Association between ambient temperature and respiratory health

Introduction

Climate change, driven by greenhouse gas emissions and industrialisation, poses a major global challenge (1). Rising ambient temperatures and variability are significant risk factors for respiratory health (2,3). Epidemiological studies show both high and low temperatures are associated with increased respiratory mortality in older and vulnerable populations (4). Temperature fluctuations, especially with air pollution, are associated with higher rates of respiratory-related hospitalisation and mortality (5). As climate change continues to intensify extreme temperature events (6), understanding their impact on respiratory health is required.

Several studies have investigated the relationship between ambient temperature and lung function, but findings are inconsistent. Some report stronger associations with either heat or cold (7-10), and others report no significant effects (11,12). A United States (U.S.) study using the National Health and Nutrition Examination Survey (NHANES) data (n=28,124) revealed that higher annual temperatures were associated with decreased forced expiratory volume in 1 second (FEV₁) (7). Conversely, a United Kingdom (UK) study of individuals with chronic obstructive pulmonary disease (COPD) (n=76) reported that a 1 ℃-decrease in ambient and indoor temperatures was associated with 2.2 mL- and 3.3 mL-reduction in FEV₁, respectively (8). A Chinese study (n=19,128) identified a J-shaped association, suggesting that both high and low temperatures may impair lung function (10). However, the impact on respiratory symptoms remains controversial across exposure duration, population, and geography (8,11,13). Most studies have focused on short-term exposure (14,15), examined linearity, and used community-level temperature data, potentially introducing misclassification (8,9,11,16). To address these limitations, we assessed short-, mid-, and long-term effects of ambient temperature on lung function and respiratory symptoms using individualised exposure estimates from a large-scale Korean NHANES (KNHANES). We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1819/rc).

Methods

Study design and dataset

This study used the KNHANES data, a population-based survey assessing the health and nutritional status of the Korean population. Conducted by the Korea Disease Control and Prevention Agency since 1988 (17), the KNHANES surveys approximately 10,000 individuals annually using stratified, multistage probability sampling to ensure representativeness. Data collection includes structured health interviews, clinical examinations, and dietary assessments, encompassing socioeconomic status, health behaviours, healthcare utilisation, anthropometric and biochemical measures, and nutrition. Pulmonary function testing (PFT) for adults aged ≥18 years has been included in KNHANES since 2007, with the minimum age revised to 40 years in 2010. Spirometry was performed using portable devices, with equipment updated from a dry-seal spirometer (Vmax series 2130; SensorMedics Corp., Yorba Linda, CA, USA) to the Vyntus Spiro (Vyaire Medical Inc., Hoechberg, Germany) on 28 June 2016. To minimise device-related discrepancies, we analysed data from 2016 to 2018.

Study population

We included individuals aged ≥40 years who participated in the KNHANES between 2016 and 2018. Of the 24,269 participants initially screened, 13,450 without PFT were excluded. Consequently, 10,819 participants with complete lung function and ambient temperature data were analysed (Figure 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flowchart describing selection of the study population. KNHANES, Korean National Health and Nutrition Examination Survey; PFT, pulmonary function test.

Lung function assessment and clinical data collection

Lung function was assessed once at the time of KNHANES participation, as the survey does not collect serial PFT measurements for longitudinal follow-up. Pulmonary function was evaluated using a portable spirometer (Vyntus Spiro, Vyaire Medical Inc.) following standardised protocols from the American Thoracic Society and European Respiratory Society (18). Trained technicians conducted and supervised the spirometry for accuracy and reproducibility. Each participant performed 3–8 spirometry manoeuvres. The KNHANES portable spirometry was previously validated (19). Predicted values for forced vital capacity (FVC) and FEV₁ were calculated using Korean-specific reference equations (20).

Participants were classified into obstructive (FEV₁/FVC <0.7) and restrictive (FVC <80% of predicted with FEV₁/FVC ≥0.7) based on spirometry (21). Chronic respiratory symptoms, including persistent cough and sputum production lasting over 3 months, were assessed via self-report.

