Physical exercise attenuates the negative effects of short-term exposure to medium air pollution levels on cardio-respiratory responses

Physical exercise attenuates the negative effects of short-term exposure to medium air pollution levels on cardio-respiratory responses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physical exercise attenuates the negative effects of short-term exposure to medium air pollution levels on cardio-respiratory responses Xingsheng Jin, Weiyi Wang, Qian Sun, Yang Chen, Bingxiang Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4552474/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background Air pollution (AP) has become a substantial environmental issue affecting human cardiorespiratory health. Physical exercise (PE) is widely accepted to promote cardiorespiratory health. There is a paucity of research on the point at which the level of polluted environment engaged in PE could be used as a preventive approach to compensate for the damages of AP. Objectives To determine the effects of PE on cardio-respiratory and inflammatory responses in different levels of short-term exposure to AP among healthy young adults. Methods We constructed a real-world crossover study of 30 healthy young adults with repeated measures. Participants participated in 90 min of moderate-intensity PE in different (low, medium, high) AP exposure scenarios. Cardiorespiratory measurements and blood samples were collected before and after the intervention. The percentage changes in cardiorespiratory health markers after exercise in the three AP levels environments were compared using linear mixed-effects models. Results Compared to the changes observed post-exercise in the low-level AP environment, only PEF (-9.36, P = 0.018) showed a significant decrease, and eosinophils showed a significant increase in the medium-level environment (25.64, P = 0.022), with no significant differences in other indicators. Conversely, post-exercise in the high-level AP environment resulted in a significant increase in DBP (6.5, P = 0.05), lung inflammation (FeNO: 13.3, p < 0.001), inflammatory cell counts (WBC: 27.0, p < 0.001; neutrophils: 26.8, p < 0.001; lymphocytes: 32.2, p < 0.001; monocytes: 28.2, p < 0.001; and eosinophils: 48.9, p < 0.001), and inflammatory factors (IL-1β: 0.76, P = 0.003; IL-10: 0.17, P = 0.02; IL-6: 0.1, P = 0.17; TNF-α: 0.97, P = 0.011; CRP: 0.17, P = 0.003). Additionally, there were significant declines in lung function parameters, including FVC (-6.84, P = 0.04), FEV1 (-8.97, P = 0.009), and PEF (-9.50, P = 0.013). Conclusions PE in medium and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response. Air pollution PM2.5 Physical exercise Cardiorespiratory health Inflammatory response Figures Figure 1 Figure 2 Figure 3 Introduction Billions of people worldwide are exposed to environments with air pollution (AP) [ 1 ]. AP poses significant health threats to humanity [ 2 , 3 ], particularly impacting cardiorespiratory health [ 4 , 5 ]. The pollutants in the air, especially particulate matter (PM), may induce systemic chronic inflammatory responses and oxidative stress in the body, which could be mechanistic pathways leading to adverse health outcomes in cardiorespiratory health [ 6 ]. As is well known, regular physical exercise (PE) brings numerous benefits to physical health, such as enhancing cardiorespiratory function, reducing inflammatory responses, and improving immunity [ 7 – 9 ]. However, an increase in PE also entails an increased risk of exposure to AP. Engaging in PE in environments with AP may lead to higher inhalation doses of pollutants due to deeper and faster breathing and increased ventilation [ 6 , 10 ], which could potentially exacerbate the adverse effects of AP on cardiorespiratory health. Thus, the question of whether PE in AP environments is beneficial to cardiorespiratory health has become a hot topic of investigation in recent years. Reviewing prior research reveals a lack of consensus due to differences in study populations, levels of pollution, research designs, and exercise protocols. Most studies have focused on middle-aged and elderly individuals or those with cardiorespiratory diseases, with fewer studies conducted on healthy young adults [ 11 , 12 ]. There are also significant variations in the levels of pollution exposure across different studies. Additionally, due to ethical reasons, there is limited research on the cardiorespiratory health effects of exercising in environments with high AP concentrations. Variations in study designs and exercise regimens (such as type, duration, and intensity of exercise) can lead to significant differences in the amount of pollutants inhaled. For example, Matt et al. [ 13 ]conducted 2 hours of moderate-intensity intermittent physical activity (such as 15-minute intermittent cycle ergometry), while Kocot et al. [ 12 ]only performed 15-minute submaximal exercise trials on a cycle ergometer. Therefore, there is a significant difference in the amount of pollutants inhaled by both, which can result in changes in measurement outcomes. Some studies suggest that compared to rest, PE can counteract the adverse effects of AP on cardiorespiratory health [ 13 – 15 ], while others argue that continuing exercise as AP levels rise may decrease cardiorespiratory function [ 12 , 16 ] and increase inflammatory responses [ 17 , 18 ]in the body. Therefore, determining the safety threshold for PE in AP environments is a goal that warrants continuous exploration. Consequently, in this study, we have decided to investigate the beneficial effects of PE on cardiorespiratory health in environments with three different AP concentrations. We aim to identify under which AP concentration PE may be beneficial for cardiorespiratory health, thus contributing to the existing body of research evidence. Methods Study design and participants This study employed a self-controlled crossover design, wherein participants engaged in 90 minutes of moderate-intensity PE under three different AP concentrations (low, medium, and high). Inclusion criteria for participants were as follows: (1) aged 18–30 and enrolled university students; (2) physically healthy; (3) no medication intake within the past 3 weeks; (4) no history of pulmonary or cardiovascular diseases; (5) absence of symptoms such as cold or fever; (6) non-smokers; (7) voluntary participation and cooperation with researchers throughout the study. Exclusion criteria included: (1) cardiovascular and respiratory system diseases; (2) recent medication treatment; (3) nasal allergy sufferers; (4) inability to tolerate moderate-to-high-intensity exercise; (5) asthma, allergy, or hypersensitivity conditions; (6) female participants menstruating during the exercise period. All participants provided voluntary informed consent after agreeing to participate in the study. The study received approval from the Ethics Committee of Shanghai University of Sport (Approval No.: 102772019RT001). The experiments were conducted between September 2023 and December 2023. Participants were instructed to abstain from alcohol consumption and vigorous physical activity 24 hours before the experiment and avoid exposure to high pollution levels in the air. Additionally, coffee and soy milk intake were prohibited within 3 hours before testing. To minimize the influence of different measurement times on the results, all experiments and measurements were conducted at the same time of day, and participants were required to consume a standardized meal 30 minutes before the experiment to reduce interference from the diet. On the day of the experiment, participants arrived at the laboratory at 7:30 a.m., underwent baseline health indicator measurements after a brief rest, and then walked to the 100-meter playground for a 90-minute moderate-intensity exercise session from 9:30 a.m. to 11:00 a.m. The exercise regimen included warm-up running (5 minutes), warm-up exercises (5 minutes), aerobic exercises (40 minutes), games (30 minutes), and stretching and relaxation (10 minutes). The entire exercise intervention was led by experienced coaches. After the 90-minute exercise intervention, participants returned to the laboratory immediately for post-intervention health indicator measurements. Blood samples were collected first, with venous blood collection completed within 15 minutes after exercise, followed by the measurement of cardiorespiratory health indicators starting 30 minutes after the exercise intervention. Each participant completed PE under three different AP environments, and between each experiment, participants underwent a washout period of at least 2 weeks. Physical exercise monitoring Before the exercise, participants uniformly wore Polar heart rate monitors to objectively monitor their exercise intensity. The exercise intensity was controlled moderately corresponding to 70% of each participant's maximum heart rate. The maximum heart rate was calculated based on the latest international standard algorithms considering age and gender, where for males, the maximum heart rate = 220 - age; for females, the maximum heart rate = 206 − 0.88 × age [ 19 ]. During the exercise session, researchers and coaches could adjust the exercise pace in real-time based on the monitoring results to ensure that the predetermined exercise intensity was achieved. Environmental exposure monitoring The research design involved conducting moderate-intensity exercise experiments under three different AP levels (low: PM 2.5 ≤ 75 µg/m 3 , medium: 75 µg/m 3 115 µg/m 3 ), based on the latest air quality guidelines from the World Health Organization (WHO) and China's Environmental Quality Standards. If the conditions were not met, the exercise intervention experiment was not conducted. During the 90-minute exercise intervention period, the air temperature, relative humidity, and PM 2.5 concentration at the monitoring site were monitored. For PM 2.5 measurement, the SidePak™ AM520i Personal Aerosol Monitor was used to detect changes in ambient PM 2.5 concentration, with the instrument sampling every 10 seconds. Data on other air pollutants were obtained from environmental monitoring stations located 3 km from the experimental center, with the average values during the intervention period taken as the pollution levels for the experiment. Cardiorespiratory health measurements The equipment used for heart rate and blood pressure testing was the Omron J710 Blood Pressure Monitor from Japan, which measured systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse. Three consecutive measurements were taken each time, and the average was recorded. For pulmonary function testing, in accordance with the standards of the European Respiratory Society (ERS) and the American Thoracic Society (ATS) [ 20 ], the Italian New Spirolab® Pulmonary Function Testing Instrument was employed. The measured indices included forced vital capacity (FVC), forced expiratory volume in one second (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (FEF 25− 75% ). Fractional exhaled nitric oxide (FeNO) was measured using the portable NIOX MINO Analyzer (Aerocrine AB, Solna, Sweden). Blood sample collection Thirty minutes before the start of the exercise and within 15 minutes after its completion, trained medical personnel collected venous blood samples from the study participants. Blood samples were drawn using Ethylenediaminetetraacetic Acid (EDTA) anticoagulant tubes for routine blood tests. Serum samples for the measurement of IL-1β, IL-10, IL-6, TNF-α, and CRP were centrifuged and aliquoted within 4 hours after blood collection and stored at -80°C. Ethical considerations Ethics approval for the study was obtained from the Ethics Committee of Shanghai University of Sport (Ethics approval no: 102772019RT001) and registered in the Chinese Clinical Trial Registry (Registered No: ChiCTR2000031851). Written informed consent was obtained from participants before they participated in the study. Statistical analysis Statistical analysis of the data was conducted using R 4.3.2 software. Descriptive analysis was primarily focused on the basic information of the study participants, pollutant concentrations, and fundamental characteristics of health indicators. Continuous variables were described using arithmetic means and standard deviations, while categorical variables were described using proportions. Changes in health indicators before and after exercise were expressed as relative differences ((post-exercise - baseline) / baseline), where a relative difference < 0% indicated a decrease after exercise. The Shapiro-Wilk test was employed to assess the distribution of data. Paired t-tests were used to evaluate the statistical significance of differences between pre- and post-exercise measurements in each experiment. Due to the non-normal distribution of pollutant concentrations, Wilcoxon rank-sum tests were utilized