Association between humidity and lung function: the 2016-2018 Korea National Health and Nutrition Examination Survey

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However, large-scale studies are required to clarify the specific effects on lung function and respiratory symptoms. This study examined the relationship between relative humidity (RH), lung function, and respiratory symptoms using data from the Korea National Health and Nutrition Examination Survey(KNHANES). Methods: This cross-sectional study analyzed data from KNHANES participants aged 40 and older, collected between 2016 and 2018. Pulmonary function tests (PFTs) and health questionnaires were used to assess lung function and respiratory symptoms. Individual environmental data, including RH, were obtained from the Community Multiscale Air Quality model and linked to the participants' addresses. Short-term (0–14 days), mid-term (30–180 days), and long-term (1–5 years) RH exposures were examined. Results: In total, 10,396 participants were included (mean age: 58.3 years, male: 43.6%). In multiple regression analysis, higher RH was negatively associated with the forced expiratory volume per 1 second/forced vital capacity (FVC) ratio across various time lags, while FVC was positively correlated with long-term RH exposure. In multiple logistic analysis adjusted for clinical and environmental covariates, long-term higher RH exposure was associated with a lower risk of restrictive lung disease (odds ratio [OR] at 4-year moving average [MA]: 0.978, 95% confidence interval [CI]: 0.959–0.997), while mid-term RH exposure decreased the risk of chronic cough (OR at 90-day MA: 0.968, 95% CI: 0.948–0.987) and sputum production (OR at 90-day MA: 0.984, 95% CI: 0.968–1.000). Conclusion: Higher RH negatively affected lung function and increased the risk of obstructive lung disease, whereas mid-term RH exposure reduced the risk of chronic cough and sputum production. Humidity Spirometry Lung disease Environmental exposure Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The relationship between environmental factors and respiratory health is a growing area of research, particularly in the context of climate change and urbanization [ 1 ]. Numerous studies have highlighted the adverse effects of air pollution on respiratory diseases and lung function, emphasizing the need for environmental control [ 2 – 5 ]. Among the various environmental factors, ambient humidity plays a significant role in human health. Research has shown that humidity affects human health, particularly during infectious diseases, and indoor relative humidity (RH) is often used to model respiratory virus transmission [ 6 ]. A Korean study also found that the risk of influenza incidence significantly increased at low (30–40%) or high (70%) RH combined with low daily temperatures of 0–5°C [ 7 ]. Other studies have linked relative humidity to the transmission and outcomes of coronavirus disease 2019 [ 8 , 9 ]. Several studies have investigated the effects of humidity on respiratory health [ 10 – 13 ]. Extreme humidity levels, particularly in hot and humid, or cold and dry conditions, are associated with worsened health, decreased physical activity, and increased exacerbations in patients with chronic obstructive pulmonary disease (COPD) [ 10 ]. Furthermore, humidity has been linked to increased respiratory mortality and outpatient visits [ 11 , 12 ]. However, one study found that, while indoor and outdoor temperatures were negatively correlated with self-reported COPD symptoms, indoor and outdoor humidity were not statistically significant in relation to these symptoms [ 13 ]. In addition, several studies have reported an association between humidity and lung function [ 14 – 17 ], but the results are conflicting and they did not examine humidity alone over various periods. Since humidity is closely linked to other meteorological factors [ 18 ], it is necessary to consider these factors together. Therefore, large-scale studies are needed to examine the effects of humidity on lung function and respiratory symptoms across different time lags, adjusting for clinical and environmental confounders. The Korea National Health and Nutrition Examination Survey (KNHANES) now includes meteorological and air pollution data. Recent studies using this data have examined the impact of air pollution on lung function [ 19 ]. Thus, we aimed to investigate the relationship between RH and both lung function and respiratory symptoms in a large Korean population using data from the KNHANES. Methods Study design and population This cross-sectional study used data from the KNHANES, conducted by the Korea Centers for Disease Control and Prevention since 1988 [ 20 , 21 ]. The KNHANES is a nationally representative survey that assesses the health and nutritional status of the non-institutionalized Korean population through a stratified, multistage probability sampling method involving approximately 10,000 individuals annually. It includes health interviews, examinations, and nutrition surveys, and collects data on socioeconomic status, health behaviors, quality of life, healthcare utilization, anthropometric measurements, biochemical profiles, and dietary intake. Pulmonary function tests (PFTs) were conducted as part of the survey using portable spirometry units. Since the fourth phase of the survey in 2007, PFTs have been administered to adults aged 18 years and older, with the age criterion adjusted to 40 years and older starting in 2010. Due to equipment updates, the spirometry devices were changed from dry-seal spirometer (Vmax series 2130; SensorMedics Corp., Yorba Linda, CA, USA) to Vyntus Spiro (Vyaire Medical Inc., Hoechberg, Germany) on June 28, 2016. The data used in this study were collected between 2016 and 2018. Participants A total of 24,269 participants aged 40 and older who were involved in the KNHANES between 2016 and 2018 were screened. The exclusion criteria included participants who did not undergo PFTs (n = 13,450) and those with missing data on major covariates (n = 423). Consequently, 10,396 participants with complete data on lung function and relevant covariates were included in the final analysis (Fig. 1 ). This study was approved by the Institutional Review Board of the Soonchunhyang University Seoul Hospital (SCHUH 2023-08-002). Clinical data collection To measure lung function, a portable spirometer was used on individuals aged 40 and above, following the American Thoracic Society/European Respiratory Society guidelines [ 22 ]. Four trained technicians ensured the quality control. Each participant performed three–eight acceptable maneuvers. The correlation between conventional and portable spirometry was high, with Pearson's coefficients of 0.986 for forced vital capacity (FVC) and 0.994 for forced expiratory volume in one second (FEV 1 ) [ 23 ]. The predicted values for FVC and FEV 1 were derived using the Korean reference standards [ 24 ]. Participants were categorized into obstructive (FEV 1 /FVC < 0.7) and restrictive (FVC < 80% predicted, FEV 1 /FVC ≥ 0.7) groups, with obstructive cases further classified as mild (FEV 1 ≥ 80% predicted) or moderate (FEV 1 three months, were assessed using the KNHANES health questionnaire. Environmental data collection To assess the impact of humidity on lung function, detailed environmental data were linked to KNHANES clinical data [ 25 ]. Meteorological data, including daily averages of temperature, wind speed, humidity, precipitation, wind direction, solar radiation, and surface pressure, were obtained from the Korea Meteorological Administration. These data were created using emission quantity and chemical transport models with a spatial resolution of 9 km grids, specific to city-county-district units. The Community Multiscale Air Quality (CMAQ) model estimates high-resolution relative humidity, air pollution, and other atmospheric conditions by integrating data from the Weather Research and Forecasting model version 3.6.1 [ 26 ], based on inputs from the National Centers for Environmental Prediction and the Global Forecast System final analysis data [ 27 ]. The participants’ geocoded addresses enabled the precise matching of daily humidity levels through spatial interpolation techniques such as Inverse Distance Weighting or Kriging. Short-term exposure was assessed using daily averages for the survey date and for each of the 14 days prior to the survey. Mid-term exposure was calculated by the moving average (MA) of daily relative humidity over cumulative periods of 30, 60, 90, 120, 150, and 180 days preceding the survey. Long-term exposure was determined by the MA of daily relative humidity over periods of 1 year (365 days), 2 years (730 days), 3 years (1,095 days), 4 years (1,460 days), and 5 years (1,826 days) preceding the survey. Statistical analysis Descriptive statistics summarized participants' baseline characteristics, with continuous variables as mean ± standard deviation and categorical variables as number (percentage). Associations between RH and lung function were evaluated using simple and multivariable linear regression models, with results expressed as beta coefficients (β) and standard errors. Logistic regression models categorized participants according to lung function (obstructive or restrictive) and respiratory symptoms (chronic cough or sputum production), and odds ratios (OR) and 95% confidence intervals (CI) were calculated. Model 1 was an unadjusted model; Model 2 adjusted for age, sex, income, education, residential area, smoking status, and body mass index (BMI); The multivariable Model 3 (main model) further adjusted for environmental covariates including mean temperature, precipitation, wind speed, and air pollution levels (particulate matter with a diameter ≤ 10 µm [PM 10 ], particulate matter with a diameter ≤ 2.5 µm [PM 2.5 ], sulfur dioxide [SO 2 ], nitrogen dioxide [NO 2 ], carbon monoxide [CO], and ozone [O 3 ]). Different lag periods for humidity exposure were analyzed for robustness. All analyses were performed using R software (version 4.0.3), with p -value of < 0.05 considered statistically significant. Results Baseline characteristics This study included 10,396 participants with a mean age of 58.3 years, 43.6% of whom were male. Household income was evenly distributed across the quartiles. The educational levels were as follows: primary school or lower (24.6%), middle school (13.5%), high school (31.8%), and college degree or higher (30.1%). The regional distribution covered major areas of South Korea, including Seoul (19.0%), Busan (7.1%), and Gyeonggi (22.1%). Ever-smokers accounted for 40.6% of the participants.The mean BMI was 24.2 kg/m². Environmental data on the index date showed a mean RH of 64.5%, mean temperature of 13.1°C, wind speed of 2.7 m/s, and precipitation rate of 0.6 mm/hr. Mean levels of air pollutants were: PM 10 at 48.6 µg/m³, PM 2.5 at 22.9 µg/m³, SO 2 at 4.1 ppb, NO 2 at 27.2 ppb, CO at 457.8 ppb, and O 3 at 29.3 ppb (Table 1 ). Table S1 shows consistent RH values over different lag days, which increased slightly to 67.8% by the fifth year, with the quartile values indicating consistent trends over