 shows, the concentration of the rare alkalis Rb and Cs is very high in granite from the Sauberg mine near Ehrenfriedersdorf.

Table 3 shows the data for this passable Lorentzian plot and demonstrates that B- Rb and Cs-rich melt fractions are generated and penetrate the present granite rock. An exact interpretation of this remarkable plot (Figure 2) is, at the moment, not possible. More analytical, microscopical, and Raman work is necessary.

Figure 2: Lorentzian distribution of Cs versus boron obtained from melt inclusions in granite quartz from the Sauberg mine near Ehrenfriedersdorf.

Table 3: Cs and (Rb + Cs) distribution in melt inclusions in quartz from the granite- system (Sauberg mine near Ehrenfriedersdorf). The maximal Cs and Rb + Cs concentrations are 58477 and 65756 ppm (sum = 12.4%) at a boron concentration of 2.26%.

Rb gives no Lorentzian distribution with B₂O₃ – therefore, the low R² value using the sum of Rb and Cs.

Alone from the small number of examples, we see that the geochemistry of the Variscan Sauberg mine is highly complex. The standard correlation of elements gives no satisfactory answers to the complex processes working at the formation of this tin deposit (see Author Collective, 1963) [19]. Regardless of the input of SCGF, the genetic origin of this tin deposit is not solvable. Primary the tin mineralization in Ehrenfriedersdorf is not uniformly distributed but instead concentrated in specific geological structures, here pegmatites or pegmatite-like bodies, influenced or generated by supercritical fluids or melts (in short, geofluids (SCGF)). The classic idea of the hydrothermal origin of cassiterite is now untenable. The sharp Lorentzian peak related to the critical point of the solvus curve is a strong hint of the impact of supercritical fluids coming from mantle deeps (with temperatures >1000°C). This idea is supported by orthorhombic polymorphs of cassiterite (e.g., the cotunnite-type cassiterite inclusions in cassiterite crystallized at a pressure of about 10 – 40 GPa (Thomas 2025) [20]. Note, however, that orthorhombic cassiterite is metastable at low pressures. The transition into tetragonal cassiterite is mostly irreversible by kinetic barriers. Such a barrier may be the “shock-like” transition from the supercritical to the under- critical state. The occurrence of orthorhombic cassiterite is supported by the occurrence of diamond, lonsdaleite, and graphite inclusions with extreme isotope composition: ¹³C-rich diamond and graphite versus ¹²C-rich ones. The SCGFs are excellent media for the potent and selective separation of isotopes (e.g., ¹²C/¹³C or H/D by diffusion effects and solubility differences (see Thomas 2024b [16,17], 2025 [20] and Thomas and Rericha 2024a) [10].

Implications for Mining and Exploration

Understanding the Lorentzian distribution of tin in the Sauberg deposit has practical implications for mining and exploration in the whole Variscan Erzgebirge/Krušnohořy region. By identifying the central and peripheral zones of tin concentration, mining operations are optimizable to target the most economically viable areas. That can lead to more efficient extraction processes and reduced operational costs. The highest Sn concentration is related to rock parties rich in pegmatites or pegmatite-like bodies (see Schröcke, 1954) [21]. Furthermore, the substantial input of Sn by SCGF changed the old ideas of the dominance of tin granites for the generation of workable deposits. The high level of tin in the granites in question is the result of the interaction between SCGF and the granites. The high water and Sn content at the critical point (C.P.) favor the formation of pegmatites and pegmatite-like structures with this element and others (see above). It is quite conceivable that under the famous Pb-Zn deposit of the Freiberg mining district (Rösler et al. 1968) [22] is a large tin deposit. Hints are REE-rich fluorite globules like that from Zinnwald (Thomas 2024c) [23] in yellow low-temperature fluorite from Halsbrücke near Freiberg and the reverse of very high concentration of Pb (1790 ppm) [as cotunnite], Zn (85120 ppm) [as (K, Pb, Cs)₂ Zn(Cl, Br)₄] and Ag (100 ppm) [as chlor-, brom- and iodargrite] in high-temperature fluid inclusions in quartz of the Sauberg mine (see Boriosova et al. 2012 [18] and Thomas et al., 2019 [8]). The Clarke values for Pb, Zn, and Ag are, according to Rösler and Lange (1975) [11], 20, 40, and 0.05 ppm, respectively [24].