to assess differences between the three different AP levels. Considering the repeated study design, linear mixed-effects models (LME) were constructed using the ‘lme4’ package in R. These models analyzed changes relative to baseline values after exercise across the three experiments. The study participants' ID was included as a random effect in the model to account for individual variability in all health outcomes. Gender, age, and body mass index (BMI) were included as fixed effects. This study considered a two-tailed p-value < 0.05 to indicate statistical significance. Results Subject characteristics A total of 30 participants were recruited, all of whom completed exposures in all three scenarios. Among them, there were 16 males (53.3%) and 14 females (46.7%). The mean age of the 30 participants was 20.1 ± 0.9 years, with an average BMI of 23.0 ± 1.9 kg/m². Specific characteristics of the participants are presented in Table 1 . Table 1 Characteristics of the study group(Mean ± SD) Characteristics Male Female All N(%) 16(53.3%) 14(46.7%) 30 Age(years) 20.4 ± 0.8 19.7 ± 0.9 20.1 ± 0.9 Height(cm) 175.9 ± 3.2 165.6 ± 5.4 171.1 ± 6.7 Body mass(kg) 66.4 ± 8.9 56.0 ± 8.0 61.6 ± 9.9 BMI(kg/m 2 ) 21.7 ± 0.8 24.5 ± 1.6 23.0 ± 1.9 SD: standard deviation; BMI: body mass index. Pollution levels During the 90-minute exercise intervention experiments, the environmental levels of PM 2.5 were monitored using the SidePak™ AM520i Individual Aerosol Monitor, showing variations over time (Fig. 1 ). The average concentrations of PM 2.5 were 35.63 ± 5.23 µg/m 3 , 95.58 ± 10.98 µg/m 3 , and 174.26 ± 17.86 µg/m 3 , respectively. Detailed characteristics of the environmental conditions recorded during the three experiments are provided in Table 2 . Significant differences were observed in the levels of PM 2.5 , inhalable particulate matter PM 10 , SO 2 , and NO 2 during the three intervention periods. Specifically, the AP levels at the medium level were significantly higher than those at the low level, while the AP levels at the high level were significantly higher than those at the medium level. The pollution concentration levels in the experiment align with expectations. Table 2 Distribution of environmental conditions recorded during exercise trials. Variable Low Medium High p Value* SO 2 (µg/m 3 ) 14.2 ± 3.3 33.6 ± 19.2 44.0 ± 24.4 <0.0142 NO 2 (µg/m 3 ) 24.0 ± 5.6 65.2 ± 9.9 65.4 ± 18.7 <0.0003 PM 10 (µg/m 3 ) 46.4 ± 6.1 147.8 ± 16.7 187.0 ± 12.1 <0.0019 PM 2.5 (µg/m 3 ) 35.63 ± 5.23 95.58 ± 10.98 174.26 ± 17.86 <0.0001 Temperature(℃) 16.61 ± 0.94 9.22 ± 0.76 1.3 ± 1.4 <0.0001 Relative humidity(%) 58.84 ± 0.91 35.54 ± 0.84 36.22 ± 2.5 <0.0001 All data are presented as mean ± standard deviation. *: significant differences between the three experimental conditions (Wilcoxon test results). Cardiorespiratory health In this study, measurements of cardiorespiratory function and airway inflammation indicators were taken before and after each of the three exercise interventions. Table 3 presents the average differences in cardiorespiratory health measurements before and after exercise at three different levels of AP. Table 3 Mean differences in baseline and post-exercise cardiorespiratory health measurements Variable Low p Value * Medium p Value * High p Value * SBP (mm Hg) -2.2 ± 8.0 < 0.047 -4.2 ± 13.4 0.126 -4.3 ± 11.8 0.071 DBP (mm Hg) -1.2 ± 3.7 0.063 -0.8 ± 10.1 0.701 3.5 ± 7.8 0.03 HR (bpm) 10.8 ± 7.4 < 0.001 12.6 ± 6.5 < 0.001 10.2 ± 5.2 < 0.001 FVC (L) 0.2 ± 0.6 0.041 0.3 ± 0.5 0.05 -0.1 ± 0.4 0.498 FEV1 (L) 0.2 ± 0.5 0.049 0.1 ± 0.5 0.591 -0.2 ± 0.4 0.031 PEF (L/min) 0.5 ± 1.6 0.087 -0.0 ± 1.1 0.91 -0.1 ± 0.8 0.381 FEF 25 − 75% (L/s) 0.2 ± 1.7 0.285 0.2 ± 0.8 0.0342 -0.1 ± 0.6 0.461 FeNO (ppb) -3.0 ± 4.0 < 0.001 -1.7 ± 3.9 0.038 1.0 ± 4.2 0.236 All data are presented as mean difference ± standard deviation. *: significant differences between baseline and post-exercise (paired t-test results). Abbreviations : SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s; PEF, Peak expiratory flow; FEF 25–75% , mean forced expiratory flow between 25% and 75% of FVC; FeNO, fractionated exhaled nitric oxide. The results indicate that SBP significantly decreased after exercise in the low pollution level environment (-2.2 mm/Hg, p < 0.047), while the mean decreased in the medium and high pollution level environments after exercise, although without significant differences. Similarly, DBP decreased after exercise in the low and medium pollution level environment, with no significant differences observed, but increased significantly after exercise in the high pollution level environment (3.5 mm/Hg, p = 0.03). For lung function, post-exercise mean values of FVC and FEV1 significantly increased in the low pollution level environment (0.2L, P = 0.041) (0.2L, P = 0.049), while mean values of PEF and FEF 25 − 75% also increased, albeit without significant differences. Additionally, post-exercise FVC still showed a weak significant increase in the medium pollution level environment (0.3 ± 0.5, P = 0.05). However, post-exercise mean values of FEV1 significantly decreased in the high-level AP environment (-0.2 ± 0.4, P = 0.031), with other lung function indicators also showing decreases in the high pollution level environment, but without significant differences. For airway inflammation indicators, we found that FeNO significantly decreased after exercise in both the low pollution level (-3 ppb, p < 0.001) and medium pollution level (-1.7 ppb, p = 0.038) environments, while it increased in the high pollution level environment, although without significant differences. Based on the observed changes in cardiorespiratory function indicators pre- and post-exercise, it is evident that exercising in medium-level AP environments may not lead to as beneficial significant changes in cardiorespiratory function indicators as seen with exercise in low pollution environments. Conversely, exercising in high pollution environments yields adverse effects. This suggests that AP may diminish the benefits of PE on cardiorespiratory function. Additionally, we calculated the percentage change of cardiorespiratory-related health indicators relative to the baseline to adjust for individual differences at the baseline level and analyzed the changes in cardiorespiratory health indicators among three different levels of AP. The specific changes are illustrated in Fig. 2 . We observed that after exercise in environments with three different AP concentrations, blood pressure decreased in all three concentration environments, with SBP decreasing below 0, indicating a decrease after exercise. DBP decreased after exercise in environments with medium to low pollution concentrations, while it increased in environments with high pollution concentrations. Analysis of lung function indicators revealed that FVC, FEV1, and FEF 25 − 75% increased after exercise in environments with medium pollution concentrations, while they decreased after exercise in environments with high pollution concentrations. Additionally, the percentage changes in lung function-related indicators after exercise in environments with low, medium, and high concentrations of pollution all showed a decreasing trend. Analysis of the airway inflammation indicator FeNO revealed a decrease after exercise in environments with low to medium pollution concentrations, while it increased after exercise in environments with high pollution concentrations. Moreover, after exposure to environments with three different concentrations of pollution, the percentage change in FeNO values showed an increasing trend. Subsequently, we utilized LME to adjust for participants' gender, age, and BMI. Using the percentage change in cardiorespiratory health indicators after exercising in the low pollution level environment as the reference, we further confirmed the aforementioned results. The specific results were consistent with the description provided in Fig. 2 above, and the analytical outcomes are detailed in Table 4 . Table 4 Differences in cardiorespiratory health among three different AP levels SBP DBP Coefficient 95% CI p Value * Coefficient 95% CI p Value * M -0.62 -4.76 3.41 0.770 -3.44 5.21 7.66 0.685 H -1.44 -5.58 2.59 0.496 6.45 2.11 10.75 0.005 FVC FEV1 Coefficient 95% CI p Value * Coefficient 95% CI p Value * M -0.43 -7.24 6.0 0.9 -6.57 -13.44 0.01 0.062 H -6.84 -13.41 -0.68 0.04 -8.97 -15.62 -2.63 0.009 PEF FEF 25 − 75% Coefficient 95% CI p Value * Coefficient 95% CI p Value * M -9.36 -16.71 -2.00 0.018 -0.87 -13.29 2.93 0.828 H -9.50 -16.60 -2.41 0.013 -4.35 -15.88 0.63 0.248 FeNO Coefficient 95% CI p Value * M 5.2 -2.20 12.56 0.183 H 13.3 5.93 20.69 0.001 Mixed effect models adjusted for gender, age, and BMI. Exposure scenario with reference to ‘Low level AP and PE exposure’. * : Statistical significance in the results of the linear mixed-effects model. M: Medium level AP and PE exposure; H: High level AP and PE exposure. The LME analysis revealed that the changes in SBP at medium and high levels of AP environment both showed no significant difference compared to the low concentration. However, DBP showed a significant increase in the high concentration of AP compared to the low level (6.45, P = 0.05), with no significant difference observed in relative changes between medium and low levels. Regarding lung function, compared to the changes after exercise in environments with low pollution concentrations, PEF significantly decreased after exercise in environments with medium (-9.36, P = 0.018) and high (-9.50, P = 0.013) pollution concentrations. FVC and FEV1 showed no significant differences after exercise in medium concentrations but significantly decreased after exercise in high concentrations (-6.84, P = 0.04) (-8.97, P = 0.009). Regarding the airway inflammation indicator FeNO, compared to the changes after exercise in environments with low pollution concentrations, there was no significant difference after exercise in environments with medium concentrations, but FeNO significantly increased after exercise in environments with high concentrations (13.3, P = 0. 001). The results further demonstrate that changes in cardiorespiratory health indicators post-exercise in high-level AP environments are significantly decreased compared to low-level AP environments. Conversely, in low- and medium-level AP environments, differences in cardiorespiratory health indicator changes are not statistically significant. Effects on circulating inflammation markers Figure 3 illustrates the percentage change relative to baseline in inflammatory markers following exercise across three different levels of AP. Overall, the change in inflammatory markers after exercise in environments with high pollution concentrations was notably higher than those in medium and low concentrations, while the changes between medium and low concentrations were small. Moreover, the majority of inflammatory markers showed an increase after exercise regardless of the AP concentration, except monocytes and eosinophils, which exhibited a slight decrease in change after exercise in environments with medium and low concentrations. Table 5 Differences in inflammatory marker changes among three different levels of AP WBC Neutrophils Coefficient 95% CI p Value * Coefficient 95% CI p Value * M 1.77 -7.75 11.40 0.721 -3.51 -18.44 11.74 0.653 H 27.0 17.48 36.63 0.000 26.76 12.06 41.90 0.000 Lymphocytes Monocytes Coefficient 95% CI p Value * Coefficient 95% CI p Value * M 3.35 -9.03 15.34 0.594 2.11 -6.90 10.97 0.647 H 32.22 19.85 44.22 0.000 28.23 19.28 36.95 0.000 Eosinophils Basophils Coefficient 95% CI p Value * Coefficient 95% CI p Value * M 25.64 3.93 46.41 0.022 14.33 -4.89 32.78 0.142 H 48.93 27.25 70.12 0.000 11.5 -8.00 29.79 0.236 IL-1β IL-10 Coefficient 95% CI p Value * Coefficient 95% CI p Value * M 0.39 -0.09 0.86 0.116 0.05 -0.09 0.19 0.503 H 0.76 0.29 1.23 0.003 0.17 0.03 0.32 0.020 IL-6 TNF-α Coefficient 95% CI p Value * Coefficient 95% CI p Value * M 0.05 -0.02 0.11 0.174 -0.00 -0.08 0.07 0.920 H 0.10 0.04 0.17 0.004 0.97 0.02 0.17 0.011 CRP Coefficient 95% CI p Value * M 0.05 -0.06 0.16 0.370 H 0.17 0.06 0.28 0.003 Mixed effect models adjusted for gender, age, and BMI. Exposure scenario with reference to ‘Low level AP and PE exposure’. * : Statistical significance in the results of the linear mixed-effects model. M: Medium level AP and PE exposure; H: High level AP and PE exposure. Abbreviations : IL, interleukin; TNF-α, tumour necrosis factor α; CRP, C reactive protein. Table 5 presents the results of LME analysis comparing the differences in percentage change of inflammatory markers between medium and high levels of AP with reference to the percentage change at the low pollution level. The results indicate that compared to the changes observed after exercise in the low pollution environment, there was a significant increase in the levels of white blood cells (27.0, p < 0.001), neutrophils (26.8, p < 0.001), lymphocytes (32.2, p < 0.001), monocytes (28.2, p < 0.001), eosinophils (48.9, p < 0.001), IL-1β (0.76, P = 0.003), IL-10 (0.17, P = 0.02), IL-6 (0.1, P = 0.17), TNF-α (0.97, P = 0.011), and CRP(0.17, P = 0.003) after exercise in the high pollution environment. Additionally, only eosinophils exhibited a significant increase (25.6, p = 0.022) in the medium-pollution environment, while the percentage change in other inflammatory cells after exercise in the medium-pollution environment did not significantly differ from that in the low pollution environment. The results above suggest that high levels of AP significantly increase inflammation in the body, while the difference in inflammation levels between medium and low pollution levels is smaller. Discussion This study conducted a self-controlled crossover design to assess the effects of moderate-intensity physical exercise on the cardiorespiratory health of healthy young adults in environments with low, medium, and high AP concentrations. Our findings indicate that PE in medium and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response. Pollution Levels Reviewing previous studies, it is evident that different studies have varied definitions for low and high concentration ranges. Some studies classify pollution levels as high, while other studies might consider them low, potentially contributing to inconsistent research conclusions. Our study clearly distinguishes between low, medium, and high levels of AP concentrations. For instance, in the study by Kocot et al. [ 12 ], a threshold of PM 10 at 50 µg/m 3 was used to differentiate between good and poor air quality, categorizing pollution levels as poor that aligns with our high pollution exposure levels. However, our PM 2.5 exposure concentrations are higher than those reported by Kocot et al. Additionally, in our study, medium pollution exposure levels are comparable to high pollution exposure levels in other experimental studies [ 13 , 17 ]. Effects of combined exercise and AP exposure on cardiorespiratory function Our experimental findings indicate that exercise can reduce blood pressure, which is consistent with previous research results. Moreover, compared to exposure at medium to high concentrations, exercising at lower concentrations significantly lowers SBP. Similarly, in a crossover trial conducted in Barcelona, Spain, Kubesch et al. [ 21 ] found that intermittent PA was associated with lower SBP compared to resting, particularly following exposure to lower traffic-related air pollution (TRAP). Additionally, Kubesch et al. [ 21 ] demonstrated that exposure to higher TRAP was associated with higher DBP compared to lower TRAP. Krzysztof et al. [ 12 ] conducted a crossover study on healthy adult males, which also showed significant differences in the relative changes of DBP between pollution exposure experiments and control experiments, with a greater increase during pollution exposure experiments. This finding aligns with ours, where we observed a slight but significant increase in DBP following exercise exposure to higher pollution concentrations compared to exposure at lower levels. This suggests that even though exercise can regulate blood pressure, exercising in environments with higher pollution levels may still increase DBP. This could be attributed to the increased concentration of PM 2.5 , which may weaken the blood pressure-lowering effect of exercise. Evidence suggests that inhaling PM may trigger acute autonomic imbalance, leading to acute endothelial/vascular dysfunction, favoring vasoconstriction and a sharp decline in aortic compliance, as well as increased bioactivity of endothelin or renin-angiotensin-aldosterone system activation [ 22 , 23 ]. These factors, individually or collectively, may contribute to an elevation in blood pressure within hours of exposure to air particles. Therefore, elevated blood pressure may be a biomarker of adverse pathways leading to increased cardiovascular risk [ 22 ]. Regarding changes in lung function, Kubesch et al. [ 17 ] conducted a study involving 28 healthy adults, and their findings regarding low levels of AP concentration align with ours. They found that following PA during periods of low TRAP exposure, there was a significant increase in FEV1 and FEF 25 − 75% . They also demonstrated that PA was associated with increases in FEV1, FVC, and FEF 25 − 75% compared to rest, and even exercise in high TRAP environments had beneficial effects on lung function. In our study, we observed a significant increase in mean FVC and FEV1 following exercise in low-concentration environments, with PEF and FEF 25 − 75% showing an increase as well, albeit not statistically significant. Following exercise in medium-concentration environments, mean FVC still showed a slight but significant increase. This suggests that PA remains beneficial for lung function in environments with medium to low AP concentrations. Similarly, in a crossover study by Matt et al. [ 13 ] involving 30 healthy adults, immediate post-exercise comparisons with baseline showed significant increases in FEV1 (48.5 mL, p = 0.02), FEV1/FVC (0.64%, p = 0.01), and FEF 25 − 75% (97.8 mL, p = 0.02). However, in our study, the magnitude of respiratory responses was small, and these responses were observed only in healthy young adults. Furthermore, in the LME, we found that compared to exercise in low-concentration environments, there was a significant decrease in FVC, FEV1 during exercise in high-concentration environments, and PEF during exercise in medium to high-concentration environments. Additionally, the differences in relative changes in other lung function indicators gradually increased and showed a decreasing trend. This suggests that although exercise improves lung function, the benefits diminish with increasing pollutant concentrations. Kocot et al. [ 12 ] conducted a crossover experiment involving 15 minutes of submaximal exercise in healthy young adult males under conditions of poor and good air quality. The pollutant concentrations in their exposure group were similar to our medium-concentration pollutant levels. They compared the relative changes between the exposure and control groups and found no differences in FVC, FEV1, and FEV1/FVC after exercise, which is consistent with our findings. Unlike Kocot et al., we also compared the changes in cardiorespiratory health indicators after exercise in high and low AP concentration environments, finding significant decreases in FVC, FEV1, and PEF. Kocot et al. concluded that acute respiratory changes following exercise under exposure conditions depend on pollutant concentrations, with only participants exposed to particularly high levels showing acute decreases in FEV1/FVC post-exercise, and the relative changes in FEV1/FVC were significantly negatively correlated with pollutant concentrations. Strak et al. [ 24 ] investigated the effects of AP on the respiratory health of healthy cyclists and found a slight increase in lung function immediately after cycling, but a negative correlation with AP emerged six hours after cycling. Matt et al.'s study [ 13 ] also indicated that PA mitigates the negative effects of PM on the upper and lower respiratory tracts, with substantial evidence of interaction between PM and physical activity's respiratory effects. They suggested that increased lung ventilation during physical activity may lead to a higher proportion of particle deposition in the nasal pharyngeal region by collision, thereby affecting PM deposition and preventing interference with the impact of physical activity on the upper and lower airways [ 13 ]. Changes in inflammation after combined exposure to exercise and AP. Previous studies have demonstrated that higher levels of AP, particularly PM, can induce the production of nitric oxide by epithelial cells, leading to a significant increase in FeNO levels [ 25 , 26 ], resulting in local inflammatory responses. Furthermore, research has indicated a correlation between changes in FeNO levels after exercise and the concentration of air pollutants. For instance, Kubesch et al. [ 17 ] observed a significant association between coarse particulate matter and increased FeNO levels. Additionally, they noted a modest increase in FeNO levels after PA compared to rest. In contrast to their findings, our study reveals a significant decrease in mean FeNO levels after exercise in environments with medium to low AP, while in high AP environments, mean FeNO levels increase but without significant differences. This difference may be attributed to variations in study design; our study only collected FeNO levels 30 minutes post-exercise and did not gather information on longer-term reactions following combined exercise and AP exposure. Inflammatory responses following AP exposure may require more time, as PA has been shown to increase nitric oxide production through epigenetic changes, with the association between PM 2.5 and FeNO being most significant with a one-day lag time [ 27 ]. Kocot et al. [ 28 ] conducted exercise sessions with 76 healthy university students under conditions of high AP and good air quality, finding that increased FeNO levels were associated with higher levels of PA and higher concentrations of air pollutants. Moreover, the statistical significance of the difference in FeNO levels between pollution exposure and control experiments was observed 15 minutes after exercise cessation, rather than immediately post-exercise. Hence, the duration of the inflammatory response is also a factor to consider in this study. Additionally, Bos et al. [ 29 ] found that FeNO levels increased after training in urban environments, while aerobic training in rural environments did not affect FeNO levels. Consistent with their findings, the linear mixed-effects analysis in our study also suggests a significant increase in FeNO levels after exercise in high AP environments relative to low concentrations. This indicates that engaging in PA in highly polluted air environments may increase respiratory tract inflammation levels, triggering local inflammatory responses. There is limited research on the systemic inflammatory response to combined exposure to AP and PA. However, previous reports have indicated [ 17 ] that engaging in PA in environments with AP can increase the count of inflammatory cells, leading to a systemic inflammatory response. Acute PA increases the number of inflammatory cells in the body, and exercise also increases the dose of inhaled particles, resulting in an increase in systemic inflammatory biomarkers. The combined effect of these factors leads to an increase in systemic inflammatory markers in the body. Kubesch et al. [ 17 ] found that compared to no exercise in low TRAP conditions, there was a significant increase in white blood cells after exercise during high TRAP exposure. They also observed associations between PM 10 and PM 2.5 with increased white blood cells and between coarse PM and increased neutrophils. Bos et al. [ 29 ] investigated the changes in inflammatory markers following moderate exercise conducted on a bicycle ergometer in urban and rural environments. They found that after training in the urban setting, there was an increase in white blood cells and neutrophils, whereas no change was observed in the rural group. Consistent with our findings, acute exercise led to an increase in most inflammatory cells regardless of the AP concentration, with a more pronounced increase observed after exercise in environments with higher concentrations. Furthermore, a comparison of the percentage change in inflammatory cells between medium and high levels relative to low levels using LME also revealed a significant increase in white blood cells, neutrophils, lymphocytes, monocytes, eosinophils and other inflammatory factors at high levels, with a greater magnitude of change. In contrast, only eosinophils showed significant changes at medium levels, with no significant differences observed in other inflammatory cell changes. This suggests that exercise in environments with medium pollution levels leads to relatively small changes in inflammatory response compared to low levels, while exercise in environments with higher AP concentrations substantially increases the systemic inflammatory response, adversely affecting the body. Limitations Our study has several limitations. Firstly, the sample size is small, and the study population consists of healthy young adults, thus the findings may not be generalizable to other populations. Secondly, we only focused on short-term exercise and acute responses. Longer duration exercises or repeated measurements over longer periods post-exercise could provide more substantial insights. Additionally, during low-level exposure, participants were exposed to unfiltered air. Therefore, we primarily compared the effects of pollution at three different concentration levels rather than comparing AP to clean air. Lastly, due to the extended duration of the experiments, variations in environmental temperature and relative humidity may have introduced some confounding effects into the results. Conclusions PE in medium- (PM 2.5 ≤115µg/m 3 ) and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response. Declarations Acknowledgements We would like to thank M. Min He for the English language editorial services. Author contributions Xingsheng Jin: Writing – original draft, Writing - review & editing. Weiyi Wang: Writing - review & editing. Qian Sun: Writing - review & editing. Yang Chen: Writing - review & editing. Bingxiang Xu: Supervision, Funding acquisition, Writing - review & editing. Haili Tian: Supervision, Funding acquisition, Writing - review & editing. Funding This work was supported by grants from the Humanities and Social Sciences Youth Fund of the Ministry of Education (grant number 21YJC890030), the Shanghai Natural Science Foundation (grant number 23ZR1403700), the National Natural Science Foundation of China (grant number 32200515), and the Open Research Fund of the National Key Laboratory of Genetic Engineering (grant number SKLGE-2315). Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Ethics approval for the study was obtained from the Ethics Committee of Shanghai University of Sport (Ethics approval no: 102772019RT001) and registered in the Chinese Clinical Trial Registry (Registered No: ChiCTR2000031851). Written informed consent was obtained from participants before they participated in the study. Consent for publication All participants provided informed consent for publication. Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E: Environmental and Health Impacts of Air Pollution: A Review. Front Public Health 2020, 8:14. Holgate S: Air pollution is a public health emergency. BMJ 2022, 378:o1664. Schraufnagel DE, Balmes JR, De Matteis S, Hoffman B, Kim WJ, Perez-Padilla R, Rice M, Sood A, Vanker A, Wuebbles DJ: Health Benefits of Air Pollution Reduction. Ann Am Thorac Soc 2019, 16(12):1478-1487. Miller MR: The cardiovascular effects of air pollution: Prevention and reversal by pharmacological agents. Pharmacol Ther 2022, 232:107996. Lee KK, Bing R, Kiang J, Bashir S, Spath N, Stelzle D, Mortimer K, Bularga A, Doudesis D, Joshi SS et al: Adverse health effects associated with household air pollution: a systematic review, meta-analysis, and burden estimation study. Lancet Glob Health 2020, 8(11):e1427-e1434. Hahad O, Kuntic M, Frenis K, Chowdhury S, Lelieveld J, Lieb K, Daiber A, Munzel T: Physical Activity in Polluted Air-Net Benefit or Harm to Cardiovascular Health? A Comprehensive Review. Antioxidants (Basel) 2021, 10(11). Ruegsegger GN, Booth FW: Health Benefits of Exercise. Cold Spring Harb Perspect Med 2018, 8(7). Chastin SFM, Abaraogu U, Bourgois JG, Dall PM, Darnborough J, Duncan E, Dumortier J, Pavon DJ, McParland J, Roberts NJ et al: Effects of Regular Physical Activity on the Immune System, Vaccination and Risk of Community-Acquired Infectious Disease in the General Population: Systematic Review and Meta-Analysis. Sports Med 2021, 51(8):1673-1686. Ferguson T, Olds T, Curtis R, Blake H, Crozier AJ, Dankiw K, Dumuid D, Kasai D, O'Connor E, Virgara R et al: Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digit Health 2022, 4(8):e615-e626. An R, Shen J, Ying B, Tainio M, Andersen ZJ, de Nazelle A: Impact of ambient air pollution on physical activity and sedentary behavior in China: A systematic review. Environ Res 2019, 176:108545. Sinharay R, Gong J, Barratt B, Ohman-Strickland P, Ernst S, Kelly FJ, Zhang JJ, Collins P, Cullinan P, Chung KF: Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. Lancet 2018, 391(10118):339-349. Kocot K, Zejda JE: Acute cardiorespiratory response to ambient air pollution exposure during short-term physical exercise in young males. Environ Res 2021, 195:110746. Matt F, Cole-Hunter T, Donaire-Gonzalez D, Kubesch N, Martinez D, Carrasco-Turigas G, Nieuwenhuijsen M: Acute respiratory response to traffic-related air pollution during physical activity performance. Environ Int 2016, 97:45-55. Wagner DR, Brandley DC: Exercise in Thermal Inversions: PM(2.5) Air Pollution Effects on Pulmonary Function and Aerobic Performance. Wilderness Environ Med 2020, 31(1):16-22. Giles LV, Carlsten C, Koehle MS: The pulmonary and autonomic effects of high-intensity and low-intensity exercise in diesel exhaust. Environ Health 2018, 17(1):87. Park HY, Gilbreath S, Barakatt E: Respiratory outcomes of ultrafine particulate matter (UFPM) as a surrogate measure of near-roadway exposures among bicyclists. Environ Health 2017, 16(1):6. Kubesch NJ, de Nazelle A, Westerdahl D, Martinez D, Carrasco-Turigas G, Bouso L, Guerra S, Nieuwenhuijsen MJ: Respiratory and inflammatory responses to short-term exposure to traffic-related air pollution with and without moderate physical activity. Occup Environ Med 2015, 72(4):284-293. Pasqua LA, Damasceno MV, Cruz R, Matsuda M, Martins MAG, Marquezini MV, Lima-Silva AE, Saldiva PHN, Bertuzzi R: Exercising in the urban center: Inflammatory and cardiovascular effects of prolonged exercise under air pollution. Chemosphere 2020, 254:126817. Gulati M, Shaw LJ, Thisted RA, Black HR, Bairey Merz CN, Arnsdorf MF: Heart rate response to exercise stress testing in asymptomatic women: the st. James women take heart project. Circulation 2010, 122(2):130-137. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P et al: Standardisation of spirometry. Eur Respir J 2005, 26(2):319-338. Kubesch N, De Nazelle A, Guerra S, Westerdahl D, Martinez D, Bouso L, Carrasco-Turigas G, Hoffmann B, Nieuwenhuijsen MJ: Arterial blood pressure responses to short-term exposure to low and high traffic-related air pollution with and without moderate physical activity. Eur J Prev Cardiol 2015, 22(5):548-557. Hudda N, Eliasziw M, Hersey SO, Reisner E, Brook RD, Zamore W, Durant JL, Brugge D: Effect of Reducing Ambient Traffic-Related Air Pollution on Blood Pressure: A Randomized Crossover Trial. Hypertension 2021, 77(3):823-832. Giorgini P, Di Giosia P, Grassi D, Rubenfire M, Brook RD, Ferri C: Air Pollution Exposure and Blood Pressure: An Updated Review of the Literature. Curr Pharm Des 2016, 22(1):28-51. Strak M, Boogaard H, Meliefste K, Oldenwening M, Zuurbier M, Brunekreef B, Hoek G: Respiratory health effects of ultrafine and fine particle exposure in cyclists. Occup Environ Med 2010, 67(2):118-124. Chen X, Liu F, Niu Z, Mao S, Tang H, Li N, Chen G, Liu S, Lu Y, Xiang H: The association between short-term exposure to ambient air pollution and fractional exhaled nitric oxide level: A systematic review and meta-analysis of panel studies. Environ Pollut 2020, 265(Pt A):114833. Anand A, Castiglia E, Zamora ML: The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review. Curr Environ Health Rep 2024. Chen R, Qiao L, Li H, Zhao Y, Zhang Y, Xu W, Wang C, Wang H, Zhao Z, Xu X et al: Fine Particulate Matter Constituents, Nitric Oxide Synthase DNA Methylation and Exhaled Nitric Oxide. Environ Sci Technol 2015, 49(19):11859-11865. Kocot K, Baranski K, Melaniuk-Wolny E, Zajusz-Zubek E, Kowalska M: Acute FeNO and Blood Pressure Responses to Air Pollution Exposure in Young Adults during Physical Activity. Int J Environ Res Public Health 2020, 17(23). Bos I, De Boever P, Vanparijs J, Pattyn N, Panis LI, Meeusen R: Subclinical effects of aerobic training in urban environment. Med Sci Sports Exerc 2013, 45(3):439-447. Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 11 Jun, 2024 Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 09 Jun, 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-4552474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312963076,"identity":"b4663189-7900-4554-94da-346a3285fc5f","order_by":0,"name":"Xingsheng Jin","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Xingsheng","middleName":"","lastName":"Jin","suffix":""},{"id":312963077,"identity":"6f0de6e8-5100-48a9-a9d3-dfd5ddc23846","order_by":1,"name":"Weiyi Wang","email":"","orcid":"","institution":"Baotou Medical 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Bingxiang","middleName":"","lastName":"Xu","suffix":""},{"id":312963081,"identity":"5ec8dbbd-6c03-430d-bafb-3784d37fcf6e","order_by":5,"name":"Haili Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZjCSkGFjZj784IOBjR1BHTxQLTxs7GxphjMK0pIJa4FYBKT5eQykeT4cYmwgpMWenfnZ44IaCx4+ZgYDYxuDA8wM7IePbsDvMDZz4xnHgA5jZkh4nGNwh4+BJy3tBgG/mEnzsIG1HDDOMXjGDPSXGQEt7N+kef6BtDA2SFsYHGZsIKyFx0yatw2khZlBmoEoLYd5yqR5+0Ba2NgMewzSktkI+YW9//g2aZ5vdXLy/ec/P/jxx8aOn/3wMbxaMAEbacpHwSgYBaNgFGADAEx4NmCRw+d7AAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":true,"prefix":"","firstName":"Haili","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-06-09 05:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4552474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4552474/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-21045-z","type":"published","date":"2024-12-19T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59283930,"identity":"d8999c93-e0e4-4f94-b547-f4072c3f46c7","added_by":"auto","created_at":"2024-06-28 16:05:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172761,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in PM\u003csub\u003e2.5\u003c/sub\u003e concentration during the exposure period.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4552474/v1/f211e7603ea09dc8c6366fc0.png"},{"id":59283929,"identity":"7152fe5f-4fed-4d7e-9f05-265e8d843ca9","added_by":"auto","created_at":"2024-06-28 16:05:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173742,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in cardiorespiratory health measurements relative to baseline.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4552474/v1/8c0e432f544ce0a6c261f387.png"},{"id":59283931,"identity":"ce3b8067-0bb5-4ba4-9e3f-3d12d6b3ef39","added_by":"auto","created_at":"2024-06-28 16:05:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282229,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in inflammatory markersrelative to baseline.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4552474/v1/f1c04f2d1264dfb663e47c9e.png"},{"id":72202762,"identity":"003200a5-2c87-41d1-a6d3-5b7218929e8d","added_by":"auto","created_at":"2024-12-23 16:16:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4552474/v1/23bf41e5-1e3d-4442-9d02-9befaaf0cf1a.pdf"},{"id":59283932,"identity":"20281df2-9706-485f-b410-c59302c94729","added_by":"auto","created_at":"2024-06-28 16:05:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17708,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-4552474/v1/d1fc6a4117486ef6b041c1f0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physical exercise attenuates the negative effects of short-term exposure to medium air pollution levels on cardio-respiratory responses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBillions of people worldwide are exposed to environments with air pollution (AP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. AP poses significant health threats to humanity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], particularly impacting cardiorespiratory health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The pollutants in the air, especially particulate matter (PM), may induce systemic chronic inflammatory responses and oxidative stress in the body, which could be mechanistic pathways leading to adverse health outcomes in cardiorespiratory health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs is well known, regular physical exercise (PE) brings numerous benefits to physical health, such as enhancing cardiorespiratory function, reducing inflammatory responses, and improving immunity [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, an increase in PE also entails an increased risk of exposure to AP. Engaging in PE in environments with AP may lead to higher inhalation doses of pollutants due to deeper and faster breathing and increased ventilation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which could potentially exacerbate the adverse effects of AP on cardiorespiratory health. Thus, the question of whether PE in AP environments is beneficial to cardiorespiratory health has become a hot topic of investigation in recent years.