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.

References

1. Cosío BG, Pérez De Llano L, Lopez Viña A, Torrego A, Lopez-Campos JL, et al. (2017) Th-2 signature in chronic airway diseases: towards the extinction of asthma-COPD overlap syndrome? The European Respiratory Journal. 49. [crossref]
2. Janin S，Rochat T (2007) Phenotypes of severe persistent asthma in adults. Revue Medicale Suisse. 3: 2663-2667.
3. Lea S, Higham A, Beech A, Singh D (2023) How inhaled corticosteroids target inflammation in COPD. European Respiratory Review. 32. [crossref]
4. Soter S., Barta I.Antus B (2013) Predicting sputum eosinophilia in exacerbations of COPD using exhaled nitric oxide. Inflammation. 36: 1178-1185. [crossref]
5. Thorhallsdottir A. K., Gislason D., Malinovschi A., Clausen M., Gislason T., et al. (2016) Exhaled nitric oxide in a middle-aged Icelandic population cohort. Journal of Breath Research. 10. [crossref]
6. Nerpin E, Ferreira DS, Weyler J, Schlunnsen V, Jogi R, et al. (2021) Bronchodilator response and lung function decline: Associations with exhaled nitric oxide with regard to sex and smoking status. The World Allergy Organization Journal. 14. [crossref]
7. Sepkovic DW, Haley NJ (1985) Biomedical applications of cotinine quantitation in smoking related research. American Journal of Public Health. 75: 663-665. [crossref]
8. Jarvis MJ, Russell MA, Benowitz NL, Feyerabend C (1988) Elimination of cotinine from body fluids: implications for noninvasive measurement of tobacco smoke exposure. American Journal of Public Health. 78: 696-698. [crossref]
9. Hecht SS (2004) Carcinogen derived biomarkers: applications in studies of human exposure to secondhand tobacco smoke. Tobacco Control. 13 Suppl 1(Suppl 1): i48-56. [crossref]
10. Benowitz NL (1996) Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiologic Reviews. 18: 188-204. [crossref]
11. Benowitz NL (1983) The use of biologic fluid samples in assessing tobacco smoke consumption. NIDA Research Monograph. 48: 6-26. [crossref]
12. Etzel RA (1990) A review of the use of saliva cotinine as a marker of tobacco smoke exposure. Preventive Medicine. 19: 190-197. [crossref]
13. Hayes Andrew F (2017) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach: Guilford publications.
14. Delen FM, Sippel JM, Osborne ML, Law S, Thukkani N, et al. (2000) Increased exhaled nitric oxide in chronic bronchitis: comparison with asthma and COPD. Chest. 117: 695-701. [crossref]
15. Kharitonov SA, Yates D, Robbins RA, Logan-Sinclair R, Shinebourne EA, et al. (1994) Increased nitric oxide in exhaled air of asthmatic patients. Lancet. 343: 133-135. [crossref]
16. Kharitonov SA, Wells AU, O’connor BJ, Cole PJ, Hansell DM, et al. (1995) Elevated levels of exhaled nitric oxide in bronchiectasis. American Journal of Respiratory and Critical Care Medicine. 151: 1889-1893. [crossref]
17. Mormile M, Mormile I, Fuschillo S, Rossi FW, Lamagna L, et al. (2023) Eosinophilic Airway Diseases: From Pathophysiological Mechanisms to Clinical Practice. International Journal of Molecular Sciences. 24. [crossref]
18. Persson MG, Wiklund NP, Gustafsson LE (1993) Endogenous nitric oxide in single exhalations and the change during exercise. The American Review of Respiratory Disease. 148: 1210-1214. [crossref]
19. Maroun M. J., Mehta S., Turcotte R., Cosio M. G.，Hussain S. N (1985) Effects of physical conditioning on endogenous nitric oxide output during exercise. J Appl Physiol. 79: 1219-1225. [crossref]
20. Massaro AF，Drazen JM (1985) Exhaled nitric oxide during exercise: site of release and modulation by ventilation and blood flow. J Appl Physiol. 80: 1863-1864. [crossref]