time. Table 1 Baseline characteristics of total participants Characteristics Total N 10396 Age, years 58.3 ± 11.3 Male 4,536 (43.6) Household income 1st quartile 2445 (23.5) 2nd quartile 2607 (25.1) 3rd quartile 2666 (25.6) 4th quartile 2678 (25.8) Educational level Primary education or less 2562 (24.6) Middle school 1407 (13.5) High school 3303 (31.8) College degree or higher 3124 (30.1) Region Seoul 1974 (19.0) Busan 734 (7.1) Daegu 490 (4.7) Incheon 572 (5.5) Gwangju 327 (3.1) Daejeon 373 (3.6) Ulsan 232 (2.2) Sejong 218 (2.1) Gyeonggi 2300 (22.1) Gangwon 372 (3.6) Chungbuk 318 (3.1) Chungnam 348 (3.3) Jeonbuk 352 (3.4) Jeonnam 344 (3.3) Gyeongbuk 576 (5.5) Gyeongnam 649 (6.2) Jeju 217 (2.1) Ever-smoker 4225 (40.6) BMI, kg/m 2 24.2 ± 3.3 Environmental covariates Relative humidity, % 64.5 ± 14.9 Ambient temperature, °C 13.1 ± 10.2 Wind speed, m/s 2.7 ± 1.3 Precipitation, mm/hr 0.6 ± 2.4 PM 10 , µg/m³ 48.6 ± 20.5 PM 2.5 , µg/m³ 22.9 ± 10.7 SO 2 , ppb 4.1 ± 2.7 NO 2 , ppb 27.2 ± 15.7 CO, ppb 457.8 ± 163.8 O 3 , ppb 29.3 ± 14.6 Data are presented as mean ± standard deviation or number (%). BMI body mass index, PM 10 particulate matter with diameter ≤ 10 µm, PM 2.5 particulate matter with diameter ≤ 2.5 µm, SO 2 sulfur dioxide, NO 2 nitrogen dioxide, CO carbon monoxide, O 3 ozone Lung function and respiratory symptoms The mean predicted FEV 1 was 88.5%, with a mean measured FEV 1 of 2.6 liters, and the mean predicted FVC was 88.5%, with a mean measured FVC of 3.3 liters. The mean FEV 1 /FVC ratio was 70.1%. Obstructive lung disease (FEV 1 /FVC < 0.7) was identified in 1,415 participants (13.6%), with 605 (5.8%) classified as mild and 810 (7.8%) as moderate. Restrictive lung disease (FVC < 80% predicted, FEV 1 /FVC ≥ 0.7) was found in 1,918 participants (18.5%). Chronic cough was reported by 272 participants (2.6%) and sputum production by 438 participants (4.2%), both with an average duration of 7.6 years (Table 2 ). Table 2 Lung function and respiratory symptoms in total participants Variables N = 10396 FEV 1 , % predicted 88.5 ± 13.5 FEV 1 , L 2.6 ± 0.7 FVC, % predicted 88.5 ± 12.4 FVC, L 3.3 ± 0.8 FEV 1 /FVC, % 70.1 ± 7.5 Obstructive 1415 (13.6) Mild (FEV 1 ≥ 80% predicted) 605 (5.8) Moderate (FEV 1 < 80% predicted) 810 (7.8) Restrictive 1918 (18.5) Chronic cough 272 (2.6) Duration, years 7.6 ± 10.6 Sputum production 438 (4.2) Duration, years 7.6 ± 10.3 Data are presented as mean ± standard deviation or number (%). FEV 1 forced expiratory volume per 1 second, FVC forced vital capacity Association between humidity and lung function In simple regression, there was a consistent negative association between FEV 1 /FVC and RH across all time lags, with β values ranging from − 0.015 to -0.151, all statistically significant. For FEV 1 , the negative association with RH was significant for many time lags, particularly in the short term (lags 0–4, 7–9 days) with β values ranging from − 0.018 to -0.048, and in the mid-term (30 to 150-days MA) with β values ranging from − 0.034 to -0.048. For FVC, most time lags did not show a significant association with RH, with only a few time points, such as day 5, showing a significant positive β value of 0.018 (p = 0.033) (Additional file 1: Table S2). In multiple regression analysis (Table 3 ), FEV 1 /FVC showed a consistent negative association with RH across various time lags, with statistically significant β values in the short term (lags 0–1, 8–9 days), mid-term (30 to 180 days MA), and long term (1–5-years average), with β values decreasing as the lag length increased (Fig. 2 A). For FEV 1 , there was a tendency towards negative associations at lag 0 day (β = -0.021, p = 0.084) and lag 8 days (β = -0.018, p = 0.086); however, most associations were not statistically significant. FVC generally showed a positive correlation with RH, with statistically significant β values observed at lag 5 days (β = 0.026, p = 0.003), 3-year MA (β = 0.076, p = 0.016), and 4-year MA (β = 0.086, p = 0.007) (Fig. 2 B). Table 3 Multivariable regression analysis of the effect of relative humidity on lung function over various time lags FEV 1 /FVC FEV 1 FVC Lags Beta, % SE p -value Beta, % SE p -value Beta, % SE p -value 0 day -0.016 0.006 0.007 -0.021 0.012 0.084 -0.002 0.011 0.865 1 day -0.015 0.005 0.006 -0.014 0.011 0.193 0.005 0.010 0.583 2 days -0.008 0.005 0.124 -0.009 0.011 0.411 0.003 0.009 0.744 3 days -0.006 0.005 0.259 -0.014 0.010 0.169 -0.006 0.009 0.490 4 days -0.006 0.005 0.177 -0.012 0.010 0.222 -0.003 0.009 0.705 5 days -0.008 0.005 0.123 0.017 0.010 0.095 0.026 0.009 0.003 6 days -0.007 0.005 0.161 0.008 0.010 0.442 0.016 0.009 0.075 7 days -0.005 0.005 0.339 -0.018 0.010 0.078 -0.012 0.009 0.188 8 days -0.015 0.005 0.003 -0.018 0.011 0.086 -0.001 0.009 0.918 9 days -0.015 0.005 0.003 -0.013 0.011 0.206 0.005 0.009 0.620 10 days -0.006 0.005 0.241 -0.006 0.010 0.576 0.003 0.009 0.731 11 days -0.001 0.005 0.825 -0.005 0.010 0.606 -0.004 0.009 0.649 12 days 0.003 0.005 0.459 0.001 0.010 0.944 -0.003 0.008 0.725 13 days 0.002 0.005 0.741 -0.006 0.009 0.517 -0.007 0.008 0.380 14 days -0.002 0.005 0.702 -0.004 0.009 0.708 0.000 0.008 0.996 30-day average -0.024 0.009 0.012 -0.027 0.020 0.161 0.004 0.017 0.797 60-day average -0.024 0.010 0.014 -0.010 0.020 0.626 0.022 0.018 0.224 90-day average -0.029 0.010 0.004 -0.020 0.021 0.341 0.015 0.018 0.407 120-day average -0.031 0.011 0.003 -0.024 0.022 0.274 0.012 0.019 0.526 150-day average -0.032 0.011 0.005 -0.031 0.024 0.186 0.006 0.021 0.784 180-day average -0.029 0.013 0.023 -0.014 0.026 0.605 0.018 0.023 0.439 1-year average -0.039 0.017 0.021 0.024 0.035 0.494 0.059 0.031 0.059 2-year average -0.055 0.017 0.001 0.000 0.035 0.994 0.052 0.031 0.088 3-year average -0.063 0.017 < 0.001 0.013 0.036 0.717 0.076 0.031 0.016 4-year average -0.059 0.017 < 0.001 0.027 0.036 0.448 0.086 0.032 0.007 5-year average -0.060 0.018 0.001 -0.009 0.038 0.809 0.050 0.033 0.132 The beta coefficients (β) indicate the change in lung function parameters per unit increase in relative humidity. In multivariable regression, adjustments were made for age, sex, income, education level, residential area, smoking status, body mass index, mean temperature, precipitation, wind speed, and levels of particulate matter with a diameter ≤ 10 µm, particulate matter with a diameter ≤ 2.5 µm, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone. FEV 1 forced expiratory volume per 1 second, FVC forced vital capacity, SE standard error Association between humidity and lung function abnormalities In unadjusted model (Model 1), significant positive associations between RH and obstructive lung disease were found in the short-term (lags 1 day and 9 days) and long-term (1–5-years MA) (Additional file 1: Fig. S1 A). In the clinical covariate-adjusted model (Model 2), significant associations were identified at 1-and 2-years MA, with marginal associations observed at 3 to 5-years MA (Additional file 1: Fig. S1 B). In the main model (Model 3), which was adjusted for environmental factors, there was a tendency for the OR to increase above 1 as the exposure lag period increased, compared to short-term exposure. However, at 2-years MA, there was a marginal association, but this was not statistically significant (OR: 1.02, 95% CI: 0.997–1.043, p = 0.096) (Fig. 3 A). When mild obstruction was defined using an 80% threshold, significant positive associations were observed at lag 2 days (OR: 1.005, 95% CI: 1.001–1.015, p = 0.035) and lag 5 days (OR: 1.008, 95% CI: 1.001–1.015, p = 0.021), with other lags showing no statistical significance (Additional file 1: Fig. S2A). For moderate obstruction, no significant associations were observed across all time lags, with ORs ranging from 0.996 to 1.024, indicating no clear trend or significant effect of RH (Additional file 1: Fig. S2B). Restrictive lung disease showed little statistical significance until mid-term, but there was a tendency for the risk to increase with higher RH. However, with lags of more than one year, an increase in RH was associated with a decreased risk of restrictive lung disease in both Model 1 (significant at 1-, 3-, and 4-years MA) and Model 2 (significant at 1 to 5-years MA) (Additional file 1: Fig. S3). The main model showed mixed results in the short term, with significant negative associations at lag 5 days (OR: 0.996, 95% CI: 0.992–1.000, p = 0.033) and significant positive associations at lag 12 days (OR: 1.005, 95% CI: 1.001–1.009, p = 0.019) and lag 13 days (OR: 1.004, 95% CI: 1.000–1.008, p = 0.047) (Fig. 3 B). However, in the long term, significant negative associations were observed at 4-years MA (OR: 0.978, 95% CI: 0.959–0.997, p = 0.024) (Fig. 3 B). Association between humidity and respiratory symptoms Both Models 1 and 2 indicated that mid-term exposure (90–180-days MA) to higher RH was associated with a decreased risk of chronic cough, whereas short-term and long-term exposures did not show significant associations. However, the OR tended to increase above one with long-term exposure (Additional file: Fig. S4). In the main model, statistically significant negative associations between RH and chronic cough were observed for mid-term exposures, including MA 60-days (OR: 0.980, 95% CI: 0.961–0.999, p = 0.043), 90-days (OR: 0.968, 95% CI: 0.948–0.987, p = 0.001), 120-days (OR: 0.963, 95% CI: 0.942–0.984, p < 0.001), 150-days (OR: 0.962, 95% CI: 0.939–0.984, p = 0.001), and 180 days (OR: 0.957, 95% CI: 0.931–0.984, p = 0.002). However, for MA longer than one year, the statistical significance disappeared (Fig. 4 A). Similar to the findings for chronic cough, mid-term exposure (90–180-days MA) to higher RH was significantly linked to a decrease in sputum production, whereas no significant associations were found for short-term and long-term exposures in both Models 1 and 2 (Additional file 1: Fig. S5). The main model also showed consistent results, with statistically significant negative associations between RH and sputum production observed for mid-term exposures at: MA 90-days (OR: 0.984, 95% CI: 0.968–1.000, p = 0.047), 120-days (OR: 0.979, 95% CI: 0.963–0.996, p = 0.015), and 150-days (OR: 0.979, 95% CI: 0.961–0.997, p = 0.025) (Fig. 4 B). Discussion To the best of our knowledge, this is the first study to comprehensively analyze the association between RH and respiratory health, lung function, and symptoms. FEV 1 /FVC showed a negative association with RH across short-term, mid-term, and long-term exposures, whereas FVC exhibited a positive association, particularly in the long-term. The obstructive pattern had few significant associations but showed an increasing risk with longer-term higher RH exposure. In contrast, short-term exposure to higher RH increased the risk of restrictive pattern, whereas long-term exposure reduced the risk of restrictive lung disease. Mid-term exposure to higher RH was significantly associated with a decreased risk of chronic cough and sputum production, whereas short-term and long-term exposure showed no significant association. In our study, after adjusting for covariates, RH was negatively correlated with FEV 1 /FVC but positively correlated with FVC. Previous studies on RH and lung function