Environmental data collection

Meteorological data were integrated with the KNHANES clinical records (22). Daily temperature and meteorological variables (e.g., wind speed, relative humidity, precipitation, and wind direction) were obtained from the Korea Meteorological Administration. These regional datasets were refined using atmospheric modelling techniques to improve spatial resolution, ensuring a 9-km grid alignment with administrative regions (city, county, and district levels).

Individual temperature exposure was estimated using the Community Multiscale Air Quality model, integrating meteorological outputs from the Weather Research and Forecasting model version 3.6.1 (23) and global climate datasets from the National Centers for Environmental Prediction, and Global Forecast System final analysis (24). Participant residential addresses were geocoded, and spatial interpolation (e.g., Inverse Distance Weighting and Kriging) generated individualised temperature exposure estimates. Temperature exposure was analysed across multiple timeframes. Short-term exposure was defined as the average daily temperature from the survey date (day 0) through 14 days prior (lag 0–14). Mid- and long-term exposures were calculated using moving averages (MAs) over 30–180 days and 1–5 years before participation.

Statistical analysis

Continuous and categorical variables were presented as mean ± standard deviation or median (interquartile range) and number (%), respectively. Assessing non-linear relationships between temperature and lung function, we used generalised additive models (GAMs) and distributed lag non-linear models (DLNMs) (25). The GAMs evaluated short- (day 0), mid-, and long-term effects by modelling the temperature with natural cubic splines (26). To minimise the influence of extreme values, analysis was restricted to temperatures between the 1^st and 99^th percentile. Models were adjusted for individual [sex, age, body mass index (BMI), income, smoking, asthma history, and daily walking activity] and environmental (meteorology and air pollution) factors. To address multicollinearity, only particulate matter with a diameter of ≤2.5 µm (PM_2.5), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) were included in the adjusted models. Particulate matter with a diameter of ≤10 µm (PM₁₀), carbon monoxide, and ozone were excluded due to high correlation with PM_2.5 and NO₂. The minimum risk temperature (MRT) was defined as the temperature associated with the highest pulmonary function in GAM or DLNM curves, and the temperature effects were estimated relative to the MRT. For certain exposure windows, estimates were unavailable due to non-significant non-linearity or model instability [marked as ‘not calculated (NC)’ in the table].

The optimal DLNM specification was selected by comparing the temperature knot placements and lag structures using the Akaike information criterion (AIC) (Table S1). Fourteen-day lag models consistently outperformed the 7-day models. For FVC and FEV₁, the best fit was observed with three knots at the 10^th, 75^th, and 90^th percentiles; whereas FEV₁/FVC was better modelled with a single knot at the 50^th percentile. As the AIC differences were small (<6.5), the 14-day lag with three knots was used as the main model, and the 7-day lag model for the sensitivity analyses. All models incorporated cross-basis splines for temperature and lag (14-day: knots at 2, 5, and 8 days; 7-day: 1, 3, and 5 days). To reduce overfitting while preserving flexibility, we used limited knots (1–3 for temperature, 3 for lag) (27,28). Meteorological and pollution variables were modelled using smooth functions. Cumulative effects were estimated across the temperature range, focusing on cold (0 ℃), moderate (20 ℃), and hot (32.4 ℃) conditions. For binary outcomes (PFT abnormalities and chronic respiratory symptoms), unadjusted and adjusted logistic regression was performed, reporting odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was set at P<0.05, and analyses were conducted using R software (version 4.4.2, R Core Team, Vienna, Austria).

Subgroup analyses and sensitivity analyses

To evaluate potential effect modification and identify subpopulation vulnerability, we conducted subgroup analyses stratified by (I) sex (men vs. women); (II) smoking status (current smoker vs. non-smoker); and (III) region (Seoul-Incheon-Gyeonggi vs. other areas). Each subgroup was analysed using the same modelling framework as the primary analysis.