\u003c/p\u003e \u003cp\u003eReviewing prior research reveals a lack of consensus due to differences in study populations, levels of pollution, research designs, and exercise protocols. Most studies have focused on middle-aged and elderly individuals or those with cardiorespiratory diseases, with fewer studies conducted on healthy young adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There are also significant variations in the levels of pollution exposure across different studies. Additionally, due to ethical reasons, there is limited research on the cardiorespiratory health effects of exercising in environments with high AP concentrations. Variations in study designs and exercise regimens (such as type, duration, and intensity of exercise) can lead to significant differences in the amount of pollutants inhaled. For example, Matt et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]conducted 2 hours of moderate-intensity intermittent physical activity (such as 15-minute intermittent cycle ergometry), while Kocot et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]only performed 15-minute submaximal exercise trials on a cycle ergometer. Therefore, there is a significant difference in the amount of pollutants inhaled by both, which can result in changes in measurement outcomes. Some studies suggest that compared to rest, PE can counteract the adverse effects of AP on cardiorespiratory health [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while others argue that continuing exercise as AP levels rise may decrease cardiorespiratory function [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and increase inflammatory responses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]in the body. Therefore, determining the safety threshold for PE in AP environments is a goal that warrants continuous exploration.\u003c/p\u003e \u003cp\u003eConsequently, in this study, we have decided to investigate the beneficial effects of PE on cardiorespiratory health in environments with three different AP concentrations. We aim to identify under which AP concentration PE may be beneficial for cardiorespiratory health, thus contributing to the existing body of research evidence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study employed a self-controlled crossover design, wherein participants engaged in 90 minutes of moderate-intensity PE under three different AP concentrations (low, medium, and high). Inclusion criteria for participants were as follows: (1) aged 18\u0026ndash;30 and enrolled university students; (2) physically healthy; (3) no medication intake within the past 3 weeks; (4) no history of pulmonary or cardiovascular diseases; (5) absence of symptoms such as cold or fever; (6) non-smokers; (7) voluntary participation and cooperation with researchers throughout the study. Exclusion criteria included: (1) cardiovascular and respiratory system diseases; (2) recent medication treatment; (3) nasal allergy sufferers; (4) inability to tolerate moderate-to-high-intensity exercise; (5) asthma, allergy, or hypersensitivity conditions; (6) female participants menstruating during the exercise period. All participants provided voluntary informed consent after agreeing to participate in the study. The study received approval from the Ethics Committee of Shanghai University of Sport (Approval No.: 102772019RT001).\u003c/p\u003e \u003cp\u003eThe experiments were conducted between September 2023 and December 2023. Participants were instructed to abstain from alcohol consumption and vigorous physical activity 24 hours before the experiment and avoid exposure to high pollution levels in the air. Additionally, coffee and soy milk intake were prohibited within 3 hours before testing. To minimize the influence of different measurement times on the results, all experiments and measurements were conducted at the same time of day, and participants were required to consume a standardized meal 30 minutes before the experiment to reduce interference from the diet. On the day of the experiment, participants arrived at the laboratory at 7:30 a.m., underwent baseline health indicator measurements after a brief rest, and then walked to the 100-meter playground for a 90-minute moderate-intensity exercise session from 9:30 a.m. to 11:00 a.m. The exercise regimen included warm-up running (5 minutes), warm-up exercises (5 minutes), aerobic exercises (40 minutes), games (30 minutes), and stretching and relaxation (10 minutes). The entire exercise intervention was led by experienced coaches.\u003c/p\u003e \u003cp\u003eAfter the 90-minute exercise intervention, participants returned to the laboratory immediately for post-intervention health indicator measurements. Blood samples were collected first, with venous blood collection completed within 15 minutes after exercise, followed by the measurement of cardiorespiratory health indicators starting 30 minutes after the exercise intervention. Each participant completed PE under three different AP environments, and between each experiment, participants underwent a washout period of at least 2 weeks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhysical exercise monitoring\u003c/h2\u003e \u003cp\u003eBefore the exercise, participants uniformly wore Polar heart rate monitors to objectively monitor their exercise intensity. The exercise intensity was controlled moderately corresponding to 70% of each participant's maximum heart rate. The maximum heart rate was calculated based on the latest international standard algorithms considering age and gender, where for males, the maximum heart rate\u0026thinsp;=\u0026thinsp;220 - age; for females, the maximum heart rate\u0026thinsp;=\u0026thinsp;206\u0026thinsp;\u0026minus;\u0026thinsp;0.88 \u0026times; age [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. During the exercise session, researchers and coaches could adjust the exercise pace in real-time based on the monitoring results to ensure that the predetermined exercise intensity was achieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental exposure monitoring\u003c/h2\u003e \u003cp\u003eThe research design involved conducting moderate-intensity exercise experiments under three different AP levels (low: PM\u003csub\u003e2.5\u003c/sub\u003e \u0026le; 75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, medium: 75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;PM\u003csub\u003e2.5\u003c/sub\u003e \u0026le;115 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and high: PM\u003csub\u003e2.5\u003c/sub\u003e \u0026gt; 115 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), based on the latest air quality guidelines from the World Health Organization (WHO) and China's Environmental Quality Standards. If the conditions were not met, the exercise intervention experiment was not conducted.\u003c/p\u003e \u003cp\u003eDuring the 90-minute exercise intervention period, the air temperature, relative humidity, and PM\u003csub\u003e2.5\u003c/sub\u003e concentration at the monitoring site were monitored. For PM\u003csub\u003e2.5\u003c/sub\u003e measurement, the SidePak\u0026trade; AM520i Personal Aerosol Monitor was used to detect changes in ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentration, with the instrument sampling every 10 seconds. Data on other air pollutants were obtained from environmental monitoring stations located 3 km from the experimental center, with the average values during the intervention period taken as the pollution levels for the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCardiorespiratory health measurements\u003c/h2\u003e \u003cp\u003eThe equipment used for heart rate and blood pressure testing was the Omron J710 Blood Pressure Monitor from Japan, which measured systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse. Three consecutive measurements were taken each time, and the average was recorded.\u003c/p\u003e \u003cp\u003eFor pulmonary function testing, in accordance with the standards of the European Respiratory Society (ERS) and the American Thoracic Society (ATS) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the Italian New Spirolab\u0026reg; Pulmonary Function Testing Instrument was employed. The measured indices included forced vital capacity (FVC), forced expiratory volume in one second (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (FEF\u003csub\u003e25\u0026minus;\u0026thinsp;75%\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eFractional exhaled nitric oxide (FeNO) was measured using the portable NIOX MINO Analyzer (Aerocrine AB, Solna, Sweden).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBlood sample collection\u003c/h2\u003e \u003cp\u003eThirty minutes before the start of the exercise and within 15 minutes after its completion, trained medical personnel collected venous blood samples from the study participants. Blood samples were drawn using Ethylenediaminetetraacetic Acid (EDTA) anticoagulant tubes for routine blood tests. Serum samples for the measurement of IL-1β, IL-10, IL-6, TNF-α, and CRP were centrifuged and aliquoted within 4 hours after blood collection and stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e for the study was obtained from the Ethics Committee of Shanghai University of Sport (Ethics approval no: 102772019RT001) and registered in the Chinese Clinical Trial Registry (Registered No: ChiCTR2000031851). Written informed consent was obtained from participants before they participated in the study.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis of the data was conducted using R 4.3.2 software. Descriptive analysis was primarily focused on the basic information of the study participants, pollutant concentrations, and fundamental characteristics of health indicators. Continuous variables were described using arithmetic means and standard deviations, while categorical variables were described using proportions. Changes in health indicators before and after exercise were expressed as relative differences ((post-exercise - baseline) / baseline), where a relative difference\u0026thinsp;\u0026lt;\u0026thinsp;0% indicated a decrease after exercise. The Shapiro-Wilk test was employed to assess the distribution of data. Paired t-tests were used to evaluate the statistical significance of differences between pre- and post-exercise measurements in each experiment. Due to the non-normal distribution of pollutant concentrations, Wilcoxon rank-sum tests were utilized to assess differences between the three different AP levels. Considering the repeated study design, linear mixed-effects models (LME) were constructed using the \u0026lsquo;lme4\u0026rsquo; package in R. These models analyzed changes relative to baseline values after exercise across the three experiments. The study participants' ID was included as a random effect in the model to account for individual variability in all health outcomes. Gender, age, and body mass index (BMI) were included as fixed effects. This study considered a two-tailed p-value \u0026lt; 0.05 to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubject characteristics\u003c/h2\u003e \u003cp\u003eA total of 30 participants were recruited, all of whom completed exposures in all three scenarios. Among them, there were 16 males (53.3%) and 14 females (46.7%). The mean age of the 30 participants was 20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 years, with an average BMI of 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 kg/m\u0026sup2;. Specific characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eCharacteristics of the study group(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSD: standard deviation; BMI: body mass index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePollution levels\u003c/h2\u003e \u003cp\u003eDuring the 90-minute exercise intervention experiments, the environmental levels of PM\u003csub\u003e2.5\u003c/sub\u003e were monitored using the SidePak\u0026trade; AM520i Individual Aerosol Monitor, showing variations over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e were 35.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, 95.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and 174.26\u0026thinsp;\u0026plusmn;\u0026thinsp;17.86 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. Detailed characteristics of the environmental conditions recorded during the three experiments are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant differences were observed in the levels of PM\u003csub\u003e2.5\u003c/sub\u003e, inhalable particulate matter PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e during the three intervention periods. Specifically, the AP levels at the medium level were significantly higher than those at the low level, while the AP levels at the high level were significantly higher than those at the medium level. The pollution concentration levels in the experiment align with expectations.\u003c/p\u003e \u003cp\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\u003eDistribution of environmental conditions recorded during exercise trials.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e44.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0003\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\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e147.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e187.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0019\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\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e35.