have reported inconsistent results [ 14 – 17 ]. Lepeule et al. found that a 5% increase in the 7-day average RH was associated with a 0.2% decrease in both FVC and FEV 1 among elderly men in the USA (n = 1,103) [ 14 ]. Chen et al. observed a decline in peak expiratory flow (PEF) rates with a high 14-day lag RH in elderly Chinese; however, this association disappeared after adjusting for pollutant exposure [ 15 ]. A Swedish study of young adults (n = 1,853) found no significant association between building humidity indicators and FEV 1 or FVC [ 16 ]. However, the European Respiratory Health Survey found that women in homes (n = 6,443) with dampness (water damage or damp spots) experienced an additional FEV 1 decline of -2.25 ml/year, with increased lung function decline as dampness scores rose [ 17 ]. In addition, a Canadian cross-sectional survey (n = 36) showed that extremes of humidity, particularly in hot and humid (≥ 25°C, > 50% RH) conditions, were associated with worsened health status, decreased physical activity, and increased exacerbations in patients with COPD, compared with moderate conditions (14–21°C, 30–50% RH) [ 10 ]. Although our study did not find a direct relationship between RH and FEV 1 in multivariable analysis, FEV 1 /FVC, an obstruction indicator, showed a negative correlation with RH, consistent with previous findings [ 10 , 14 , 15 , 17 ]. This may be due to the complex interplay in which higher RH is associated with elevated air pollution levels, leading to impaired lung function. Although we adjusted for air pollutants, higher humidity can increase the PM concentration, thereby affecting lung function [ 28 , 29 ]. A meta-analysis of 25 studies on children's short-term exposure to PM 2.5 found that higher RH (≥ average) led to a greater decrease in PEF (-4.02 L/min [high] vs. -1.20 L/min [low]) compared to lower RH (< average) [ 30 ]. Additionally, higher humidity can increase the release of volatile organic compounds and black carbon [ 31 , 32 ], which were not controlled for in our study, potentially worsening respiratory irritation and lung function [ 33 , 34 ]. Higher humidity can also increase allergens, triggering bronchospasm, and exacerbating obstruction [ 35 ]. These findings collectively suggest that humidity may contribute to lung function obstruction. However, our study found that long-term exposure to high RH was associated with a lower risk of restrictive patterns and was positively correlated with FVC. This may be due to higher humidity maintaining mucosal hydration, which prevents mucosal dryness and subsequent inflammation [ 36 ]. Dry and damaged mucosal surfaces can trigger the release of inflammatory mediators that promote a type 2 inflammatory response characterized by the release of cytokines such as interleukin (IL)-4 and IL-13 [ 37 , 38 ]. There is evidence that IL-13 contributes to pulmonary fibrosis, including idiopathic pulmonary fibrosis, and that anti-IL-13 therapy can reduce fibrosis and enhance airway epithelium repair through both transforming growth factor-β-dependent and independent mechanisms [ 39 ]. Collectively, these findings suggest that high humidity is associated with a reduced risk of epithelial injury and restrictive patterns. Higher humidity may preserve lung function by maintaining mucociliary clearance and reducing chronic inflammation [ 40 , 41 ]. Rea et al. revealed that long-term (12-month) humidification therapy with fully humidified air at 37°C, delivered daily through nasal cannulae to COPD and bronchiectasis patients (n = 108), significantly reduced exacerbation days (18.2 days vs. 33.5 days; p = 0.045), increased time to first exacerbation (median 52 days vs. 27 days; p = 0.0495), and improved lung function and quality of life compared to usual care [ 42 ]. We also observed that chronic cough and sputum production decreased with higher mid-term RH. However, this trend did not persist for over a year in our study, likely because of seasonal variations. High summer humidity is counterbalanced by dry conditions in other seasons, making the benefits of higher RH on chronic respiratory symptoms transient. This study had several limitations. First, its cross-sectional design limits the ability to establish causality between RH and respiratory health outcomes, and longitudinal studies are needed to confirm these associations over time. Second, despite adjusting for various clinical and environmental covariates, residual confounding factors may have been present. However, we adjusted comprehensively for a range of individual environmental factors and air pollutants. Third, while the accuracy of individual RH values predicted by the CMAQ might have introduced some discrepancies compared to actual measurements, a previous study showed a high correlation (r = 0.78) between the measured and predicted values [ 43 ]. Finally, the study population was limited to Korea; therefore, the findings may not be generalizable to other regions with different climatic conditions and population characteristics. Despite these limitations, our comprehensive adjustments for environmental and clinical factors enhanced the robustness of our findings using large-scale data with individual levels of RH exposure. Conclusion In conclusion, this study found that RH affects lung function and respiratory symptoms in a complex manner, varying with exposure duration. Different RH levels have both positive and negative effects on respiratory health. These findings highlight the importance of considering RH in public health strategies. Further longitudinal studies are needed to confirm these associations and explore the long-term effects of RH on respiratory health. Abbreviations RH Relative humidity COPD Chronic obstructive pulmonary disease KNHANES Korea National Health and Nutrition Examination Survey PFT Pulmonary function test FVC Forced vital capacity FEV 1 Forced expiratory volume in one second CMAQ Community Multiscale Air Quality OR Odds ratio CI Confidence interval BMI Body mass index PM 10 particulate matter with a diameter ≤ 10 µm PM 2.5 particulate matter with a diameter ≤ 2.5 µm SO 2 sulfur dioxide NO 2 nitrogen dioxide CO carbon monoxide O 3 ozone PEF peak expiratory flow IL interleukin Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Soonchunhyang University Seoul Hospital (SCHUH 2023-08-002). The requirement for informed consent was waived because the data from the KNHANES were de-identified and publicly available. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Soonchunhyang University Research Fund. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1G1A1010730). Authors’ contributions H-Y.Y. takes full responsibility for the study conception and design and the content of this manuscript, including data and analysis. J.S. and B.L. performed statistical analyses and data interpretation. J.S. drafted the initial manuscript, and B.L. edited it. All authors reviewed and approved the final version of the manuscript. Acknowledgements Not applicable. References Joshi M, Goraya H, Joshi A, Bartter T. Climate change and respiratory diseases: a 2020 perspective. Curr Opin Pulm Med. 2020;26:119–27. Stafoggia M, Oftedal B, Chen J, Rodopoulou S, Renzi M, Atkinson RW, Bauwelinck M, Klompmaker JO, Mehta A, Vienneau D, et al. Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project. Lancet Planet Health. 2022;6:e9–18. Edginton S, O'Sullivan DE, King W, Lougheed MD. Effect of outdoor particulate air pollution on FEV(1) in healthy adults: a systematic review and meta-analysis. Occup Environ Med. 2019;76:583–91. Luo H, Zhang Q, Niu Y, Kan H, Chen R. Fine particulate matter and cardiorespiratory health in China: A systematic review and meta-analysis of epidemiological studies. J Environ Sci (China). 2023;123:306–16. Dominski FH, Lorenzetti Branco JH, Buonanno G, Stabile L, Gameiro da Silva M, Andrade A. Effects of air pollution on health: A mapping review of systematic reviews and meta-analyses. Environ Res. 2021;201:111487. Aganovic A, Bi Y, Cao G, Kurnitski J, Wargocki P. Modeling the impact of indoor relative humidity on the infection risk of five respiratory airborne viruses. Sci Rep. 2022;12:11481. Park JE, Son WS, Ryu Y, Choi SB, Kwon O, Ahn I. Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region. Influenza Other Respir Viruses. 2020;14:11–8. Verheyen CA, Bourouiba L. Associations between indoor relative humidity and global COVID-19 outcomes. J R Soc Interface. 2022;19:20210865. Mecenas P, Bastos R, Vallinoto ACR, Normando D. Effects of temperature and humidity on the spread of COVID-19: A systematic review. PLoS ONE. 2020;15:e0238339. Mekhuri S, Quach S, Barakat C, Sun W, Nonoyama ML. A cross-sectional survey on the effects of ambient temperature and humidity on health outcomes in individuals with chronic respiratory disease. Can J Respir Ther. 2023;59:256–69. Chen S, Liu C, Lin G, Hänninen O, Dong H, Xiong K. The role of absolute humidity in respiratory mortality in Guangzhou, a hot and wet city of South China. Environ Health Prev Med. 2021;26:109. Bao HR, Liu XJ, Tan EL, Shu J, Dong JY, Li S. [Effects of temperature and relative humidity on the number of outpatients with chronic obstructive pulmonary disease and their interaction effect in Lanzhou, China]. Beijing Da Xue Xue Bao Yi Xue Ban. 2020;52:308–16. Mu Z, Chen PL, Geng FH, Ren L, Gu WC, Ma JY, Peng L, Li QY. Synergistic effects of temperature and humidity on the symptoms of COPD patients. Int J Biometeorol. 2017;61:1919–25. Lepeule J, Litonjua AA, Gasparrini A, Koutrakis P, Sparrow D, Vokonas PS, Schwartz J. Lung function association with outdoor temperature and relative humidity and its interaction with air pollution in the elderly. Environ Res. 2018;165:110–7. Chen X, Zhu T, Wang Q, Wang T, Chen W, Yao Y, Xu Y, Qiu X. Higher temperature and humidity exacerbate pollutant-associated lung dysfunction in the elderly. Environ Res. 2024;245:118039. Gunnbjörnsdottir MI, Norbäck D, Plaschke P, Norrman E, Björnsson E, Janson C. The relationship between indicators of building dampness and respiratory health in young Swedish adults. Respir Med. 2003;97:302–7. Norbäck D, Zock JP, Plana E, Heinrich J, Svanes C, Sunyer J, Künzli N, Villani S, Olivieri M, Soon A, Jarvis D. Lung function decline in relation to mould and dampness in the home: the longitudinal European Community Respiratory Health Survey ECRHS II. Thorax. 2011;66:396–401. Davis RE, McGregor GR, Enfield KB. Humidity: A review and primer on atmospheric moisture and human health. Environ Res. 2016;144:106–16. Choi SB, Yun S, Kim SJ, Park YB, Oh K. Effects of exposure to ambient air pollution on pulmonary function impairment in Korea: the 2007–2017 Korea National Health and Nutritional Examination Survey. Epidemiol Health. 2021;43:e2021082. Oh K, Kim Y, Kweon S, Kim S, Yun S, Park S, Lee YK, Kim Y, Park O, Jeong EK. Korea National Health and Nutrition Examination Survey, 20th anniversary: accomplishments and future directions. Epidemiol Health. 2021;43:e2021025. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014;43:69–77. Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, Kaminsky DA, McCarthy K, McCormack MC, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200:e70–88. Park HJ, Rhee CK, Yoo KH, Park YB. Reliability of Portable Spirometry Performed in the Korea National Health and Nutrition Examination Survey Compared to Conventional Spirometry. Tuberc Respir Dis (Seoul). 2021;84:274–81. Choi JK, Paek D, Lee JO. Normal Predictive Values of Spirometry in Korean Population. Tuberc Respir Dis. 2005;58:230–42. Hwang MJ, Sung J, Yoon M, Kim JH, Yun HY, Choi DR, Koo YS, Oh K, Yun S, Cheong HK. Establishment of the Korea National Health and Nutrition Examination Survey air pollution study dataset for the researchers on the health impact of ambient air pollution. Epidemiol Health. 2021;43:e2021015. Koo Y-S, Choi D-R, Yun H-Y, Yoon G-W, Lee J-B. A development of PM concentration reanalysis method using CMAQ with surface data assimilation and MAIAC AOD in Korea. J Korean Soc Atmospheric Environ. 2020;36:558–73. Zhou X, Zhu Y, Hou D, Fu B, Li W, Guan H, Sinsky E, Kolczynski W, Xue X, Luo Y. The development of the NCEP global ensemble forecast system version 12. Weather Forecast. 2022;37:1069–84. Liu L, Ma X, Wen W, Sun C, Jiao J. Characteristics and potential sources of wintertime air pollution in Linfen, China. Environ Monit Assess. 2021;193:252. Birinci E, Deniz A, Özdemir ET. The relationship between PM(10) and meteorological variables in the mega city Istanbul. Environ Monit Assess. 2023;195:304. Zhang W, Ma R, Wang Y, Jiang N, Zhang Y, Li T. The relationship between particulate matter and lung function of children: A systematic review and meta-analysis. Environ Pollut. 2022;309:119735. Rajeevan K, Sumesh RK, Resmi EA, Unnikrishnan CK. An observational study on the variation of black carbon aerosol and source identification over a tropical station in south India. Atmospheric Pollution Res. 2019;10:30–44. Huang S, Xiong J, Zhang Y. The Impact of Relative Humidity on the Emission Behaviour of Formaldehyde in Building Materials. Procedia Eng. 2015;121:59–66. Yoon HI, Hong YC, Cho SH, Kim H, Kim YH, Sohn JR, Kwon M, Park SH, Cho MH, Cheong HK. Exposure to volatile organic compounds and loss of pulmonary function in the elderly. Eur Respir J. 2010;36:1270–6. Franco Suglia S, Gryparis A, Schwartz J, Wright RJ. Association between traffic-related black carbon exposure and lung function among urban women. Environ Health Perspect. 2008;116:1333–7. Ludwig S, Jimenez-Bush I, Brigham E, Bose S, Diette G, McCormack MC, Matsui EC, Davis MF. Analysis of home dust for Staphylococcus aureus and staphylococcal enterotoxin genes using quantitative PCR. Sci Total Environ. 2017;581–582:750–5. Guarnieri G, Olivieri B, Senna G, Vianello A. Relative Humidity and Its Impact on the Immune System and Infections. Int J Mol Sci 2023, 24. Wise SK, Laury AM, Katz EH, Den Beste KA, Parkos CA, Nusrat A. Interleukin-4 and interleukin-13 compromise the sinonasal epithelial barrier and perturb intercellular junction protein expression. Int Forum Allergy Rhinol. 2014;4:361–70. Schleimer RP. Immunopathogenesis of Chronic Rhinosinusitis and Nasal Polyposis. Annu Rev Pathol. 2017;12:331–57. Wijsenbeek MS, Kool M, Cottin V. Targeting interleukin-13 in idiopathic pulmonary fibrosis: from promising path to dead end. Eur Respir J 2018, 52. Chidekel A, Zhu Y, Wang J, Mosko JJ, Rodriguez E, Shaffer TH. The effects of gas humidification with high-flow nasal cannula on cultured human airway epithelial cells. Pulm Med 2012, 2012:380686. Hasani A, Chapman TH, McCool D, Smith RE, Dilworth JP, Agnew JE. Domiciliary humidification improves lung mucociliary clearance in patients with bronchiectasis. Chron Respir Dis. 2008;5:81–6. Rea H, McAuley S, Jayaram L, Garrett J, Hockey H, Storey L, O'Donnell G, Haru L, Payton M, O'Donnell K. The clinical utility of long-term humidification therapy in chronic airway disease. Respir Med. 2010;104:525–33. Li J, Yu S, Chen X, Zhang Y, Li M, Li Z, Song Z, Liu W, Li P, Xie M. Evaluation of the WRF-CMAQ model performances on air quality in China with the impacts of the observation nudging on meteorology. Aerosol Air Qual Res. 2022;22:220023. Additional Declarations No competing interests reported. Supplementary Files 2humidityKNHANESadditionalfile1080524hy081124js.docx Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2024 Read the published version in Respiratory Research → Version 1 posted Editorial decision: Revision requested 02 Sep, 2024 Reviews received at journal 02 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 14 Aug, 2024 Submission checks completed at journal 14 Aug, 2024 First submitted to journal 13 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4904104","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348355187,"identity":"49020c62-d578-4409-8786-ac6100a222ef","order_by":0,"name":"Jinwoo Seok","email":"","orcid":"","institution":"Soonchunhyang University Seoul Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinwoo","middleName":"","lastName":"Seok","suffix":""},{"id":348355190,"identity":"c5281844-f7a3-483d-8e7e-578a0cf6171e","order_by":1,"name":"Bo Lee","email":"","orcid":"","institution":"Soonchunhyang University Seoul Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Lee","suffix":""},{"id":348355193,"identity":"2b4d09a6-0b38-4ed5-b346-315b992fdaa2","order_by":2,"name":"Hee-Young Yoon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACA3YYi70HTPHwEdTCDKGAas9AtLCByANEaZHIgYgQ1GLOzGP44EfNHwb5mW8Pfi7MsZNhY+A9+PgDHi2WzTzGhj3HDBgMbuclS8/clgx0GF+yAV6HHeYxk2ZgM6jfIJ1jIM27jRmohcdMgoAW898M/wyADjtj/Jt3Wz1Ii/kPQrYwM7YBvX8DaB3vtsNgW/B637KZrViyt8+YweBMjpk177bjPGzMPMYSZ/BoMWdv3vjhxzc5Bvn2M8a3ebdV2/Oz9xh+qMCjBQtgJk35KBgFo2AUjAIsAAC1BD3eYKFqMwAAAABJRU5ErkJggg==","orcid":"","institution":"Soonchunhyang University Seoul Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hee-Young","middleName":"","lastName":"Yoon","suffix":""}],"badges":[],"createdAt":"2024-08-13 05:01:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4904104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4904104/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12931-024-03054-z","type":"published","date":"2024-12-02T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66426153,"identity":"f6bf7554-ec13-4d8f-baf7-9c1e17708c6a","added_by":"auto","created_at":"2024-10-11 17:25:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48247,"visible":true,"origin":"","legend":"\u003cp\u003eEnrollment of participants. KNHANES, Korea National Health and Nutrition Examination Survey; PFT, pulmonary function test.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/2ce1ada800c4c95c879a2332.jpg"},{"id":66426151,"identity":"9a687e43-ce8c-4b2d-b4a1-405a5e7d94d5","added_by":"auto","created_at":"2024-10-11 17:25:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":192082,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative figures of the effect of various lagged relative humidity levels on lung function in multivariable regression analysis. (\u003cstrong\u003eA\u003c/strong\u003e) FEV\u003csub\u003e1\u003c/sub\u003e/FVC and 0 day-lagged relative humidity. (\u003cstrong\u003eB\u003c/strong\u003e) FVC and 4 year-lagged relative humidity. This scatter plot depicts the relationship between relative humidity lagged by various time periods and lung function. The X-axis represents the relative humidity at different lag periods, while the Y-axis represents the lung function. The blue line represents the regression line, indicating the trend observed through multivariable regression analysis. The beta coefficient and p-value are displayed in the plot, highlighting the statistical significance of the observed effect. RH, relative humidity; FVC, forced vital capacity; FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in 1 second.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/a04a1c6a4c6e96988ff22a76.jpg"},{"id":66426154,"identity":"a57ad47d-80ac-41c7-8e08-813fb20426c9","added_by":"auto","created_at":"2024-10-11 17:25:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148140,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of various lagged relative humidity levels on lung function abnormalities. (\u003cstrong\u003eA\u003c/strong\u003e) Obstructive pattern. (\u003cstrong\u003eB\u003c/strong\u003e) Restrictive pattern. This forest plot represents the ORs and 95% CIs for the effect of relative humidity at various lag periods on lung function abnormalities, as determined by multivariable logistic regression analysis (Model 3). The X-axis displays the OR, and the variables on the Y-axis represent relative humidity lagged by different time periods, ranging from the same day (lag 0 day) to 5 years (5- year moving average). OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/3c44fbe5e838cca7e0cfbda6.jpg"},{"id":66426449,"identity":"30194847-70ed-4285-8001-962688b6be8a","added_by":"auto","created_at":"2024-10-11 17:33:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159632,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of various lagged relative humidity levels on respiratory symptoms. (\u003cstrong\u003eA\u003c/strong\u003e) Chronic cough. (\u003cstrong\u003eB\u003c/strong\u003e) Sputum production. This forest plot represents the ORs and 95% CIs for the effect of relative humidity at various lag periods on respiratory symptoms, as determined by multivariable logistic regression analysis (Model 3). The X-axis displays the OR, and the variables on the Y-axis represent relative humidity lagged by different time periods, ranging from the same day (lag 0 day) to 5 years (5-year moving average). OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/e4c4eb164f88ebacd1cc14de.jpg"},{"id":70964782,"identity":"90b92c50-dff8-4709-9330-87992d40e78d","added_by":"auto","created_at":"2024-12-09 16:15:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/04872d23-74a2-416b-86be-564d1e526772.pdf"},{"id":66426155,"identity":"096e423e-5ed6-49c7-b676-2ea938c9a1cf","added_by":"auto","created_at":"2024-10-11 17:25:39","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1758461,"visible":true,"origin":"","legend":"","description":"","filename":"2humidityKNHANESadditionalfile1080524hy081124js.docx","url":"https://assets-eu.researchsquare.com/files/rs-4904104/v1/334e09c177bb0ef38b34ebeb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between humidity and lung function: the 2016-2018 Korea National Health and Nutrition Examination Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eThe relationship between environmental factors and respiratory health is a growing area of research, particularly in the context of climate change and urbanization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Numerous studies have highlighted the adverse effects of air pollution on respiratory diseases and lung function, emphasizing the need for environmental control [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among the various environmental factors, ambient humidity plays a significant role in human health. Research has shown that humidity affects human health, particularly during infectious diseases, and indoor relative humidity (RH) is often used to model respiratory virus transmission [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A Korean study also found that the risk of influenza incidence significantly