To assess the robustness of the temperature-lung function associations, two sensitivity analyses were performed. First, sequential covariate adjustment models were fitted to examine how different covariate blocks influenced effect estimates: Model 1 included age, sex, and smoking status; Model 2 additionally included BMI, region, income, asthma diagnosis, number of walking days per week, and survey year; and Model 3, the prespecified main model, further adjusted for meteorological (wind speed, relative humidity, rainfall, wind direction, surface pressure, global shortwave radiation) and air-pollution variables (PM_2.5, SO₂, and NO₂). Second, a fully adjusted model excluding relative humidity was constructed to examine whether humidity contributed to the observed associations.

Results

Baseline characteristics

A total of 10,819 participants from the 2016–2018 KNHANES dataset were included. The mean age was 58.4 years, 44.0% were men, and 40.6% were ever-smokers (Table 1). The mean BMI was 24.3 kg/m², and 2.7% had asthma. The income levels were evenly distributed. Education levels were generally balanced, with few middle school graduates (13.1%). Regarding physical activity, 20.0% did not walk regularly, whereas 26.6% walked daily. PFTs showed mean predicted FVC of 88.5%±12.9% and predicted FEV₁ of 88.5%±13.9%. Obstructive and restrictive patterns were observed in 13.9% and 18.4% of the participants, respectively. Chronic cough and sputum production were reported by 2.6% and 4.2% of the participants, respectively (median duration, 3 months). The participants were distributed across South Korea, with 40.4% residing in the capital area (Figure S1). The mean temperature was 13.1±10.2 ℃ (median, 4.5 ℃). Other environmental data are in Table S2.

Association between ambient temperature and lung function

On day 0, higher temperatures were significantly associated with reduced FEV₁ (−2.59%, 95% CI: −4.35% to −0.82%) at 29.7 ℃ (nadir) relative to the MRT of 22.0 ℃ (Figure 2A, Table S3). At MA 90 days, the FVC (−3.11%, 95% CI: −5.03% to −1.19%) was significantly lower at 26.4 ℃, corresponding to the 99^th percentile of temperature (Figure 2B).

Figure 2 Short- and mid-term association between ambient temperature and lung function. (A) On day 0 and (B) over a 90-day MA. The relationship between ambient temperature and lung function relative to the MRT was estimated using a generalized additive model. The black line represents the estimated effect, and the shaded area indicates the 95% CI. The horizontal dashed and vertical blue dashed lines denote the reference (zero effect) level and MRT, respectively. Statistically significant deviations from the MRT (P<0.05) are indicated by red dots. CI, confidence interval; FEV₁, forced expiratory volume in 1 second; FVC, forced vital capacity; MA, moving average; MRT, minimum risk temperature.

At 5-year MA, ambient temperature showed a significant negative association with FEV₁ [nadir (13.7 ℃): −3.87%, 95% CI: −6.81% to −0.93%; MRT: 10.0 ℃] (Figure S2, Table S3). No significant associations were found for 1- and 3-year MAs; some estimates were unavailable owing to model limitations.

Short-term cumulative effects of ambient temperature

In the 14-day cumulative exposure (day 0–14), higher ambient temperatures were significantly associated with impaired lung function (Figure 3, Table S4). The nadir effect at 29.7 ℃ relative to the MRT of 23.9 ℃ was −4.98% (95% CI: −6.99% to −2.96%) for FVC and −5.95% (95% CI: −8.23% to −3.67%) for FEV₁. The FEV₁/FVC ratio showed a smaller but significant decrease (−0.40%; 95% CI: −0.74% to −0.06%) at 1.4 ℃ relative to the MRT of 20.0 ℃. A second nadir effect was observed at 20.0 ℃ relative to the MRT of 23.9 ℃ for FVC (−0.67%; 95% CI: −1.32% to −0.03%), at 19.2 ℃ relative to the MRT of 23.9 ℃ for FEV₁ (−0.75%; 95% CI: −1.45% to −0.05%), and at 12.6 ℃ relative to the MRT of 20.0 ℃ for FEV₁/FVC (−0.14%; 95% CI: −0.27% to −0.01%).