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e95.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e174.26\u0026thinsp;\u0026plusmn;\u0026thinsp;17.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature(℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e35.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. *: significant differences between the three experimental conditions (Wilcoxon test results).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCardiorespiratory health\u003c/h2\u003e \u003cp\u003eIn this study, measurements of cardiorespiratory function and airway inflammation indicators were taken before and after each of the three exercise interventions. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the average differences in cardiorespiratory health measurements before and after exercise at three different levels of AP.\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\u003eMean differences in baseline and post-exercise cardiorespiratory health measurements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"\u0026plusmn;\" 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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1 (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEF (L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e (L/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll data are presented as mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. *: significant differences between baseline and post-exercise (paired t-test results). \u003cb\u003eAbbreviations\u003c/b\u003e: SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s; PEF, Peak expiratory flow; FEF\u003csub\u003e25\u0026ndash;75%\u003c/sub\u003e, mean forced expiratory flow between 25% and 75% of FVC; FeNO, fractionated exhaled nitric oxide.\u003c/p\u003e \u003cp\u003eThe results indicate that SBP significantly decreased after exercise in the low pollution level environment (-2.2 mm/Hg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.047), while the mean decreased in the medium and high pollution level environments after exercise, although without significant differences. Similarly, DBP decreased after exercise in the low and medium pollution level environment, with no significant differences observed, but increased significantly after exercise in the high pollution level environment (3.5 mm/Hg, p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eFor lung function, post-exercise mean values of FVC and FEV1 significantly increased in the low pollution level environment (0.2L, P\u0026thinsp;=\u0026thinsp;0.041) (0.2L, P\u0026thinsp;=\u0026thinsp;0.049), while mean values of PEF and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e also increased, albeit without significant differences. Additionally, post-exercise FVC still showed a weak significant increase in the medium pollution level environment (0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, P\u0026thinsp;=\u0026thinsp;0.05). However, post-exercise mean values of FEV1 significantly decreased in the high-level AP environment (-0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, P\u0026thinsp;=\u0026thinsp;0.031), with other lung function indicators also showing decreases in the high pollution level environment, but without significant differences.\u003c/p\u003e \u003cp\u003eFor airway inflammation indicators, we found that FeNO significantly decreased after exercise in both the low pollution level (-3 ppb, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and medium pollution level (-1.7 ppb, p\u0026thinsp;=\u0026thinsp;0.038) environments, while it increased in the high pollution level environment, although without significant differences.\u003c/p\u003e \u003cp\u003eBased on the observed changes in cardiorespiratory function indicators pre- and post-exercise, it is evident that exercising in medium-level AP environments may not lead to as beneficial significant changes in cardiorespiratory function indicators as seen with exercise in low pollution environments. Conversely, exercising in high pollution environments yields adverse effects. This suggests that AP may diminish the benefits of PE on cardiorespiratory function.\u003c/p\u003e \u003cp\u003eAdditionally, we calculated the percentage change of cardiorespiratory-related health indicators relative to the baseline to adjust for individual differences at the baseline level and analyzed the changes in cardiorespiratory health indicators among three different levels of AP. The specific changes are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We observed that after exercise in environments with three different AP concentrations, blood pressure decreased in all three concentration environments, with SBP decreasing below 0, indicating a decrease after exercise. DBP decreased after exercise in environments with medium to low pollution concentrations, while it increased in environments with high pollution concentrations. Analysis of lung function indicators revealed that FVC, FEV1, and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e increased after exercise in environments with medium pollution concentrations, while they decreased after exercise in environments with high pollution concentrations. Additionally, the percentage changes in lung function-related indicators after exercise in environments with low, medium, and high concentrations of pollution all showed a decreasing trend. Analysis of the airway inflammation indicator FeNO revealed a decrease after exercise in environments with low to medium pollution concentrations, while it increased after exercise in environments with high pollution concentrations. Moreover, after exposure to environments with three different concentrations of pollution, the percentage change in FeNO values showed an increasing trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Subsequently, we utilized LME to adjust for participants' gender, age, and BMI. Using the percentage change in cardiorespiratory health indicators after exercising in the low pollution level environment as the reference, we further confirmed the aforementioned results. The specific results were consistent with the description provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e above, and the analytical outcomes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in cardiorespiratory health among three different AP levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eFEV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-13.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-15.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePEF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eFEF\u003c/b\u003e\u003csub\u003e\u003cb\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-16.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-16.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFeNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMixed effect models adjusted for gender, age, and BMI. Exposure scenario with reference to \u0026lsquo;Low level AP and PE exposure\u0026rsquo;. \u003csup\u003e*\u003c/sup\u003e: Statistical significance in the results of the linear mixed-effects model. M: Medium level AP and PE exposure; H: High level AP and PE exposure.\u003c/p\u003e \u003cp\u003eThe LME analysis revealed that the changes in SBP at medium and high levels of AP environment both showed no significant difference compared to the low concentration. However, DBP showed a significant increase in the high concentration of AP compared to the low level (6.45, P\u0026thinsp;=\u0026thinsp;0.05), with no significant difference observed in relative changes between medium and low levels.\u003c/p\u003e \u003cp\u003eRegarding lung function, compared to the changes after exercise in environments with low pollution concentrations, PEF significantly decreased after exercise in environments with medium (-9.36, P\u0026thinsp;=\u0026thinsp;0.018) and high (-9.50, P\u0026thinsp;=\u0026thinsp;0.013) pollution concentrations. FVC and FEV1 showed no significant differences after exercise in medium concentrations but significantly decreased after exercise in high concentrations (-6.84, P\u0026thinsp;=\u0026thinsp;0.04) (-8.97, P\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eRegarding the airway inflammation indicator FeNO, compared to the changes after exercise in environments with low pollution concentrations, there was no significant difference after exercise in environments with medium concentrations, but FeNO significantly increased after exercise in environments with high concentrations (13.3, P\u0026thinsp;=\u0026thinsp;0. 001).\u003c/p\u003e \u003cp\u003eThe results further demonstrate that changes in cardiorespiratory health indicators post-exercise in high-level AP environments are significantly decreased compared to low-level AP environments. Conversely, in low- and medium-level AP environments, differences in cardiorespiratory health indicator changes are not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects on circulating inflammation markers\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the percentage change relative to baseline in inflammatory markers following exercise across three different levels of AP. Overall, the change in inflammatory markers after exercise in environments with high pollution concentrations was notably higher than those in medium and low concentrations, while the changes between medium and low concentrations were small. Moreover, the majority of inflammatory markers showed an increase after exercise regardless of the AP concentration, except monocytes and eosinophils, which exhibited a slight decrease in change after exercise in environments with medium and low concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in inflammatory marker changes among three different levels of AP\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLymphocytes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMonocytes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEosinophils\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eBasophils\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIL-1β\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eIL-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMixed effect models adjusted for gender, age, and BMI. Exposure scenario with reference to \u0026lsquo;Low level AP and PE exposure\u0026rsquo;. \u003csup\u003e*\u003c/sup\u003e: Statistical significance in the results of the linear mixed-effects model. M: Medium level AP and PE exposure; H: High level AP and PE exposure. \u003cb\u003eAbbreviations\u003c/b\u003e: IL, interleukin; TNF-α, tumour necrosis factor α; CRP, C reactive protein.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of LME analysis comparing the differences in percentage change of inflammatory markers between medium and high levels of AP with reference to the percentage change at the low pollution level. The results indicate that compared to the changes observed after exercise in the low pollution environment, there was a significant increase in the levels of white blood cells (27.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), neutrophils (26.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymphocytes (32.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), monocytes (28.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), eosinophils (48.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL-1β (0.76, P\u0026thinsp;=\u0026thinsp;0.003), IL-10 (0.17, P\u0026thinsp;=\u0026thinsp;0.02), IL-6 (0.1, P\u0026thinsp;=\u0026thinsp;0.17), TNF-α (0.97, P\u0026thinsp;=\u0026thinsp;0.011), and CRP(0.17, P\u0026thinsp;=\u0026thinsp;0.003) after exercise in the high pollution environment. Additionally, only eosinophils exhibited a significant increase (25.6, p\u0026thinsp;=\u0026thinsp;0.022) in the medium-pollution environment, while the percentage change in other inflammatory cells after exercise in the medium-pollution environment did not significantly differ from that in the low pollution environment. The results above suggest that high levels of AP significantly increase inflammation in the body, while the difference in inflammation levels between medium and low pollution levels is smaller.