increased at low (30\u0026ndash;40%) or high (70%) RH combined with low daily temperatures of 0\u0026ndash;5\u0026deg;C [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other studies have linked relative humidity to the transmission and outcomes of coronavirus disease 2019 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have investigated the effects of humidity on respiratory health [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Extreme humidity levels, particularly in hot and humid, or cold and dry conditions, are associated with worsened health, decreased physical activity, and increased exacerbations in patients with chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, humidity has been linked to increased respiratory mortality and outpatient visits [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, one study found that, while indoor and outdoor temperatures were negatively correlated with self-reported COPD symptoms, indoor and outdoor humidity were not statistically significant in relation to these symptoms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, several studies have reported an association between humidity and lung function [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], but the results are conflicting and they did not examine humidity alone over various periods. Since humidity is closely linked to other meteorological factors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], it is necessary to consider these factors together. Therefore, large-scale studies are needed to examine the effects of humidity on lung function and respiratory symptoms across different time lags, adjusting for clinical and environmental confounders. The Korea National Health and Nutrition Examination Survey (KNHANES) now includes meteorological and air pollution data. Recent studies using this data have examined the impact of air pollution on lung function [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, we aimed to investigate the relationship between RH and both lung function and respiratory symptoms in a large Korean population using data from the KNHANES.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used data from the KNHANES, conducted by the Korea Centers for Disease Control and Prevention since 1988 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The KNHANES is a nationally representative survey that assesses the health and nutritional status of the non-institutionalized Korean population through a stratified, multistage probability sampling method involving approximately 10,000 individuals annually. It includes health interviews, examinations, and nutrition surveys, and collects data on socioeconomic status, health behaviors, quality of life, healthcare utilization, anthropometric measurements, biochemical profiles, and dietary intake.\u003c/p\u003e \u003cp\u003ePulmonary function tests (PFTs) were conducted as part of the survey using portable spirometry units. Since the fourth phase of the survey in 2007, PFTs have been administered to adults aged 18 years and older, with the age criterion adjusted to 40 years and older starting in 2010. Due to equipment updates, the spirometry devices were changed from dry-seal spirometer (Vmax series 2130; SensorMedics Corp., Yorba Linda, CA, USA) to Vyntus Spiro (Vyaire Medical Inc., Hoechberg, Germany) on June 28, 2016. The data used in this study were collected between 2016 and 2018.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 24,269 participants aged 40 and older who were involved in the KNHANES between 2016 and 2018 were screened. The exclusion criteria included participants who did not undergo PFTs (n\u0026thinsp;=\u0026thinsp;13,450) and those with missing data on major covariates (n\u0026thinsp;=\u0026thinsp;423). Consequently, 10,396 participants with complete data on lung function and relevant covariates were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Institutional Review Board of the Soonchunhyang University Seoul Hospital (SCHUH 2023-08-002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eClinical data collection\u003c/h2\u003e \u003cp\u003eTo measure lung function, a portable spirometer was used on individuals aged 40 and above, following the American Thoracic Society/European Respiratory Society guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Four trained technicians ensured the quality control. Each participant performed three\u0026ndash;eight acceptable maneuvers. The correlation between conventional and portable spirometry was high, with Pearson's coefficients of 0.986 for forced vital capacity (FVC) and 0.994 for forced expiratory volume in one second (FEV\u003csub\u003e1\u003c/sub\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The predicted values for FVC and FEV\u003csub\u003e1\u003c/sub\u003e were derived using the Korean reference standards [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Participants were categorized into obstructive (FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7) and restrictive (FVC\u0026thinsp;\u0026lt;\u0026thinsp;80% predicted, FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026ge;\u0026thinsp;0.7) groups, with obstructive cases further classified as mild (FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80% predicted) or moderate (FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;80% predicted). Medical history and chronic respiratory symptoms, such as sputum production and cough lasting\u0026thinsp;\u0026gt;\u0026thinsp;three months, were assessed using the KNHANES health questionnaire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental data collection\u003c/h2\u003e \u003cp\u003eTo assess the impact of humidity on lung function, detailed environmental data were linked to KNHANES clinical data [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Meteorological data, including daily averages of temperature, wind speed, humidity, precipitation, wind direction, solar radiation, and surface pressure, were obtained from the Korea Meteorological Administration. These data were created using emission quantity and chemical transport models with a spatial resolution of 9 km grids, specific to city-county-district units. The Community Multiscale Air Quality (CMAQ) model estimates high-resolution relative humidity, air pollution, and other atmospheric conditions by integrating data from the Weather Research and Forecasting model version 3.6.1 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], based on inputs from the National Centers for Environmental Prediction and the Global Forecast System final analysis data [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The participants\u0026rsquo; geocoded addresses enabled the precise matching of daily humidity levels through spatial interpolation techniques such as Inverse Distance Weighting or Kriging.\u003c/p\u003e \u003cp\u003eShort-term exposure was assessed using daily averages for the survey date and for each of the 14 days prior to the survey. Mid-term exposure was calculated by the moving average (MA) of daily relative humidity over cumulative periods of 30, 60, 90, 120, 150, and 180 days preceding the survey. Long-term exposure was determined by the MA of daily relative humidity over periods of 1 year (365 days), 2 years (730 days), 3 years (1,095 days), 4 years (1,460 days), and 5 years (1,826 days) preceding the survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarized participants' baseline characteristics, with continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables as number (percentage). Associations between RH and lung function were evaluated using simple and multivariable linear regression models, with results expressed as beta coefficients (β) and standard errors. Logistic regression models categorized participants according to lung function (obstructive or restrictive) and respiratory symptoms (chronic cough or sputum production), and odds ratios (OR) and 95% confidence intervals (CI) were calculated.\u003c/p\u003e \u003cp\u003eModel 1 was an unadjusted model; Model 2 adjusted for age, sex, income, education, residential area, smoking status, and body mass index (BMI); The multivariable Model 3 (main model) further adjusted for environmental covariates including mean temperature, precipitation, wind speed, and air pollution levels (particulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m [PM\u003csub\u003e10\u003c/sub\u003e], particulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m [PM\u003csub\u003e2.5\u003c/sub\u003e], sulfur dioxide [SO\u003csub\u003e2\u003c/sub\u003e], nitrogen dioxide [NO\u003csub\u003e2\u003c/sub\u003e], carbon monoxide [CO], and ozone [O\u003csub\u003e3\u003c/sub\u003e]). Different lag periods for humidity exposure were analyzed for robustness. All analyses were performed using R software (version 4.0.3), with \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThis study included 10,396 participants with a mean age of 58.3 years, 43.6% of whom were male. Household income was evenly distributed across the quartiles. The educational levels were as follows: primary school or lower (24.6%), middle school (13.5%), high school (31.8%), and college degree or higher (30.1%). The regional distribution covered major areas of South Korea, including Seoul (19.0%), Busan (7.1%), and Gyeonggi (22.1%). Ever-smokers accounted for 40.6% of the participants.The mean BMI was 24.2 kg/m\u0026sup2;.\u003c/p\u003e \u003cp\u003eEnvironmental data on the index date showed a mean RH of 64.5%, mean temperature of 13.1\u0026deg;C, wind speed of 2.7 m/s, and precipitation rate of 0.6 mm/hr. Mean levels of air pollutants were: PM\u003csub\u003e10\u003c/sub\u003e at 48.6 \u0026micro;g/m\u0026sup3;, PM\u003csub\u003e2.5\u003c/sub\u003e at 22.9 \u0026micro;g/m\u0026sup3;, SO\u003csub\u003e2\u003c/sub\u003e at 4.1 ppb, NO\u003csub\u003e2\u003c/sub\u003e at 27.2 ppb, CO at 457.8 ppb, and O\u003csub\u003e3\u003c/sub\u003e at 29.3 ppb (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows consistent RH values over different lag days, which increased slightly to 67.8% by the fifth year, with the quartile values indicating consistent trends over time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of total participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,536 (43.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2445 (23.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2607 (25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2666 (25.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2678 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2562 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1407 (13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3303 (31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3124 (30.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeoul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1974 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e734 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaegu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncheon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572 (5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGwangju\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaejeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlsan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSejong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGyeonggi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2300 (22.