Figure 3 Cumulative effect of ambient temperature on lung function over 0–14 days DLNMs. (A) FVC (% predicted); (B) FEV₁ (% predicted); (C) FEV₁/FVC ratio. The black line represents the estimated effect of temperature on lung function, and the shaded area indicates the 95% CI. The blue dashed vertical line denotes the MRT. Red dots highlight statistically significant effects (P<0.05), indicating temperatures at which lung function deviates significantly from that at the MRT. CI, confidence interval; DLNM, distributed lag non-linear model; FEV₁, forced expiratory volume in 1 second; FVC, forced vital capacity; MRT, minimum risk temperature.

A consistent but weaker association was observed in the 7-day cumulative exposure analysis (days 0–7), supporting the robustness of our findings (Figure S3, Table S4).

Association between ambient temperature exposure and respiratory outcomes

Obstructive lung function defects were not significantly associated with ambient temperature in any period in unadjusted or adjusted models (Figure S4A). Restrictive pattern defects showed no significant association with the 90-day MA in the unadjusted model, but a significant positive association after adjustment (OR: 1.026; 95% CI: 1.009–1.044) (Figure S4B).

For chronic respiratory symptoms, the unadjusted model showed significant associations with chronic cough at the 90- and 180-day MAs; however, not statistically significant after adjustment. In the adjusted model, a marginal positive association was observed at the 180-day MA (OR: 1.048; 95% CI: 0.996–1.103; P=0.07) and 1-year MA (OR: 1.155; 95% CI: 0.975–1.373; P=0.10) (Figure S5A). In unadjusted analysis, sputum production was negatively associated with temperature at the 180-day MA; this weakened after adjustment. However, significant positive associations persisted at the 1-year MA (OR: 1.349; 95% CI: 1.173–1.554), 3-year MA (OR: 1.299; 95% CI: 1.093–1.552), and 5-year MA (OR: 1.189; 95% CI: 1.000–1.423) (Figure S5B).

Subgroup analyses

Subgroup analyses showed that the direction of temperature-lung function associations was generally consistent with the main analysis.

Subgroup analyses by sex showed broadly comparable exposure-response patterns between men and women (Tables S5,S6). Women demonstrated significant reductions in FVC in selected mid-term exposure windows, while men exhibited fewer statistically significant associations; however, there was no clear indication of strong sex-specific effect modification.

In subgroup analyses stratified by smoking status, the overall direction of temperature-lung function associations was similar between smokers and non-smokers (Tables S7,S8). Smokers exhibited more pronounced and statistically significant decreases in FVC and FEV₁ for several short- and mid-term exposure windows, whereas associations among non-smokers were generally weaker.

Finally, in subgroup analyses stratified by region, air-pollution levels were substantially higher in the metropolitan (Seoul-Incheon-Gyeonggi) region than in other regions across all measured air-pollution levels (Table S9); however, the temperature–lung function relationships were directionally similar to those observed in the main model (Tables S10,S11). Although many DLNM estimates were non-calculated due to reduced statistical power after stratification, we did not observe any notable regional differences in the association patterns.

Sensitivity analyses

Sensitivity analyses were performed to assess the robustness of the observed temperature-lung function associations. Several covariates—including age, sex, smoking, BMI, asthma, and region—showed significant univariate associations with lung function (Table S12), supporting their inclusion in sequential adjustment models. Based on this rationale, sequential covariate adjustment models were fitted to examine whether effect estimates were sensitive to the addition of different covariate blocks. Although incremental adjustment attenuated the magnitude of associations, the overall shape and direction of the temperature-lung function relationships remained consistent across Models 1–3 (Tables S13,S14).

Additionally, to explore whether humidity contributed to the observed associations, we fitted a fully adjusted model excluding relative humidity. The resulting temperature-lung function patterns were broadly similar to those of the main model (Tables S15,S16), indicating that the main findings were not materially influenced by humidity.

Discussion

This study assessed short-, mid-, and long-term temperature effects on lung function and respiratory outcomes using a nationally representative dataset. Temperature effects varied by duration, with short-term exposure affecting lung function at higher temperatures. On the other hand, the long-term exposure showed a non-linear pattern, with FEV₁ declining at higher temperatures. Additionally, higher mid-term temperatures were associated with increased risk of restrictive lung function abnormalities, whereas higher long-term temperatures were associated with increased risk of chronic respiratory symptoms, particularly sputum production.

