Exploring the association between pulmonary function and air pollution exposure in healthy children in Jinan, Shandong Province: based on a cross-sectional study

Introduction

Air pollutants pose a serious threat to human health. Among them, particulate matter with an aerodynamic diameter ≤2.5 µm (PM_2.5) is of particular concern because it can penetrate deeper into the human respiratory tract and alveoli, and even further into the bloodstream, seriously damaging the respiratory system (1). Particulate matter with an aerodynamic diameter ≤10 µm (PM₁₀) can also enter the respiratory tract below the larynx, posing a potential hazard to human health (2). Long-term exposure to these air pollutants, especially PM₁₀, has been shown to be strongly associated with decreased lung function in non-asthmatic schoolchildren (3,4). Notably, even short-term exposure to high concentrations of nitrogen dioxide (NO₂) is directly associated with acute decrease in lung function and is strongly linked to a significantly elevated risk of pneumonia in children (5,6).

Children are more sensitive to air pollutants as their immune and respiratory systems are not fully developed; they are also likely to inhale more air pollutants than adults during physical exercise (7-9). Due to the lower height of children, they are closer to the ground and are therefore more likely to inhale air containing higher concentrations of pollutants (10). Lung function is an important objective indicator of the health of the human respiratory system. Some studies have shown (11-13) that short-term exposure to air pollutants is associated with decreased lung function in children. Therefore, an in-depth investigation of the relationship between pollutants and children’s lung function may help to improve our understanding of the threats of air pollution to children’s respiratory health, which may provide a basis for the development of effective measures to protect children’s respiratory health.

Although prior studies have demonstrated an association between elevated pollutant concentrations over short periods of time and decreased lung function in children, these studies focused on children with medical conditions (14-16), which limits the applicability of the findings to the healthy children. And these studies also focused on a single indicator or a limited number of indicators of lung function, such as forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁) and peak expiratory flow (PEF) (17-19), whereas in reality lung function tests cover a much wider range of indicators, for example, also including forced expiratory flow at 25% (FEF₂₅) and forced expiratory flow at 75% (FEF₇₅). In addition, most studies have focused primarily on the effects of a single pollutant (20-22), with only a few studies focusing on the joint effects of multiple pollutants (23).

In view of this, we sought to explore the short-term and lagged effects of air pollutants on healthy children using five key lung function indices (FVC, PEF, FEV₁, FEF₂₅ and FEF₇₅), and in addition to constructing a multi-pollutant model to investigate the joint effects of pollutants in order to gain a more comprehensive understanding of their potential impact on children’s health. We hope that this study will provide practical significance and scientific basis for the formulation of more effective policies to protect children’s respiratory health. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-24-438/rc).

Methods

Study area

The present study was based in Jinan City, one of the most heavily air-polluted cities in China. Jinan is an inland city in northern China and the capital of Shandong Province; it covers a total of 8,177.21 km² and is divided into ten districts. It is located at 36.40°N latitude and 110.00°E longitude. As a northern inland city, the low temperatures in winter make it difficult for pollutants to disperse, and the burning of coal and other fossil fuels may also add to the air pollution (23). This city is also one of the important transportation hubs in east China, with its primary energy source dominated by coal. The climate there is a mid-latitude monsoon climate.

Study design and participants

This study was a longitudinal cross-sectional study. In this study, stratified random sampling was used to stratify the school’s students in grades 3 through 5 by grade level, with each grade level serving as a separate stratum. In December of each year between 2016 and 2020, one class from each grade level stratum was randomly selected for testing. This random sampling strategy ensured that each class in each grade level had the same probability of being selected, effectively reducing the impact of selection bias on the study results. Before performing the lung function tests, we first interviewed the parents of the students to rule out any children with underlying respiratory conditions. Second, we measured the body indices, including height (cm) and weight (kg), of all study participants who participated in the test, and recorded their sex and date of birth in detail. Finally, a professional technician verifies basic information about each subject and performs brief health screenings on all participating children to ensure that they do not have respiratory illnesses or symptoms on the day of the test. These screenings include asking children if they have symptoms such as fever, cough or shortness of breath, and if they have recently sought medical attention.

To ensure the reliability of the findings and the quality of the survey, the main content and precautions of the survey were explained to students, their guardians and school leaders before the survey was conducted, and parents were assured that the information they provided would be kept strictly confidential and would not be used for any other purposes. The survey was entirely voluntary and informed consent was obtained from all participants and their primary guardians. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Shandong Second Medical University (No. 2021SDL188) and informed consent was taken from all the participants’ guardians.