21. Olin AC, Aldenbratt A, Ekman A, Ljungkvist G, Jungersten L (2001) Increased nitric oxide in exhaled air after intake of a nitrate-rich meal. Respiratory Medicine. 95: 153-158. [crossref]
22. Duncan C., Dougall H., Johnston P., Green S., Brogan R., et al. (1995) Chemical generation of nitric oxide in the mouth from the enterosalivary circulation of dietary nitrate. Nature Medicine. 1: 546-551. [crossref]
23. Silkoff PE, Singh D, Fitzgerald JM, Eich A, Ludwig-Sengpiel A (2017) Inhaled Steroids and Active Smoking Drive Chronic Obstructive Pulmonary Disease Symptoms and Biomarkers to a Greater Degree Than Airflow Limitation. Biomarker Insights. 12. [crossref]
24. Nagasaki T, Matsumoto H, Nakaji H, Niimi A, Ito I, et al. (2013) Smoking attenuates the age-related decrease in IgE levels and maintains eosinophilic inflammation. Clinical and Experimental Allergy: Journal of the British Society for Allergy and Clinical Immunology. 43: 608-615. [crossref]
25. Merianos AL, Jandarov RA, Cataletto M, Mahabee-Gittens EM (2021) Tobacco smoke exposure and fractional exhaled nitric oxide levels among U.S. adolescents. Nitric Oxide. 117: 53-59. [crossref]
26. Oshikata C, Tsuburai T, Tsurikisawa N, Ono E, Higashi A, et al. (2008) [Cutoff point of the fraction of exhaled nitric oxide (FeNO) with the off-line method for diagnosing asthma and the effect of smoking on FeNO]. Nihon Kokyuki Gakkai Zasshi. 46: 356-362. [crossref]
27. Smit LA, Heederik D, Doekes G, Wouters IM (2009) Exhaled nitric oxide in endotoxin-exposed adults: effect modification by smoking and atopy. Occupational and Environmental Medicine. 66: 251-255. [crossref]
28. Bateman ED, Hurd SS, Barnes PJ, Bousquet J, Drazen JM, et al. (2008) Global strategy for asthma management and prevention: GINA executive summary. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology. 31: 143-178. [crossref]
29. Bafadhel M., Mckenna S, Terry S, Mistry V, Pancholi M, et al. (2012) Blood eosinophils to direct corticosteroid treatment of exacerbations of chronic obstructive pulmonary disease: a randomized placebo-controlled trial. American Journal of Respiratory and Critical Care Medicine. 186: 48-55. [crossref]
30. Pavord I. D., Lettis S., Locantore N., Pascoe S., Jones P. W., et al. (2016) Blood eosinophils and inhaled corticosteroid/long-acting β-2 agonist efficacy in COPD. Thorax. 71: 118-125. [crossref]
31. Pascoe S, Locantore N, Dransfield MT, Barnes NC, Pavord ID (2015) Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials. Lancet Respir Med. 3: 435-442. [crossref]
32. Siddiqui S. H., Guasconi A., Vestbo J., Jones P., Agusti A., et al. (2015) Blood Eosinophils: A Biomarker of Response to Extrafine Beclomethasone/Formoterol in Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 192: 523-525. [crossref]
33. Hartl S., Breyer M. K., Burghuber O. C., Ofenheimer A., Schrott A., et al. (2020) Blood eosinophil count in the general population: typical values and potential confounders. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology. 55. [crossref]
34. Pavord ID, Chanez P, Criner GJ, Kerstjens HAM, Korn S, et al. (2017) Mepolizumab for Eosinophilic Chronic Obstructive Pulmonary Disease. The New England Journal of Medicine. 377: 1613-1629.
35. Çolak Y, Afzal S, Nordestgaard BG, Marott JL, Lange P (2018) Combined value of exhaled nitric oxide and blood eosinophils in chronic airway disease: the Copenhagen General Population Study. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology. 52. [crossref]