That interpretation results from the Lorentzian distribution, which offers insights into the geological processes that led to the formation of the tin deposits. The distribution pattern may reflect the influence of SCGF, their way from the mantle into the crustal regions, which later formed fault structures and other geological factors that concentrated tin in specific areas over geological periods via hydrothermal re- deposition and crystallization as cassiterite. Tin is highly soluble in SCGFs, as we can see from their Lorentzian distribution. However, an essential part of tin comes from mantle depths (10 – 40 GPa) as solid phases like the cotunnite polymorph of cassiterite. It is well- known that the high-pressure and high-temperature polymorphs of cassiterite, e.g., orthorhombic phases, are formed at 10 – 40 GPa or higher. Typically, they transform back to tetragonal cassiterite upon decompression. As we see in many examples (Thomas, 2025 [25] and Thomas and Rericha, 2023 [12,14]), this process can be inhibited by fast cooling, particularly by the transition from the supercritical to the critical and under-critical state outwit.

Conclusion

The Sauberg tin deposit provides a robust framework for understanding the spatial variability and clustering of tin mineralization. The insights gained from this statistical approach can inform both current mining practices and future exploration efforts, ultimately contributing to the efficient and sustainable exploitation of this valuable resource (at the moment, this is an abandoned mining district). However, we can learn a lot from the two-dimensional element distribution (elements versus water concentration. The distribution of the trace elements in melt inclusions in granites and pegmatites in relationship with the water content of the same inclusions shows clearly Lorentzian curves with the maximum concentration at the critical point of the solvus and that of the Lorentzian curves. These results give robust hints for the origin of Sn and other elements via supercritical fluids (SCGF). A critical rethinking of the origin of this and other analog mineral deposits is necessary.

Acknowledgment

The Author thanks the members of the working group, which builds up for the first time a usable measuring chamber at DESY for the study of micrometer-large fluid and melt inclusions in a quartz matrix. Besides the Author, the following scientists belong to this group: Prof. A. Knöchel (DESY Hamburg), Dr. J. Walter, Prof. Althaus (both from Karlsruhe), Dr. M. Haller, Dr. M. Radtke (both from DESY Hamburg), Dr. W. Klemm, TU Bergakademie Freiberg. Thanks also go to Dr. D. Rhede (GFZ Potsdam) for the longstanding cooperation in the microprobe analysis of melt inclusions and to Dr. J.D. Webster (AMNH New York) for performing the SIMS analyses.

References

1. Kosals Ja A (1976) Main features of geochemistry of rare metals in granitic melts and solutions. Nauka, Novosibirsk. 232 p. (in Russian).
2. Liu Y, Li, J, Chou IM (2020) Cassiterite crystallization experiments in alkali carbonate aqueous solutions using a hydrothermal diamond-anvil cell. American Mineralogist. 105: 664-673.
3. Breiter K (2014) 800 years of mining activity and 450 years of geological research in the Krušné Hory/Erzgebirge Mountains, Central Europe Bol Mus Para Emílio Goeldi Cienc Nat Belém. 9: 105-134.
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7. Thomas R (2024a) Vom Schmelzeinschluss zum superkritischen Fluid – Ergebnisse und Folgen der Befahrung der Grube Sauberg bei Ehrenfriedersdorf. Veröffentlichungen Museum für Naturkunde Chemnitz. 47: 59-66.
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9. Thomas R, Davidson P, Rericha A, Voznyak DK (2022) Water-rich melt inclusions as “frozen” samples of the supercritical state in granites and pegmatites reveal extreme element enrichment resulting under non-equilibrium Mineralogical Journal (Ukraine). 44: 3-15.
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15. Pietsch E (1938) Gmelins Handbuch der anorganischen Chemie. Edition 8. Verlag Chemie, GmbH Berlin. Pg: 104.
16. Thomas R (2024b) 13C-rich diamond in a pegmatite from Ronne, Bornholm Island: Proofs for the interaction between mantle and crust. Geol Earth Mar Sci. 6: 1-3.
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18. Borisova AY, Thomas R, Salvi S, Candaudap F, Lanzanova A, Chemeleff J (2012) Tin and associated metal and metalloid geochemistry by femtosecond LA-ICP-QMS microanalysis of pegmatite-leucogranite melt and fluid inclusions: new evidence for melt-melt-fluid Mineralogical Magazine. 76: 91-113.
19. Author Collective (1983) Isotopen- and elementgeochemische sowie radio- geochronolgische Untersuchungen an der Zinnlagerstätte Ehrenfriedersdorf. AdW of the GDR, ZFI-Mittelungen 76, 271 p.
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DOI: 10.31038/GEMS.2025711

Let us first emphasize some important facts about the mining and mineral resources sector in Egypt:

1. Despite the great potential of the mineral wealth sector in Egypt and the spread of many mineral ores in most Egyptian deserts and in large proportions, this sector does not participate in the national product except by a very small percentage represensing not more than 1% in the country’s national product.
2. Experts and specialists emphasize that mineral wealth represents the third side in building the economies of countries along with agriculture and industry, and from here it is necessary to exploit these resources optimally, according to procedures and measures to activate research and exploration operations, and use the best ways to extract and exploit them in an economic manner.