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducted a self-controlled crossover design to assess the effects of moderate-intensity physical exercise on the cardiorespiratory health of healthy young adults in environments with low, medium, and high AP concentrations. Our findings indicate that PE in medium and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePollution Levels\u003c/h2\u003e \u003cp\u003eReviewing previous studies, it is evident that different studies have varied definitions for low and high concentration ranges. Some studies classify pollution levels as high, while other studies might consider them low, potentially contributing to inconsistent research conclusions. Our study clearly distinguishes between low, medium, and high levels of AP concentrations. For instance, in the study by Kocot et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], a threshold of PM\u003csub\u003e10\u003c/sub\u003e at 50 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e was used to differentiate between good and poor air quality, categorizing pollution levels as poor that aligns with our high pollution exposure levels. However, our PM\u003csub\u003e2.5\u003c/sub\u003e exposure concentrations are higher than those reported by Kocot et al. Additionally, in our study, medium pollution exposure levels are comparable to high pollution exposure levels in other experimental studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEffects of combined exercise and AP exposure on cardiorespiratory function\u003c/h2\u003e \u003cp\u003eOur experimental findings indicate that exercise can reduce blood pressure, which is consistent with previous research results. Moreover, compared to exposure at medium to high concentrations, exercising at lower concentrations significantly lowers SBP. Similarly, in a crossover trial conducted in Barcelona, Spain, Kubesch et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found that intermittent PA was associated with lower SBP compared to resting, particularly following exposure to lower traffic-related air pollution (TRAP). Additionally, Kubesch et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrated that exposure to higher TRAP was associated with higher DBP compared to lower TRAP. Krzysztof et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] conducted a crossover study on healthy adult males, which also showed significant differences in the relative changes of DBP between pollution exposure experiments and control experiments, with a greater increase during pollution exposure experiments. This finding aligns with ours, where we observed a slight but significant increase in DBP following exercise exposure to higher pollution concentrations compared to exposure at lower levels. This suggests that even though exercise can regulate blood pressure, exercising in environments with higher pollution levels may still increase DBP. This could be attributed to the increased concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, which may weaken the blood pressure-lowering effect of exercise. Evidence suggests that inhaling PM may trigger acute autonomic imbalance, leading to acute endothelial/vascular dysfunction, favoring vasoconstriction and a sharp decline in aortic compliance, as well as increased bioactivity of endothelin or renin-angiotensin-aldosterone system activation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These factors, individually or collectively, may contribute to an elevation in blood pressure within hours of exposure to air particles. Therefore, elevated blood pressure may be a biomarker of adverse pathways leading to increased cardiovascular risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding changes in lung function, Kubesch et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] conducted a study involving 28 healthy adults, and their findings regarding low levels of AP concentration align with ours. They found that following PA during periods of low TRAP exposure, there was a significant increase in FEV1 and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e. They also demonstrated that PA was associated with increases in FEV1, FVC, and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e compared to rest, and even exercise in high TRAP environments had beneficial effects on lung function. In our study, we observed a significant increase in mean FVC and FEV1 following exercise in low-concentration environments, with PEF and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e showing an increase as well, albeit not statistically significant. Following exercise in medium-concentration environments, mean FVC still showed a slight but significant increase. This suggests that PA remains beneficial for lung function in environments with medium to low AP concentrations. Similarly, in a crossover study by Matt et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] involving 30 healthy adults, immediate post-exercise comparisons with baseline showed significant increases in FEV1 (48.5 mL, p\u0026thinsp;=\u0026thinsp;0.02), FEV1/FVC (0.64%, p\u0026thinsp;=\u0026thinsp;0.01), and FEF\u003csub\u003e25\u0026thinsp;\u0026minus;\u0026thinsp;75%\u003c/sub\u003e (97.8 mL, p\u0026thinsp;=\u0026thinsp;0.02). However, in our study, the magnitude of respiratory responses was small, and these responses were observed only in healthy young adults.\u003c/p\u003e \u003cp\u003eFurthermore, in the LME, we found that compared to exercise in low-concentration environments, there was a significant decrease in FVC, FEV1 during exercise in high-concentration environments, and PEF during exercise in medium to high-concentration environments. Additionally, the differences in relative changes in other lung function indicators gradually increased and showed a decreasing trend. This suggests that although exercise improves lung function, the benefits diminish with increasing pollutant concentrations. Kocot et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] conducted a crossover experiment involving 15 minutes of submaximal exercise in healthy young adult males under conditions of poor and good air quality. The pollutant concentrations in their exposure group were similar to our medium-concentration pollutant levels. They compared the relative changes between the exposure and control groups and found no differences in FVC, FEV1, and FEV1/FVC after exercise, which is consistent with our findings. Unlike Kocot et al., we also compared the changes in cardiorespiratory health indicators after exercise in high and low AP concentration environments, finding significant decreases in FVC, FEV1, and PEF. Kocot et al. concluded that acute respiratory changes following exercise under exposure conditions depend on pollutant concentrations, with only participants exposed to particularly high levels showing acute decreases in FEV1/FVC post-exercise, and the relative changes in FEV1/FVC were significantly negatively correlated with pollutant concentrations. Strak et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] investigated the effects of AP on the respiratory health of healthy cyclists and found a slight increase in lung function immediately after cycling, but a negative correlation with AP emerged six hours after cycling. Matt et al.'s study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] also indicated that PA mitigates the negative effects of PM on the upper and lower respiratory tracts, with substantial evidence of interaction between PM and physical activity's respiratory effects. They suggested that increased lung ventilation during physical activity may lead to a higher proportion of particle deposition in the nasal pharyngeal region by collision, thereby affecting PM deposition and preventing interference with the impact of physical activity on the upper and lower airways [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eChanges in inflammation after combined exposure to exercise and AP.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that higher levels of AP, particularly PM, can induce the production of nitric oxide by epithelial cells, leading to a significant increase in FeNO levels [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], resulting in local inflammatory responses. Furthermore, research has indicated a correlation between changes in FeNO levels after exercise and the concentration of air pollutants. For instance, Kubesch et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] observed a significant association between coarse particulate matter and increased FeNO levels. Additionally, they noted a modest increase in FeNO levels after PA compared to rest. In contrast to their findings, our study reveals a significant decrease in mean FeNO levels after exercise in environments with medium to low AP, while in high AP environments, mean FeNO levels increase but without significant differences. This difference may be attributed to variations in study design; our study only collected FeNO levels 30 minutes post-exercise and did not gather information on longer-term reactions following combined exercise and AP exposure. Inflammatory responses following AP exposure may require more time, as PA has been shown to increase nitric oxide production through epigenetic changes, with the association between PM\u003csub\u003e2.5\u003c/sub\u003e and FeNO being most significant with a one-day lag time [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Kocot et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] conducted exercise sessions with 76 healthy university students under conditions of high AP and good air quality, finding that increased FeNO levels were associated with higher levels of PA and higher concentrations of air pollutants. Moreover, the statistical significance of the difference in FeNO levels between pollution exposure and control experiments was observed 15 minutes after exercise cessation, rather than immediately post-exercise. Hence, the duration of the inflammatory response is also a factor to consider in this study. Additionally, Bos et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] found that FeNO levels increased after training in urban environments, while aerobic training in rural environments did not affect FeNO levels. Consistent with their findings, the linear mixed-effects analysis in our study also suggests a significant increase in FeNO levels after exercise in high AP environments relative to low concentrations. This indicates that engaging in PA in highly polluted air environments may increase respiratory tract inflammation levels, triggering local inflammatory responses.\u003c/p\u003e \u003cp\u003eThere is limited research on the systemic inflammatory response to combined exposure to AP and PA. However, previous reports have indicated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] that engaging in PA in environments with AP can increase the count of inflammatory cells, leading to a systemic inflammatory response. Acute PA increases the number of inflammatory cells in the body, and exercise also increases the dose of inhaled particles, resulting in an increase in systemic inflammatory biomarkers. The combined effect of these factors leads to an increase in systemic inflammatory markers in the body. Kubesch et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found that compared to no exercise in low TRAP conditions, there was a significant increase in white blood cells after exercise during high TRAP exposure. They also observed associations between PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e with increased white blood cells and between coarse PM and increased neutrophils. Bos et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] investigated the changes in inflammatory markers following moderate exercise conducted on a bicycle ergometer in urban and rural environments. They found that after training in the urban setting, there was an increase in white blood cells and neutrophils, whereas no change was observed in the rural group. Consistent with our findings, acute exercise led to an increase in most inflammatory cells regardless of the AP concentration, with a more pronounced increase observed after exercise in environments with higher concentrations. Furthermore, a comparison of the percentage change in inflammatory cells between medium and high levels relative to low levels using LME also revealed a significant increase in white blood cells, neutrophils, lymphocytes, monocytes, eosinophils and other inflammatory factors at high levels, with a greater magnitude of change. In contrast, only eosinophils showed significant changes at medium levels, with no significant differences observed in other inflammatory cell changes. This suggests that exercise in environments with medium pollution levels leads to relatively small changes in inflammatory response compared to low levels, while exercise in environments with higher AP concentrations substantially increases the systemic inflammatory response, adversely affecting the body.