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGangwon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChungbuk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChungnam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJeonbuk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJeonnam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGyeongbuk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGyeongnam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e649 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJeju\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4225 (40.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbient temperature, \u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind speed, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation, mm/hr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e, \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e, \u0026micro;g/m\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457.8\u0026thinsp;\u0026plusmn;\u0026thinsp;163.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e particulate matter with diameter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e particulate matter with diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m, \u003cem\u003eSO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e sulfur dioxide, \u003cem\u003eNO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e nitrogen dioxide, \u003cem\u003eCO\u003c/em\u003e carbon monoxide, \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e ozone\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLung function and respiratory symptoms\u003c/h2\u003e \u003cp\u003eThe mean predicted FEV\u003csub\u003e1\u003c/sub\u003e was 88.5%, with a mean measured FEV\u003csub\u003e1\u003c/sub\u003e of 2.6 liters, and the mean predicted FVC was 88.5%, with a mean measured FVC of 3.3 liters. The mean FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio was 70.1%. Obstructive lung disease (FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7) was identified in 1,415 participants (13.6%), with 605 (5.8%) classified as mild and 810 (7.8%) as moderate. Restrictive lung disease (FVC\u0026thinsp;\u0026lt;\u0026thinsp;80% predicted, FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026ge;\u0026thinsp;0.7) was found in 1,918 participants (18.5%). Chronic cough was reported by 272 participants (2.6%) and sputum production by 438 participants (4.2%), both with an average duration of 7.6 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLung function and respiratory symptoms in total participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10396\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e, % predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e, L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC, % predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC, L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1415 (13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80% predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;80% predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e810 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestrictive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1918 (18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic cough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eFEV\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e forced expiratory volume per 1 second, \u003cem\u003eFVC\u003c/em\u003e forced vital capacity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between humidity and lung function\u003c/h2\u003e \u003cp\u003eIn simple regression, there was a consistent negative association between FEV\u003csub\u003e1\u003c/sub\u003e/FVC and RH across all time lags, with β values ranging from \u0026minus;\u0026thinsp;0.015 to -0.151, all statistically significant. For FEV\u003csub\u003e1\u003c/sub\u003e, the negative association with RH was significant for many time lags, particularly in the short term (lags 0\u0026ndash;4, 7\u0026ndash;9 days) with β values ranging from \u0026minus;\u0026thinsp;0.018 to -0.048, and in the mid-term (30 to 150-days MA) with β values ranging from \u0026minus;\u0026thinsp;0.034 to -0.048. For FVC, most time lags did not show a significant association with RH, with only a few time points, such as day 5, showing a significant positive β value of 0.018 (p\u0026thinsp;=\u0026thinsp;0.033) (Additional file 1: Table S2).\u003c/p\u003e \u003cp\u003eIn multiple regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), FEV\u003csub\u003e1\u003c/sub\u003e/FVC showed a consistent negative association with RH across various time lags, with statistically significant β values in the short term (lags 0\u0026ndash;1, 8\u0026ndash;9 days), mid-term (30 to 180 days MA), and long term (1\u0026ndash;5-years average), with β values decreasing as the lag length increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For FEV\u003csub\u003e1\u003c/sub\u003e, there was a tendency towards negative associations at lag 0 day (β = -0.021, p\u0026thinsp;=\u0026thinsp;0.084) and lag 8 days (β = -0.018, p\u0026thinsp;=\u0026thinsp;0.086); however, most associations were not statistically significant. FVC generally showed a positive correlation with RH, with statistically significant β values observed at lag 5 days (β\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.003), 3-year MA (β\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;=\u0026thinsp;0.016), and 4-year MA (β\u0026thinsp;=\u0026thinsp;0.086, p\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable regression analysis of the effect of relative humidity on lung function over various time lags\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLags\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180-day average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-year average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-year average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-year average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-year average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eThe beta coefficients (β) indicate the change in lung function parameters per unit increase in relative humidity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eIn multivariable regression, adjustments were made for age, sex, income, education level, residential area, smoking status, body mass index, mean temperature, precipitation, wind speed, and levels of particulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m, particulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eFEV\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e forced expiratory volume per 1 second, \u003cem\u003eFVC\u003c/em\u003e forced vital capacity, \u003cem\u003eSE\u003c/em\u003e standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between humidity and lung function abnormalities\u003c/h2\u003e \u003cp\u003eIn unadjusted model (Model 1), significant positive associations between RH and obstructive lung disease were found in the short-term (lags 1 day and 9 days) and long-term (1\u0026ndash;5-years MA) (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). In the clinical covariate-adjusted model (Model 2), significant associations were identified at 1-and 2-years MA, with marginal associations observed at 3 to 5-years MA (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). In the main model (Model 3), which was adjusted for environmental factors, there was a tendency for the OR to increase above 1 as the exposure lag period increased, compared to short-term exposure. However, at 2-years MA, there was a marginal association, but this was not statistically significant (OR: 1.02, 95% CI: 0.997\u0026ndash;1.043, p\u0026thinsp;=\u0026thinsp;0.096) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). When mild obstruction was defined using an 80% threshold, significant positive associations were observed at lag 2 days (OR: 1.005, 95% CI: 1.001\u0026ndash;1.015, p\u0026thinsp;=\u0026thinsp;0.035) and lag 5 days (OR: 1.008, 95% CI: 1.001\u0026ndash;1.015, p\u0026thinsp;=\u0026thinsp;0.021), with other lags showing no statistical significance (Additional file 1: Fig. S2A). For moderate obstruction, no significant associations were observed across all time lags, with ORs ranging from 0.996 to 1.024, indicating no clear trend or significant effect of RH (Additional file 1: Fig. S2B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRestrictive lung disease showed little statistical significance until mid-term, but there was a tendency for the risk to increase with higher RH. However, with lags of more than one year, an increase in RH was associated with a decreased risk of restrictive lung disease in both Model 1 (significant at 1-, 3-, and 4-years MA) and Model 2 (significant at 1 to 5-years MA) (Additional file 1: Fig. S3). The main model showed mixed results in the short term, with significant negative associations at lag 5 days (OR: 0.996, 95% CI: 0.992\u0026ndash;1.000, p\u0026thinsp;=\u0026thinsp;0.033) and significant positive associations at lag 12 days (OR: 1.005, 95% CI: 1.001\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.019) and lag 13 days (OR: 1.004, 95% CI: 1.000\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). However, in the long term, significant negative associations were observed at 4-years MA (OR: 0.978, 95% CI: 0.959\u0026ndash;0.997, p\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between humidity and respiratory symptoms\u003c/h2\u003e \u003cp\u003eBoth Models 1 and 2 indicated that mid-term exposure (90\u0026ndash;180-days MA) to higher RH was associated with a decreased risk of chronic cough, whereas short-term and long-term exposures did not show significant associations. However, the OR tended to increase above one with long-term exposure (Additional file: Fig. S4). In the main model, statistically significant negative associations between RH and chronic cough were observed for mid-term exposures, including MA 60-days (OR: 0.980, 95% CI: 0.961\u0026ndash;0.999, p\u0026thinsp;=\u0026thinsp;0.043), 90-days (OR: 0.968, 95% CI: 0.948\u0026ndash;0.987, p\u0026thinsp;=\u0026thinsp;0.001), 120-days (OR: 0.963, 95% CI: 0.942\u0026ndash;0.984, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 150-days (OR: 0.962, 95% CI: 0.939\u0026ndash;0.984, p\u0026thinsp;=\u0026thinsp;0.001), and 180 days (OR: 0.957, 95% CI: 0.931\u0026ndash;0.984, p\u0026thinsp;=\u0026thinsp;0.002). However, for MA longer than one year, the statistical significance disappeared (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similar to the findings for chronic cough, mid-term exposure (90\u0026ndash;180-days MA) to higher RH was significantly linked to a decrease in sputum production, whereas no significant associations were found for short-term and long-term exposures in both Models 1 and 2 (Additional file 1: Fig. S5). The main model also showed consistent results, with statistically significant negative associations between RH and sputum production observed for mid-term exposures at: MA 90-days (OR: 0.984, 95% CI: 0.968\u0026ndash;1.000, p\u0026thinsp;=\u0026thinsp;0.047), 120-days (OR: 0.979, 95% CI: 0.963\u0026ndash;0.996, p\u0026thinsp;=\u0026thinsp;0.015), and 150-days (OR: 0.979, 95% CI: 0.961\u0026ndash;0.997, p\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to comprehensively analyze the association between RH and respiratory health, lung function, and symptoms. FEV\u003csub\u003e1\u003c/sub\u003e/FVC showed a negative association with RH across short-term, mid-term, and long-term exposures, whereas FVC exhibited a positive association, particularly in the long-term. The obstructive pattern had few significant associations but showed an increasing risk with longer-term higher RH exposure. In contrast, short-term exposure to higher RH increased the risk of restrictive pattern, whereas long-term exposure reduced the risk of restrictive lung disease. Mid-term exposure to higher RH was significantly associated with a decreased risk of chronic cough and sputum production, whereas short-term and long-term exposure showed no significant association.