Sinai Peninsula is a triangle-shaped peninsula located in Egypt that has an area of about 60,000 square kilometers between the Mediterranean Sea (to the north) and Red Sea (to the south). Its land borders are the Suez Canal to the west and the Palestine-Egyptian border to the northeast. The Sinai Peninsula is in Southwest Asia while the rest of Egypt is in North Africa (Figure 1).

Figure 1: Location Map of Sinai Peninsula in the Arab Republic of Egypt

The Sinai Peninsula in the Arab Republic of Egypt is the crossroads of continents and the land of turquoise and the incubator of the most beautiful natural reserves on the planet not only that, God has blessed it with many mineral riches represented in many mineral ores, whether industrial such as cement industry raw materials (limestone, shale, gypsum, iron oxides, sand and gravel), ceramic industry raw materials (feldspar, albyte and kaolin) and ornamental stones (marble and granite) as well as metal ores that are involved in advanced technological industries (copper, lead, zinc, tungsten, molybdenum and manganese) and recently discovered in Sinai some of the precious metals (gold and silver). Sinai is famous for the presence of energy raw materials other than oil, which is coal ore, which is found in many areas, especially the G. Al-Maghara area in North Sinai, as well as the areas of Badaa and Thawra near the Abu Zenima area.

There are also a number of natural salts on the northern coasts of Sinai near the El-Arish City, which produce large quantities of table salt and other industrial salts. The Sinai Peninsula also contains the largest reserves of ultra-pure white sand, which is used in many important industries.

Based on the interest in the reconstruction of Sinai, it was necessary to draw attention to pay attention to its mineral resources and its many treasures and attract investment and reconstruction opportunities to it, so many geological, geophysical and mineralogical studies have tended to discover these mineral resources that can be developed and estimate the reserves of them and the work of many feasibility studies to exploit them optimally.

In this article, the researcher tries to shed light on the most important mineral wealth spread in the Sinai Peninsula in terms of their type and quantities, in order to direct decision-makers and those wishing to invest in the mining sector to the most important mining projects that can be established in the land of Sinai, which helps the emergence of new communities, provide job opportunities and increase the national income of the country.

Among the most important hidden mineral riches in the Sinai Peninsula are:

Turquoise

It is the most famous mineral of the Sinai Peninsula, and is found in the mountains of Wadi Al-Maghara and Sarabit in the city of Al-Tur, and was the first to think about mining turquoise in the last century, Major MacDonald, a retired English officer, in Wadi Al-Maghara in 1854 and built him a house at the foot of a hill inhabited by old miners, and he lived his wife there for five years in collecting the metal, but he did not achieve the success he begged for and died in 1870.

Oil

Petroleum is the most important mineral resource in Sinai. There are many oil fields, including the Gulf of Suez, Belayim, Assal and Abo Rdis, and the region’s reserves are estimated at about 237 million barrels of crude oil and natural gases.

White Sand

White sand is found in the Sinai Peninsula in the area of G. Abu Hittat – Paradise Plateau on the Nuweiba – Saint Catherine road and the Abu Zenima area with a total reserve of up to (155 million cubic meters or 330 million tons) of ultra-pure sand. These sands are involved in many important industries, including: luxury glass types – tableware – white glass – transparent packaging – optical glass – crystal – colored glass and others.

Ornamental Stones

Ornamental stones, especially granite of various kinds, are spread in the areas of Saint Catherine and Nubia, while marble of sedimentary origin and consisting of hard limestone rocks is found in the areas of Al-Hassana in central Sinai and these raw materials are used for many purposes, including: decorating buildings and facilities – floors – stairs – the manufacture of antiques and statues.

Kaolin

It is one of the distinctive raw materials in Sinai and is located on Nuweiba – Saint Catherine road and the proven reserves of it are estimated at about 15 million tons, as well as the Abu Zenima area, and the reserves are estimated at about 80 million tons and kaolin ores are used in many industries such as: ceramics and Chinese – white cement – medical industries – plastic – refractory bricks and refractories – sanitary ware.

Limestone

It is found in G. Labani, G. Al-Halal, Raysan Unaizah, G. Al-Maghara and G. Al-Jifjafa and is used in the manufacture of cement, chemical industries, fertilizers, paints and in construction and road construction.

Dolomites

It is found around the edges of G. Al-Maghara and G. Al-Halal and is used in construction, road construction and protection of port docks and has many uses, the most important of which are: the production of aggregates necessary for road paving and reinforced concrete, agriculture to improve the soil and restore its acid balance, cement industry, refractories for lining furnaces and molds used in steel production.

Coal deposits

Coal deposits are located in Sinai in G.Al-Maghara area and the proven reserves of it in G. Al-Maghara are 27 million tons, of which about 21 million tons can be mined, and there is also located in Abu Zenima and Oyoun Moussa areas, and the proven reserve has been estimated at about 18.5 million tons, it is used as fuel for power plants and cement factories.