36. Pedersen KM, Çolak Y, Ellervik C, Hasselbalch HC, Bojesen SE, et al. (2019) Smoking and Increased White and Red Blood Cells. Arteriosclerosis, Thrombosis, and Vascular Biology. 39: 965-977. [crossref]
37. Couillard S., Laugerud A., Jabeen M., Ramakrishnan S., Melhorn J., et al. (2022) Derivation of a prototype asthma attack risk scale centred on blood eosinophils and exhaled nitric oxide. Thorax. 77: 199-202. [crossref]
38. Couillard S, Do WIH, Beasley R, Hinks TSC, Pavord ID (2022) Predicting the benefits of type-2 targeted anti-inflammatory treatment with the prototype Oxford Asthma Attack Risk Scale (ORACLE). ERJ Open Res. 8. [crossref]
39. Shrimanker R, Keene O, Hynes G, Wenzel S, Yancey S (2019) Prognostic and Predictive Value of Blood Eosinophil Count, Fractional Exhaled Nitric Oxide, and Their Combination in Severe Asthma: A Post Hoc Analysis. American Journal of Respiratory and Critical Care Medicine. 200: 1308-1312. [crossref]
40. Ortega HG, Yancey SW, Mayer B, Gunsoy NB, Keene ON, et al. (2016) Severe eosinophilic asthma treated with mepolizumab stratified by baseline eosinophil thresholds: a secondary analysis of the DREAM and MENSA studies. Lancet Respir Med. 4: 549-556. [crossref]
41. Couillard S., Shrimanker R., Chaudhuri R., Mansur A. H., Mcgarvey L. P., et al. (2021) Fractional Exhaled Nitric Oxide Nonsuppression Identifies Corticosteroid-Resistant Type 2 Signaling in Severe Asthma. American Journal of Respiratory and Critical Care Medicine. 204: 731-734. [crossref]
42. Fan Z, Pun VC, Chen XC, Hong Q, Tian L, et al. (2018) Personal exposure to fine particles (PM(2.5)) and respiratory inflammation of common residents in Hong Kong. Environmental Research. 164: 24-31. [crossref]
43. Maestrelli P., Canova C., Scapellato M. L., Visentin A., Tessari R., et al. (2011) Personal exposure to particulate matter is associated with worse health perception in adult asthma. Journal of Investigational Allergology & Clinical Immunology: Official Organ of the International Association of Asthmology (INTERASMA) and Sociedad Latinoamericana de Alergia e Inmunologia. 21: 120-128. [crossref]
44. Xu T, Hou J, Cheng J, Zhang R, Yin W, et al. (2018) Estimated individual inhaled dose of fine particles and indicators of lung function: A pilot study among Chinese young adults. Environ Pollut. 235: 505-513. [crossref]
45. Zhang Z, Zhang H, Yang L, Chen X, Norbäck D, et al. (2022) Associations between outdoor air pollution, ambient temperature and fraction of exhaled nitric oxide (FeNO) in university students in northern China – A panel study. Environmental Research. 212(Pt C). [crossref]
46. Huang Q, Li Y, Li C, Zhang X, Du X, et al. (2023) Cigarette smoke aggravates asthma via altering airways inflammation phenotypes and remodelling. The Clinical Respiratory Journal. 17: 1316-1327. [crossref]
47. Saranz RJ, Lozano NA, Lozano A, Alegre G, Robredo P, et al. (2022) Relationship between exhaled nitric oxide and biomarkers of atopy in children and adolescents with allergic rhinitis. Acta Otorrinolaringol Esp (Engl Ed). 73: 286-291. [crossref]
48. Guida G, Rolla G, Badiu I, Marsico P, Pizzimenti S, et al. (2010) Determinants of exhaled nitric oxide in chronic rhinosinusitis. Chest. 137: 658-664. [crossref]
49. Kaur P, Chevalier R, Friesen C, Ryan J, Sherman A, et al. (2023) Diagnostic role of fractional exhaled nitric oxide in pediatric eosinophilic esophagitis, relationship with gastric and duodenal eosinophils. World Journal of Gastrointestinal Endoscopy. 15: 407-419. [crossref]
50. Galiniak S, Rachel M (2022) Fractional Exhaled Nitric Oxide in Teenagers and Adults with Atopic Dermatitis. Adv Respir Med 90(4): 237-245. [crossref]