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has several limitations. Firstly, the sample size is small, and the study population consists of healthy young adults, thus the findings may not be generalizable to other populations. Secondly, we only focused on short-term exercise and acute responses. Longer duration exercises or repeated measurements over longer periods post-exercise could provide more substantial insights. Additionally, during low-level exposure, participants were exposed to unfiltered air. Therefore, we primarily compared the effects of pollution at three different concentration levels rather than comparing AP to clean air. Lastly, due to the extended duration of the experiments, variations in environmental temperature and relative humidity may have introduced some confounding effects into the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePE in medium- (PM\u003csub\u003e2.5\u003c/sub\u003e\u0026le;115\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank M. Min He for the English language editorial services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXingsheng Jin: Writing \u0026ndash; original draft, Writing - review \u0026amp; editing. Weiyi Wang: Writing - review \u0026amp; editing. Qian Sun: Writing - review \u0026amp; editing. Yang Chen: Writing - review \u0026amp; editing. Bingxiang Xu: Supervision, Funding acquisition, Writing - review \u0026amp; editing. Haili Tian: Supervision, Funding acquisition, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Humanities and Social Sciences Youth Fund of the Ministry of Education (grant number 21YJC890030), the Shanghai Natural Science Foundation (grant number 23ZR1403700), the National Natural Science Foundation of China (grant number 32200515), and the Open Research Fund of the National Key Laboratory of Genetic Engineering (grant number SKLGE-2315).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for the study was obtained from the Ethics Committee of Shanghai University of Sport (Ethics approval no: 102772019RT001) and registered in the Chinese Clinical Trial Registry (Registered No: ChiCTR2000031851). Written informed consent was obtained from participants before they participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eManisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E: Environmental and Health Impacts of Air Pollution: A Review. Front Public Health 2020, 8:14.\u003c/li\u003e\n\u003cli\u003eHolgate S: Air pollution is a public health emergency. BMJ 2022, 378:o1664.\u003c/li\u003e\n\u003cli\u003eSchraufnagel DE, Balmes JR, De Matteis S, Hoffman B, Kim WJ, Perez-Padilla R, Rice M, Sood A, Vanker A, Wuebbles DJ: Health Benefits of Air Pollution Reduction. Ann Am Thorac Soc 2019, 16(12):1478-1487.\u003c/li\u003e\n\u003cli\u003eMiller MR: The cardiovascular effects of air pollution: Prevention and reversal by pharmacological agents. Pharmacol Ther 2022, 232:107996.\u003c/li\u003e\n\u003cli\u003eLee KK, Bing R, Kiang J, Bashir S, Spath N, Stelzle D, Mortimer K, Bularga A, Doudesis D, Joshi SS et al: Adverse health effects associated with household air pollution: a systematic review, meta-analysis, and burden estimation study. Lancet Glob Health 2020, 8(11):e1427-e1434.\u003c/li\u003e\n\u003cli\u003eHahad O, Kuntic M, Frenis K, Chowdhury S, Lelieveld J, Lieb K, Daiber A, Munzel T: Physical Activity in Polluted Air-Net Benefit or Harm to Cardiovascular Health? A Comprehensive Review. Antioxidants (Basel) 2021, 10(11).\u003c/li\u003e\n\u003cli\u003eRuegsegger GN, Booth FW: Health Benefits of Exercise. Cold Spring Harb Perspect Med 2018, 8(7).\u003c/li\u003e\n\u003cli\u003eChastin SFM, Abaraogu U, Bourgois JG, Dall PM, Darnborough J, Duncan E, Dumortier J, Pavon DJ, McParland J, Roberts NJ et al: Effects of Regular Physical Activity on the Immune System, Vaccination and Risk of Community-Acquired Infectious Disease in the General Population: Systematic Review and Meta-Analysis. Sports Med 2021, 51(8):1673-1686.\u003c/li\u003e\n\u003cli\u003eFerguson T, Olds T, Curtis R, Blake H, Crozier AJ, Dankiw K, Dumuid D, Kasai D, O\u0026apos;Connor E, Virgara R et al: Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digit Health 2022, 4(8):e615-e626.\u003c/li\u003e\n\u003cli\u003eAn R, Shen J, Ying B, Tainio M, Andersen ZJ, de Nazelle A: Impact of ambient air pollution on physical activity and sedentary behavior in China: A systematic review. Environ Res 2019, 176:108545.\u003c/li\u003e\n\u003cli\u003eSinharay R, Gong J, Barratt B, Ohman-Strickland P, Ernst S, Kelly FJ, Zhang JJ, Collins P, Cullinan P, Chung KF: Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. Lancet 2018, 391(10118):339-349.\u003c/li\u003e\n\u003cli\u003eKocot K, Zejda JE: Acute cardiorespiratory response to ambient air pollution exposure during short-term physical exercise in young males. Environ Res 2021, 195:110746.\u003c/li\u003e\n\u003cli\u003eMatt F, Cole-Hunter T, Donaire-Gonzalez D, Kubesch N, Martinez D, Carrasco-Turigas G, Nieuwenhuijsen M: Acute respiratory response to traffic-related air pollution during physical activity performance. Environ Int 2016, 97:45-55.\u003c/li\u003e\n\u003cli\u003eWagner DR, Brandley DC: Exercise in Thermal Inversions: PM(2.5) Air Pollution Effects on Pulmonary Function and Aerobic Performance. Wilderness Environ Med 2020, 31(1):16-22.\u003c/li\u003e\n\u003cli\u003eGiles LV, Carlsten C, Koehle MS: The pulmonary and autonomic effects of high-intensity and low-intensity exercise in diesel exhaust. Environ Health 2018, 17(1):87.\u003c/li\u003e\n\u003cli\u003ePark HY, Gilbreath S, Barakatt E: Respiratory outcomes of ultrafine particulate matter (UFPM) as a surrogate measure of near-roadway exposures among bicyclists. Environ Health 2017, 16(1):6.\u003c/li\u003e\n\u003cli\u003eKubesch NJ, de Nazelle A, Westerdahl D, Martinez D, Carrasco-Turigas G, Bouso L, Guerra S, Nieuwenhuijsen MJ: Respiratory and inflammatory responses to short-term exposure to traffic-related air pollution with and without moderate physical activity. Occup Environ Med 2015, 72(4):284-293.\u003c/li\u003e\n\u003cli\u003ePasqua LA, Damasceno MV, Cruz R, Matsuda M, Martins MAG, Marquezini MV, Lima-Silva AE, Saldiva PHN, Bertuzzi R: Exercising in the urban center: Inflammatory and cardiovascular effects of prolonged exercise under air pollution. Chemosphere 2020, 254:126817.\u003c/li\u003e\n\u003cli\u003eGulati M, Shaw LJ, Thisted RA, Black HR, Bairey Merz CN, Arnsdorf MF: Heart rate response to exercise stress testing in asymptomatic women: the st. James women take heart project. Circulation 2010, 122(2):130-137.\u003c/li\u003e\n\u003cli\u003eMiller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P et al: Standardisation of spirometry. Eur Respir J 2005, 26(2):319-338.\u003c/li\u003e\n\u003cli\u003eKubesch N, De Nazelle A, Guerra S, Westerdahl D, Martinez D, Bouso L, Carrasco-Turigas G, Hoffmann B, Nieuwenhuijsen MJ: Arterial blood pressure responses to short-term exposure to low and high traffic-related air pollution with and without moderate physical activity. Eur J Prev Cardiol 2015, 22(5):548-557.\u003c/li\u003e\n\u003cli\u003eHudda N, Eliasziw M, Hersey SO, Reisner E, Brook RD, Zamore W, Durant JL, Brugge D: Effect of Reducing Ambient Traffic-Related Air Pollution on Blood Pressure: A Randomized Crossover Trial. Hypertension 2021, 77(3):823-832.\u003c/li\u003e\n\u003cli\u003eGiorgini P, Di Giosia P, Grassi D, Rubenfire M, Brook RD, Ferri C: Air Pollution Exposure and Blood Pressure: An Updated Review of the Literature. Curr Pharm Des 2016, 22(1):28-51.\u003c/li\u003e\n\u003cli\u003eStrak M, Boogaard H, Meliefste K, Oldenwening M, Zuurbier M, Brunekreef B, Hoek G: Respiratory health effects of ultrafine and fine particle exposure in cyclists. Occup Environ Med 2010, 67(2):118-124.\u003c/li\u003e\n\u003cli\u003eChen X, Liu F, Niu Z, Mao S, Tang H, Li N, Chen G, Liu S, Lu Y, Xiang H: The association between short-term exposure to ambient air pollution and fractional exhaled nitric oxide level: A systematic review and meta-analysis of panel studies. Environ Pollut 2020, 265(Pt A):114833.\u003c/li\u003e\n\u003cli\u003eAnand A, Castiglia E, Zamora ML: The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review. Curr Environ Health Rep 2024.\u003c/li\u003e\n\u003cli\u003eChen R, Qiao L, Li H, Zhao Y, Zhang Y, Xu W, Wang C, Wang H, Zhao Z, Xu X et al: Fine Particulate Matter Constituents, Nitric Oxide Synthase DNA Methylation and Exhaled Nitric Oxide. Environ Sci Technol 2015, 49(19):11859-11865.\u003c/li\u003e\n\u003cli\u003eKocot K, Baranski K, Melaniuk-Wolny E, Zajusz-Zubek E, Kowalska M: Acute FeNO and Blood Pressure Responses to Air Pollution Exposure in Young Adults during Physical Activity. Int J Environ Res Public Health 2020, 17(23).\u003c/li\u003e\n\u003cli\u003eBos I, De Boever P, Vanparijs J, Pattyn N, Panis LI, Meeusen R: Subclinical effects of aerobic training in urban environment. Med Sci Sports Exerc 2013, 45(3):439-447.\u003c/li\u003e\n\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, PM2.5, Physical exercise, Cardiorespiratory health, Inflammatory response","lastPublishedDoi":"10.21203/rs.3.rs-4552474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4552474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAir pollution (AP) has become a substantial environmental issue affecting human cardiorespiratory health. Physical exercise (PE) is widely accepted to promote cardiorespiratory health. There is a paucity of research on the point at which the level of polluted environment engaged in PE could be used as a preventive approach to compensate for the damages of AP.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo determine the effects of PE on cardio-respiratory and inflammatory responses in different levels of short-term exposure to AP among healthy young adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe constructed a real-world crossover study of 30 healthy young adults with repeated measures. Participants participated in 90 min of moderate-intensity PE in different (low, medium, high) AP exposure scenarios. Cardiorespiratory measurements and blood samples were collected before and after the intervention. The percentage changes in cardiorespiratory health markers after exercise in the three AP levels environments were compared using linear mixed-effects models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the changes observed post-exercise in the low-level AP environment, only PEF (-9.36, P\u0026thinsp;=\u0026thinsp;0.018) showed a significant decrease, and eosinophils showed a significant increase in the medium-level environment (25.64, P\u0026thinsp;=\u0026thinsp;0.022), with no significant differences in other indicators. Conversely, post-exercise in the high-level AP environment resulted in a significant increase in DBP (6.5, P\u0026thinsp;=\u0026thinsp;0.05), lung inflammation (FeNO: 13.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), inflammatory cell counts (WBC: 27.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; neutrophils: 26.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; lymphocytes: 32.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; monocytes: 28.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and eosinophils: 48.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and inflammatory factors (IL-1β: 0.76, P\u0026thinsp;=\u0026thinsp;0.003; IL-10: 0.17, P\u0026thinsp;=\u0026thinsp;0.02; IL-6: 0.1, P\u0026thinsp;=\u0026thinsp;0.17; TNF-α: 0.97, P\u0026thinsp;=\u0026thinsp;0.011; CRP: 0.17, P\u0026thinsp;=\u0026thinsp;0.003). Additionally, there were significant declines in lung function parameters, including FVC (-6.84, P\u0026thinsp;=\u0026thinsp;0.04), FEV1 (-8.97, P\u0026thinsp;=\u0026thinsp;0.009), and PEF (-9.50, P\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePE in medium and low-level AP environments seems relatively safe for cardiorespiratory health among healthy young adults. However, PE in high-level AP environments can be detrimental to cardiorespiratory health, significantly increasing the body's inflammatory response.\u003c/p\u003e","manuscriptTitle":"Physical exercise attenuates the negative effects of short-term exposure to medium air pollution levels on cardio-respiratory responses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-28 16:05:09","doi":"10.21203/rs.3.rs-4552474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-11T06:08:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-10T04:18:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T04:17:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-06-09T05:00:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4652913f-46cd-4921-b374-5728ce1f3886","owner":[],"postedDate":"June 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:10:40+00:00","versionOfRecord":{"articleIdentity":"rs-4552474","link":"https://doi.org/10.1186/s12889-024-21045-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-19 15:57:36","publishedOnDateReadable":"December 19th, 2024"},"versionCreatedAt":"2024-06-28 16:05:09","video":"","vorDoi":"10.1186/s12889-024-21045-z","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21045-z","workflowStages":[]},"version":"v1","identity":"rs-4552474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4552474","identity":"rs-4552474","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}