\u003c/p\u003e \u003cp\u003eIn our study, after adjusting for covariates, RH was negatively correlated with FEV\u003csub\u003e1\u003c/sub\u003e/FVC but positively correlated with FVC. Previous studies on RH and lung function have reported inconsistent results [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Lepeule et al. found that a 5% increase in the 7-day average RH was associated with a 0.2% decrease in both FVC and FEV\u003csub\u003e1\u003c/sub\u003e among elderly men in the USA (n\u0026thinsp;=\u0026thinsp;1,103) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Chen et al. observed a decline in peak expiratory flow (PEF) rates with a high 14-day lag RH in elderly Chinese; however, this association disappeared after adjusting for pollutant exposure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A Swedish study of young adults (n\u0026thinsp;=\u0026thinsp;1,853) found no significant association between building humidity indicators and FEV\u003csub\u003e1\u003c/sub\u003e or FVC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the European Respiratory Health Survey found that women in homes (n\u0026thinsp;=\u0026thinsp;6,443) with dampness (water damage or damp spots) experienced an additional FEV\u003csub\u003e1\u003c/sub\u003e decline of -2.25 ml/year, with increased lung function decline as dampness scores rose [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, a Canadian cross-sectional survey (n\u0026thinsp;=\u0026thinsp;36) showed that extremes of humidity, particularly in hot and humid (\u0026ge;\u0026thinsp;25\u0026deg;C, \u0026gt;\u0026thinsp;50% RH) conditions, were associated with worsened health status, decreased physical activity, and increased exacerbations in patients with COPD, compared with moderate conditions (14\u0026ndash;21\u0026deg;C, 30\u0026ndash;50% RH) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although our study did not find a direct relationship between RH and FEV\u003csub\u003e1\u003c/sub\u003e in multivariable analysis, FEV\u003csub\u003e1\u003c/sub\u003e/FVC, an obstruction indicator, showed a negative correlation with RH, consistent with previous findings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This may be due to the complex interplay in which higher RH is associated with elevated air pollution levels, leading to impaired lung function. Although we adjusted for air pollutants, higher humidity can increase the PM concentration, thereby affecting lung function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A meta-analysis of 25 studies on children's short-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e found that higher RH (\u0026ge;\u0026thinsp;average) led to a greater decrease in PEF (-4.02 L/min [high] vs. -1.20 L/min [low]) compared to lower RH (\u0026lt;\u0026thinsp;average) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, higher humidity can increase the release of volatile organic compounds and black carbon [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which were not controlled for in our study, potentially worsening respiratory irritation and lung function [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Higher humidity can also increase allergens, triggering bronchospasm, and exacerbating obstruction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These findings collectively suggest that humidity may contribute to lung function obstruction.\u003c/p\u003e \u003cp\u003eHowever, our study found that long-term exposure to high RH was associated with a lower risk of restrictive patterns and was positively correlated with FVC. This may be due to higher humidity maintaining mucosal hydration, which prevents mucosal dryness and subsequent inflammation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Dry and damaged mucosal surfaces can trigger the release of inflammatory mediators that promote a type 2 inflammatory response characterized by the release of cytokines such as interleukin (IL)-4 and IL-13 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. There is evidence that IL-13 contributes to pulmonary fibrosis, including idiopathic pulmonary fibrosis, and that anti-IL-13 therapy can reduce fibrosis and enhance airway epithelium repair through both transforming growth factor-β-dependent and independent mechanisms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Collectively, these findings suggest that high humidity is associated with a reduced risk of epithelial injury and restrictive patterns.\u003c/p\u003e \u003cp\u003eHigher humidity may preserve lung function by maintaining mucociliary clearance and reducing chronic inflammation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Rea et al. revealed that long-term (12-month) humidification therapy with fully humidified air at 37\u0026deg;C, delivered daily through nasal cannulae to COPD and bronchiectasis patients (n\u0026thinsp;=\u0026thinsp;108), significantly reduced exacerbation days (18.2 days vs. 33.5 days; p\u0026thinsp;=\u0026thinsp;0.045), increased time to first exacerbation (median 52 days vs. 27 days; p\u0026thinsp;=\u0026thinsp;0.0495), and improved lung function and quality of life compared to usual care [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We also observed that chronic cough and sputum production decreased with higher mid-term RH. However, this trend did not persist for over a year in our study, likely because of seasonal variations. High summer humidity is counterbalanced by dry conditions in other seasons, making the benefits of higher RH on chronic respiratory symptoms transient.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, its cross-sectional design limits the ability to establish causality between RH and respiratory health outcomes, and longitudinal studies are needed to confirm these associations over time. Second, despite adjusting for various clinical and environmental covariates, residual confounding factors may have been present. However, we adjusted comprehensively for a range of individual environmental factors and air pollutants. Third, while the accuracy of individual RH values predicted by the CMAQ might have introduced some discrepancies compared to actual measurements, a previous study showed a high correlation (r\u0026thinsp;=\u0026thinsp;0.78) between the measured and predicted values [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Finally, the study population was limited to Korea; therefore, the findings may not be generalizable to other regions with different climatic conditions and population characteristics. Despite these limitations, our comprehensive adjustments for environmental and clinical factors enhanced the robustness of our findings using large-scale data with individual levels of RH exposure.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study found that RH affects lung function and respiratory symptoms in a complex manner, varying with exposure duration. Different RH levels have both positive and negative effects on respiratory health. These findings highlight the importance of considering RH in public health strategies. Further longitudinal studies are needed to confirm these associations and explore the long-term effects of RH on respiratory health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKorea National Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary function test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced vital capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced expiratory volume in one second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMAQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Multiscale Air Quality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eparticulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eparticulate matter with a diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esulfur dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enitrogen dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecarbon monoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eozone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epeak expiratory flow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of\u0026nbsp;the\u0026nbsp;Soonchunhyang University Seoul Hospital (SCHUH 2023-08-002). The requirement for informed consent was waived because the data from the KNHANES were de-identified and publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the current study are available from the corresponding author\u0026nbsp;upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Soonchunhyang University Research Fund.\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1G1A1010730).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH-Y.Y. takes full responsibility for the study conception and design and\u0026nbsp;the\u0026nbsp;content of this manuscript, including data and analysis. J.S. and B.L. performed statistical analyses and data interpretation. J.S. drafted the initial manuscript, and B.L.\u0026nbsp;edited it. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJoshi M, Goraya H, Joshi A, Bartter T. Climate change and respiratory diseases: a 2020 perspective. Curr Opin Pulm Med. 2020;26:119\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStafoggia M, Oftedal B, Chen J, Rodopoulou S, Renzi M, Atkinson RW, Bauwelinck M, Klompmaker JO, Mehta A, Vienneau D, et al. Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project. Lancet Planet Health. 2022;6:e9\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdginton S, O'Sullivan DE, King W, Lougheed MD. Effect of outdoor particulate air pollution on FEV(1) in healthy adults: a systematic review and meta-analysis. Occup Environ Med. 2019;76:583\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo H, Zhang Q, Niu Y, Kan H, Chen R. Fine particulate matter and cardiorespiratory health in China: A systematic review and meta-analysis of epidemiological studies. J Environ Sci (China). 