Short-term heat exposure was associated with an immediate lung function decline, particularly FVC and FEV₁, with effects lasting over 14 days. These findings align with previous studies (9,16,29-31). Zhang et al. reported that, in 37 healthy college students, a 1 ℃-increase in outdoor temperature (lag 0) led to reduced morning and evening peak expiratory flows (PEFs) by 0.84 L/min (95% CI: −1.63 to −0.04) and 1.22 L/min (95% CI: −2.22 to −0.22), respectively, with stronger cumulative effects (29). Similarly, among 5,896 Framingham Heart Study participants in the U.S., a 5 ℃-increase in the temperature of previous week was linked to decreases of 20 mL (95% CI: −34 to −6) and 16 mL (95% CI: −32 to −1) in the FEV₁ and FVC, respectively (16). A study of 270 children with asthma from Australia revealed that higher temperatures were associated with lower morning and evening PEF and FEV₁ (lag 0–3 days) (30). In contrast, other studies found that short-term cold exposure is associated with lung function decline (8,32). In patients with COPD (n=76) in East London, lower temperatures were linked to increased exacerbation frequency and lung function decline, with the median FEV₁ and FVC decreasing by 45 mL (range, −113 to 229 mL) and 74 mL (range, −454 to 991 mL), respectively (8). Similarly, in 315 school children in Bangladesh, a 10 ℃-decrease in daily mean temperature (lag 0–1) resulted in declines of −3.02% (95% CI: −4.35% to −1.69%) and −1.48% (95% CI: −2.22% to −0.75%) in the PEF and FEV₁, respectively, with stronger effects in winter (32). However, some studies show that short-term exposure to both high and low temperatures contributes to lung function decline (33). A longitudinal study of 4,992 adult patients with asthma across 25 Chinese cities found that both extreme cold (−9.4 ℃) and heat (34.2 ℃) reduced lung function, with a greater impact at lower temperatures (FEV₁ decline: 60.4 vs. 26.0 mL, PEF decline: 299.7 vs. 35.8 mL/s, and FVC decline: 101.5 vs. 23.4 mL) (33). These variations may be attributed to differences in study populations (healthy individuals vs. those with airway disease), climates (tropical vs. temperate), and study methodologies, including temperature lags and exposure assessment. In our study, although GAMs detected limited associations, the DLNM better captured delayed and non-linear effects (25). This approach identified a non-linear pattern with nadirs at higher and lower-to-mid temperatures, indicating that moderate ambient temperatures may affect lung function through distinct physiological pathways.

Our findings showed consistent short-term reductions in lung function with higher temperatures and only a limited long-term association, observed at the 5-year exposure window. This result was not reproduced at the 1- and 3-year windows and did not coincide with changes in FEV₁/FVC, suggesting that the long-term FEV₁ association should be interpreted cautiously and may represent an exploratory finding. Few studies have investigated the association between long-term ambient temperature exposure and lung function, with heterogeneous findings (31,34). A French birth cohort study of 343 mother-child pairs revealed that long-term exposure to heat (24 vs. 12 ℃) and cold (1 vs. 12 ℃) from the second trimester to 4 weeks post-birth was associated with reduced functional residual capacity, increased respiratory rate, and lower tidal volume in female newborns (35). Another analysis of U.S. NHANES data showed a negative association between annual mean temperature and lung function, with a 10 ℉ increase linked to 0.71% and 0.59% declines in predicted FEV₁ across two time periods (1988–1994 and 2007–2012) (7). While direct comparisons are limited, our study adds evidence by identifying a non-linear association between long-term temperature exposure and FEV₁. Although high-resolution temporal data were unavailable for distributed lag modelling, GAMs revealed a curvilinear relationship, with the largest decline at the upper end of the temperature distribution.