The Carbon Baby

They are natural deposits containing carbon-coal materials, found east of Abu Zenima, and used as fuel in power plants and cement manufacturing. Its reserves are about 75 million tones per square kilometer.

Manganese Deposits

Manganese ores are found in South Sinai in the Um Bojmeh area, and appear as lenses associated with dolomite limestone rocks in the Middle Carboniferous Age, and this area has reserves of about 3 million tons, and is currently exploited by the wholly-owned Sinai Manganese Company, which replaced the British Sinai Company more than 66 years ago. There are also deposits of manganese ore in the Sharm el-Sheikh area of South Sinai, associated with iron ore, and the percentage of manganese in this area is about 45%, and this area is considered to have an estimated reserve of about 30 thousand tons, according to information documented by the Mineral Resources Authority. It is used in many important industries such as: pharmaceutical industries – battery industry – aluminum – bronze.

Lead, Zinc, Silver and Gold

Lead, zinc, silver and gold spread in the Sinai Peninsula in the area of Um Zureik and Al-Kid near the city of Dahab, it has been discovered high concentrations of lead and zinc in the area of Um Zureik west of the Gulf of Aqaba and about 45 km from the city of Sharm el-Sheikh . These concentrations exist in the form of ranges in sedimentary rocks and concentrations range from 1% to more than 12% for lead and from 1% to 8% for zinc . This has been monitored these concentrations superficially and in depth where monitoring it at a depth of 79 m in the form of a carrier layer and the results showed the presence of galena metal by between 1-3% and the main zinc mineral, which is sphalerite by between 1-8% with the monitoring of other high concentrations of silver (3000 ppm). There are also some studies that refer to the discovery of gold ore in the vicinity of sedimentary rocks near the Abu Zenima area, as well as some areas in the city of Taba.

Copper

The Sinai Peninsula is famous for the presence of copper ore, which has been exploited since the era of the pharaohs, and the most important areas that contain ore are the Samra area near the city of Dahab, Al-Ruqaita near Saint Catherine, and the monument and Sarabid Al-Khadem near the city of Abu Zenima. Copper is used in many important economic industries as well as in many alloys and in the manufacture of paints.

Sulphur

Sulfur and pyrite ores are among the raw materials that are used in many industries, especially the fertilizer industry, and sulfur ore is found in two areas, the first in north El-Arish, which is of sedimentary origin, and the second region, which is the Mount Ferrani area in South Sinai, where sulphur is present in the form of pyrite ore in large quantities.

Feldspar

It is located in South Sinai and is used in many important industries such as: glass industry – ceramics – toothpaste – sandpaper materials – borsillin – paint and polishing materials.

Black sand

The beaches of the city of El-Arish abound and contain a lot of heavy and important metals such as magnetite, illuminate, rutile, zircon and are used in many important iron industries such as the manufacture of paints – alloys dyes – textiles – paper – leather – glass and refractory bricks.

Gypsum Deposits

Gypsum deposits are located in the Ras Al-Malab area and Wadi Al-Seih in South Sinai and are used in many industries, the most important of which are: the manufacture of fertilizers – cement and other construction purposes.

Shale Sediments

Shale deposits are widely spread in the area of Abu Zenima, the area of Wadi Firan, the area of Al-Tur and the area of Oyoun Musa. It is used in many industries, including: ceramic industry – as a filter material – brick industry – cement industry – drilling fluids – refractories industry – cosmetics and some pharmaceutical preparations.

Sand and Gravel Deposits

Sand and gravel deposits are spread in various places in the Sinai Peninsula and these deposits are included in many purposes such as: the manufacture of building and construction materials and as filter agents in water purification plants.

Salt

Sodium chloride (table salt) is found around Lake Bardawil in the form of salts and is used in the production of table salt, food industries, chemical industries and drilling oil wells. The most recent use of salt is the use of rock salt mines as a safe place for burying nuclear waste.

Bentonite

It has economic importance in the drilling of oil and groundwater wells, and is located between the areas of Oyoun Moussa and Ras Sidr. Reserves are estimated at hundreds of millions of tons.

Groundwater

North Sinai Governorate enjoys a huge reserve of groundwater in a group of deep aquifers, which opens up investment opportunities in the agricultural and industrial fields and the subsequent reconstruction and other economic activities.

By reviewing the wealth of the Sinai Peninsula, it becomes clear to us the importance of benefiting from these riches from the establishment of industrial projects that increase the value of these raw materials, which absorb a lot of labor and establish new urban communities aimed at reconstructing Sinai and increasing its effectiveness in supporting national income, especially with the presence of various energy sources, road network, ports and other elements of infrastructure.