2023;123:306\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominski FH, Lorenzetti Branco JH, Buonanno G, Stabile L, Gameiro da Silva M, Andrade A. Effects of air pollution on health: A mapping review of systematic reviews and meta-analyses. Environ Res. 2021;201:111487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAganovic A, Bi Y, Cao G, Kurnitski J, Wargocki P. 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Int J Biometeorol. 2017;61:1919\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLepeule J, Litonjua AA, Gasparrini A, Koutrakis P, Sparrow D, Vokonas PS, Schwartz J. Lung function association with outdoor temperature and relative humidity and its interaction with air pollution in the elderly. Environ Res. 2018;165:110\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Zhu T, Wang Q, Wang T, Chen W, Yao Y, Xu Y, Qiu X. Higher temperature and humidity exacerbate pollutant-associated lung dysfunction in the elderly. Environ Res. 2024;245:118039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunnbj\u0026ouml;rnsdottir MI, Norb\u0026auml;ck D, Plaschke P, Norrman E, Bj\u0026ouml;rnsson E, Janson C. The relationship between indicators of building dampness and respiratory health in young Swedish adults. Respir Med. 2003;97:302\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorb\u0026auml;ck D, Zock JP, Plana E, Heinrich J, Svanes C, Sunyer J, K\u0026uuml;nzli N, Villani S, Olivieri M, Soon A, Jarvis D. Lung function decline in relation to mould and dampness in the home: the longitudinal European Community Respiratory Health Survey ECRHS II. Thorax. 2011;66:396\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis RE, McGregor GR, Enfield KB. Humidity: A review and primer on atmospheric moisture and human health. Environ Res. 2016;144:106\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi SB, Yun S, Kim SJ, Park YB, Oh K. Effects of exposure to ambient air pollution on pulmonary function impairment in Korea: the 2007\u0026ndash;2017 Korea National Health and Nutritional Examination Survey. 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Am J Respir Crit Care Med. 2019;200:e70\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark HJ, Rhee CK, Yoo KH, Park YB. Reliability of Portable Spirometry Performed in the Korea National Health and Nutrition Examination Survey Compared to Conventional Spirometry. Tuberc Respir Dis (Seoul). 2021;84:274\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi JK, Paek D, Lee JO. Normal Predictive Values of Spirometry in Korean Population. Tuberc Respir Dis. 2005;58:230\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang MJ, Sung J, Yoon M, Kim JH, Yun HY, Choi DR, Koo YS, Oh K, Yun S, Cheong HK. Establishment of the Korea National Health and Nutrition Examination Survey air pollution study dataset for the researchers on the health impact of ambient air pollution. Epidemiol Health. 2021;43:e2021015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoo Y-S, Choi D-R, Yun H-Y, Yoon G-W, Lee J-B. A development of PM concentration reanalysis method using CMAQ with surface data assimilation and MAIAC AOD in Korea. J Korean Soc Atmospheric Environ. 2020;36:558\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Zhu Y, Hou D, Fu B, Li W, Guan H, Sinsky E, Kolczynski W, Xue X, Luo Y. The development of the NCEP global ensemble forecast system version 12. Weather Forecast. 2022;37:1069\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Ma X, Wen W, Sun C, Jiao J. Characteristics and potential sources of wintertime air pollution in Linfen, China. Environ Monit Assess. 2021;193:252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirinci E, Deniz A, \u0026Ouml;zdemir ET. The relationship between PM(10) and meteorological variables in the mega city Istanbul. Environ Monit Assess. 2023;195:304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Ma R, Wang Y, Jiang N, Zhang Y, Li T. The relationship between particulate matter and lung function of children: A systematic review and meta-analysis. Environ Pollut. 2022;309:119735.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajeevan K, Sumesh RK, Resmi EA, Unnikrishnan CK. An observational study on the variation of black carbon aerosol and source identification over a tropical station in south India. Atmospheric Pollution Res. 2019;10:30\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang S, Xiong J, Zhang Y. The Impact of Relative Humidity on the Emission Behaviour of Formaldehyde in Building Materials. Procedia Eng. 2015;121:59\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon HI, Hong YC, Cho SH, Kim H, Kim YH, Sohn JR, Kwon M, Park SH, Cho MH, Cheong HK. Exposure to volatile organic compounds and loss of pulmonary function in the elderly. Eur Respir J. 2010;36:1270\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranco Suglia S, Gryparis A, Schwartz J, Wright RJ. Association between traffic-related black carbon exposure and lung function among urban women. Environ Health Perspect. 2008;116:1333\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig S, Jimenez-Bush I, Brigham E, Bose S, Diette G, McCormack MC, Matsui EC, Davis MF. Analysis of home dust for Staphylococcus aureus and staphylococcal enterotoxin genes using quantitative PCR. Sci Total Environ. 2017;581\u0026ndash;582:750\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuarnieri G, Olivieri B, Senna G, Vianello A. Relative Humidity and Its Impact on the Immune System and Infections. Int J Mol Sci 2023, 24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWise SK, Laury AM, Katz EH, Den Beste KA, Parkos CA, Nusrat A. Interleukin-4 and interleukin-13 compromise the sinonasal epithelial barrier and perturb intercellular junction protein expression. Int Forum Allergy Rhinol. 2014;4:361\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleimer RP. Immunopathogenesis of Chronic Rhinosinusitis and Nasal Polyposis. Annu Rev Pathol. 2017;12:331\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijsenbeek MS, Kool M, Cottin V. Targeting interleukin-13 in idiopathic pulmonary fibrosis: from promising path to dead end. Eur Respir J 2018, 52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChidekel A, Zhu Y, Wang J, Mosko JJ, Rodriguez E, Shaffer TH. The effects of gas humidification with high-flow nasal cannula on cultured human airway epithelial cells. Pulm Med 2012, 2012:380686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasani A, Chapman TH, McCool D, Smith RE, Dilworth JP, Agnew JE. Domiciliary humidification improves lung mucociliary clearance in patients with bronchiectasis. Chron Respir Dis. 2008;5:81\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRea H, McAuley S, Jayaram L, Garrett J, Hockey H, Storey L, O'Donnell G, Haru L, Payton M, O'Donnell K. The clinical utility of long-term humidification therapy in chronic airway disease. Respir Med. 2010;104:525\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Yu S, Chen X, Zhang Y, Li M, Li Z, Song Z, Liu W, Li P, Xie M. Evaluation of the WRF-CMAQ model performances on air quality in China with the impacts of the observation nudging on meteorology. Aerosol Air Qual Res. 2022;22:220023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Humidity, Spirometry, Lung disease, Environmental exposure","lastPublishedDoi":"10.21203/rs.3.rs-4904104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4904104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAmbient humidity has a significant impact on respiratory health and influences disease and symptoms. However, large-scale studies are required to clarify the specific effects on lung function and respiratory symptoms. This study examined the relationship between relative humidity (RH), lung function, and respiratory symptoms using data from the Korea National Health and Nutrition Examination Survey(KNHANES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study analyzed data from KNHANES participants aged 40 and older, collected between 2016 and 2018. Pulmonary function tests (PFTs) and health questionnaires were used to assess lung function and respiratory symptoms. Individual environmental data, including RH, were obtained from the Community Multiscale Air Quality model and linked to the participants' addresses. Short-term (0–14 days), mid-term (30–180 days), and long-term (1–5 years) RH exposures were examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn total, 10,396 participants were included (mean age: 58.3 years, male: 43.6%). In multiple regression analysis, higher RH was negatively associated with the forced expiratory volume per 1 second/forced vital capacity (FVC) ratio across various time lags, while FVC was positively correlated with long-term RH exposure. In multiple logistic analysis adjusted for clinical and environmental covariates, long-term higher RH exposure was associated with a lower risk of restrictive lung disease (odds ratio [OR] at 4-year moving average [MA]: 0.978, 95% confidence interval [CI]: 0.959–0.997), while mid-term RH exposure decreased the risk of chronic cough (OR at 90-day MA: 0.968, 95% CI: 0.948–0.987) and sputum production (OR at 90-day MA: 0.984, 95% CI: 0.968–1.000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigher RH negatively affected lung function and increased the risk of obstructive lung disease, whereas mid-term RH exposure reduced the risk of chronic cough and sputum production.\u003c/p\u003e","manuscriptTitle":"Association between humidity and lung function: the 2016-2018 Korea National Health and Nutrition Examination Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-11 17:25:33","doi":"10.21203/rs.3.rs-4904104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-02T11:06:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-02T08:21:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T03:03:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132901056461087437522362029214598408136","date":"2024-08-27T14:31:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112548855107013573657058473556844682283","date":"2024-08-22T14:29:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-22T14:11:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-14T12:49:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-14T07:31:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2024-08-13T04:58:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"932aff71-3c53-4ff9-81ec-b8e329a2bbfd","owner":[],"postedDate":"October 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:04:58+00:00","versionOfRecord":{"articleIdentity":"rs-4904104","link":"https://doi.org/10.1186/s12931-024-03054-z","journal":{"identity":"respiratory-research","isVorOnly":false,"title":"Respiratory Research"},"publishedOn":"2024-12-02 15:57:53","publishedOnDateReadable":"December 2nd, 2024"},"versionCreatedAt":"2024-10-11 17:25:33","video":"","vorDoi":"10.1186/s12931-024-03054-z","vorDoiUrl":"https://doi.org/10.1186/s12931-024-03054-z","workflowStages":[]},"version":"v1","identity":"rs-4904104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4904104","identity":"rs-4904104","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}