Our study found short-term exposure reduced lung function primarily at high temperatures, whereas long-term exposure followed a non-linear pattern, with the greatest decline at higher temperatures. Heat exposure can induce thermoregulatory stress, dehydration, and airway inflammation, leading to bronchoconstriction and impaired gas exchange, acutely reducing lung function (36). Additionally, heat-sensitive receptors such as transient receptor potential vanilloid 1 may trigger vagal reflexes, exacerbating airway narrowing and dyspnoea (36). High temperatures may increase ozone and particulates, contributing to acute lung function decline (37). Long-term exposure to moderately elevated temperatures may impose cumulative physiological strain, leading to chronic airway inflammation, subtle structural changes, or impaired pulmonary vascular circulation, similar to chronic respiratory diseases (38,39). Although adaptations such as improved thermoregulation efficiency and circulatory compensation may occur (40), they may not fully offset the impact of prolonged temperature-related stress. In our study, the non-linear association between long-term temperature exposure and FEV₁, characterised by the greatest decline at the upper end of the moderate temperature range, may reflect subclinical and cumulative physiological responses. Although the temperature distribution in our dataset was relatively moderate, the temperature-lung function associations remained statistically significant when assessed relative to the MRT. This suggests that lung function may progressively decline even in non-extreme temperature ranges, with sustained exposure.

In the present study, long-term exposure to high temperatures was associated with chronic sputum production, suggesting a link between heat exposure and persistent airway inflammation. Prolonged heat exposure may induce chronic airway inflammation, leading to mucus hypersecretion, a hallmark of chronic respiratory diseases such as chronic bronchitis and COPD (36,41). Heat stress can impair mucociliary clearance, reducing the ability to clear excess mucus and causing its accumulation (42). Moreover, persistent airway irritation from high temperatures, combined with oxidative stress and epithelial damage (43), may promote mucus overproduction. Heat-induced dehydration and vascular changes reportedly exacerbate airway inflammation (36), increasing mucus retention and production.

This study has several limitations. First, our study focused on outdoor temperature exposure, not indoor temperatures, potentially leading to exposure misclassification. However, rather than relying solely on monitoring station data, we applied high-resolution atmospheric models to estimate individual-level exposure improving capture of temperature variation across different timeframes. Second, despite adjusting for major confounders, residual confounding from unmeasured factors, such as underlying lung disease severity, medication use, and occupational exposure, cannot be excluded. Nevertheless, we attempted to minimise this risk by incorporating a wide range of individual and environmental covariates and applying advanced statistical methods. Third, the cross-sectional nature of this study precludes causal inferences, and future longitudinal studies are required. Fourth, KNHANES includes only a single spirometry measurement per participant, preventing assessment of within-person changes over time. Thus, our exposure windows represent temperature conditions preceding the single PFT rather than longitudinal lung function trajectories. Although individual susceptibility or adaptation could not be evaluated, the large, nationally representative sample and consistent associations across exposure windows support the robustness of our findings. Fifth, for outcomes with weaker and less consistent temperature dependence, such as FEV₁/FVC, the estimated MRT was sometimes located at the lower end of the temperature distribution and should be interpreted with caution. In these settings, spline-based minima mainly reflect statistical uncertainty rather than well-defined biological thresholds. Finally, because the temperature range in South Korea is relatively moderate compared with regions experiencing sustained extreme heat, the observed effect sizes may represent conservative estimates. Nonetheless, consistent short-term associations across a temperate climate suggest that even modest temperature elevations can influence lung function. Despite limitations, this study has several strengths. It includes a large, well-characterised population with high-resolution models. Analysing short-, mid-, and long-term exposures together allowed a comprehensive assessment of temporal temperature effects. Furthermore, statistical models captured non-linear associations while adjusting for potential confounders, including meteorological factors and air pollutants.

Conclusions

In conclusion, our study revealed that short-term heat exposure caused immediate lung function decline, whereas long-term heat exposure was associated with a non-linear decline, most pronounced at higher ambient temperatures. These findings highlight the impact of temperature extremes on respiratory health and the need for adaptive strategies to protect vulnerable populations from heat-related lung function decline.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1819/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1819/prf

Funding: This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (No. RS-2025-25459103), and the Soonchunhyang University Research Fund.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1819/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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