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.

Mineral	First-order Raman line (cm-1)	FWHM (cm-1)	n (number of crystals)
13C-rich Diamond	1313.9 ± 6.1	59.4 ± 19.1	18
12C-rich Diamond1)	1332.7 ± 0.4	4.26 ± 0.42	20
13C-rich Graphite	1521.5 ± 8.5	70.0 ± 26.0	10
13C-rich Gr needle	1518.8 ± 1.1	39.3 ± 14.7	6
12C-rich Graphite2)	1581.5	3.5	–
13C-rich Graphite2)	1519.0	–	–

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).

	13C-rich diamond	12C-rich diamond	13C-rich graphite	12C-rich graphite
Pure 13C phase	1283.1 cm-1	–	1519 cm-1	–
Pure 12C phase		1332.7 cm-1		1581 cm-1
Sn-70	1286.7 ± 6.5 cm-1	1318.8 ± 0.9 cm-1	1518.1 ± 0.8 cm-1	–

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

1. Breiter K (2014) 800 years of mining activity and 450 years of geological research in the Krušné Hory/Erzgebirge Mountains, Central Bol Mus Para Emilio Goeldi. Ciências Naturais 9: 105-134.
2. Thomas R (2024a) The CaCl₂-to-rutile phase transition in SnO₂ from high to low pressure in nature. Geol Earth Mar Sci 6: 1-4.
3. Thomas R (2024b) Rhomboedric cassiterite as inclusions in tetragonal cassiterite from Slavkovský les – North Bohemia (Czech Republic). Geol Earth Mar Sci 6: 1-6.
4. Schütze H, Stiehl G, Wetzel K, Beuge P, Haberland R, et al. (1983) Isotopen- und elementgeochemische sowie radiogeochronologische Aussagen zur Herkunft des Ehrenfriedersdorfer Granits. – Ableitung erster Modellvorstellungen. ZFI- 76: 232-254.
5. Ni H, Xiao Y, Xiong X, Liu X, Gao C, et al. (2024) Formation and evolution of supercritical Science China Earth Sciences. 67: 1-13.
6. Thomas R (1982) Ergebnisse der thermobarometrischen Untersuchungen an Flussigkeitseinschlussen in Mineralen der postmagmatischen Zinn-Wolfram- Mineralisation des Erzgebirges. Freiberger Forschungshefte C370, Pg: 85.
7. Thomas R, Davidson P, Rericha A, Recknagel U (2022) Water-rich coesite in prismatine-granulite fromWaldheim/Saxony. Veröffentlichungen Naturkunde Chemnitz. 45: 67-s80.
8. Thomas R, Davidson P, Rericha A, Recknagel U [2023] Mineral inclusions in a crustal granite: Evidence for a novel transcrustal transport mechanism. Geosciences. 13.
9. Blank VD, Kulnitsky BA, Rerezhogin IA, Tyukalova EV, Denisov VN, et (2016) Graphite-to-diamond (¹³C) direct transition in a diamond anvil high-pressure cell. Int. J. Nanotechnol. 13: 604-611.
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.
11. Thomas R, Rericha A, Davidson P, Beurlen H (2021) An unusual paragenesis of diamond, graphite, and calcite: A Raman spectroscopic Estudos Geológicos 31: 3-15.
12. Schiferl D, Malcolm N, Zaug JM, Sharma SK, Cooney TF, et (1997) The diamond ¹³C/¹²C isotope Raman pressure sensor system for high-temperature/pressure diamond-anvil cells with reactive samples. J. Appl Phys 82: 3256-3265.
13. Balakrishnan K, Veerapandy V, Fjellvag H, Vajeeston P (2022) First-principles exploration into the physical and chemical properties of certain newly identified SnO₂ ACS Publ. 7: 10382-10393.
14. Hellwig H, Goncharov AF, Gregoryanz E, Mao H, Hemley RJ (2003) Brillouin and Raman spectroscopy of the ferroelastic rutile-to CaCl2 transition in SnO2 at high Physical Review B 67: 174110-1174110-7.
15. Shieh SR, Kubo A, Duffy TS, Prakapenka VB, Shen G (2006) High-pressure phases in SnO₂ to 117 Phys. Rev. B 73: 014105-1–014105-7.
16. Betechtin AG (1964) Lehrbuch der speziellen VEB Deutscher Verlag für Grundstoffindustrie, Leipzig, PG: 679.
17. Thomas R, Rericha A (2025) Extreme element enrichment by the interaction of supercritical fluids from the mantle with crustal rocks. Minerals. 15: 1-10.
18. Enkovich PV, Brazhkin VV, Lyapin SG, Novikov AP, Kanada H, et (2016) Raman spectroscopy of isotopically pure (¹²C, ¹³C) and isotopically mixed (^12.5C) diamond single crystals at ultrahigh pressures. Journal of Experimental and Theoretical Physics. 123: 443-451.

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.