Measurement of pulmonary function

All pulmonary function tests are performed by trained technicians in accordance with the 2005 Pulmonary Function Test Standards published by the American Thoracic Society (ATS) and the European Respiratory Society (ERS) (24). The spirometer was calibrated before the test. During the test, the child stood, wore a nose clip, and blew at least three times into a portable spirometer (Pony FX, Italy) with a disposable mouthpiece. If the difference between the two highest FVC and FEV₁ values was less than 150 mL (or within 5%) and the difference between the maximum and minimum PEF values was less than 0.67 L/s, the reproducibility of the test result was acceptable. For those children whose pulmonary function results did not meet this criterion, up to eight breath tests were required. Pulmonary function indices include FVC, FEV₁, PEF, FEF₇₅, FEF₂₅.

Air pollution exposure

Air quality data for Jinan during the study period were obtained from the National Urban Air Quality Real-time Distribution Platform operated by the China Environmental Monitoring Centre (https://www.cnemc.cn). The platform ensured that the data were updated daily and the nearest national control station to the study site was selected to obtain 24-hour air pollutant averages. Air pollutants include PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃-8h. Concurrent weather data, including average temperature (Temp, ℃) and relative humidity (RH, %), were sourced from the Jinan Meteorological Bureau’s official website (http://jnqx.jinan.gov.cn).

Statistical analysis

Descriptive analysis was conducted for air quality indicators, weather conditions, subject characteristics, and pulmonary function indicators. Chi-squared tests were used for sex and age distributions. Spearman’s correlation analysis assessed the relationships between pollutant concentrations, lung function indicators, and meteorological factors (temperature and humidity). Body mass index (BMI) was calculated as weight divided by height squared. Multiple linear regression models were established to evaluate the impact of elevated air pollutant concentrations on lung function, adjusting for age, sex, height, weight, temperature, and humidity. Separate subgroup analyses were also performed for male and female students. Stepwise regression was used to select the optimal model based on the lowest Akaike Information Criterion (AIC) value. The results were presented as changes in pulmonary function indicators per 10 µg/m³ increase in the concentration of different air pollutants and the corresponding 95% confidence interval (95% CI).

To explore whether there was a lagged effect of different air pollutants on the pulmonary function of children, we analyzed the lagged effect of each pollutant independently in the model at a single lag (lag0–lag5) and cumulative lags (lag05), with lag0 referring to the pollutant concentration on the day of the pulmonary function test, Lag1 referring to the pollutant concentration on the day before the pulmonary function test and so on; Lag05 referring to the cumulative mean on the day of the pulmonary function test and the previous 5 days.

We constructed a multi-pollutant model to analyze the joint effects of multiple pollutants on lung function by selecting the pollutant with statistically significant differences (NO₂) as the main model in the single-pollutant model, and then adding the other pollutants (PM_2.5, SO₂, and O₃-8 h) to NO₂, respectively. Since the correlation coefficients between NO₂ and PM₁₀ and between NO₂ and CO were very high, which may indicate a multicollinearity problem. Therefore, we only included PM_2.5, SO₂ and O₃ in the multi-pollutant model for data analysis.

Data analysis was performed using the R software (version 4.2.3). We used the “dplyr” data processing package in R to calculate the lagged effects of pollutant exposure and further derived the cumulative lagged effects by accumulating multiple lagged values. All the tests were two-tailed, and a P value of less than 0.05 was indicative of statistical significance.

Results

General information

In this study, a total of 791 children participated in the initial survey. After screening, children with respiratory diseases such as asthma, pneumonia, bronchitis or colds, and those who were unable to attend school for the test due to leave of absence, totaling 11, were excluded. Eventually, a total of 780 children were included in the study and underwent pulmonary function tests, including 412 boys and 368 girls. There were no significant differences in gender (χ²=5.449, P>0.05) and age (χ²=0.580, P>0.05) across test years (Table 1).

The general characteristics and pulmonary function indicators of the subjects and meteorological factors are shown in Table 2. The mean height of the 780 children was 141.52 cm, the mean height was 38.94 kg, and the mean BMI was 19.13 kg/m². The mean FVC was 2.03 L [standard deviation (SD) =0.44], the mean FEV₁ was 1.73 L (SD =0.38), and the mean PEF was 2.95 L/s (SD =0.93). According to the Ambient Air Quality Standards GB 3095-2012, the PM_2.5 concentration exceeded the secondary standard limits during the survey, with a mean PM_2.5 concentration of 88.83 µg/m³ (SD =51.08) (Table 2).

Correlation analysis

All the pollutants were found to be positively correlated with Temp, with NO₂ showing the greatest correlation with Temp (ρ=0.444, P<0.01) and O₃ showing the minimum correlation with Temp (ρ=0.250, P<0.01) (Table 3). All pollutants except O₃ were positively correlated with RH, with PM_2.5 having the greatest correlation with RH (ρ=0.642, P<0.01).

The associations between air pollution exposure and pulmonary function parameters are presented in Table S1. Correlation analyses showed that air pollutants were negatively correlated with lung function indices on the day of exposure (lag0), on the third day of lag (lag3), and cumulative lag exposure up to 5 days (lag05).

Association between pollutant exposure and lung function

Single-pollutant model

The single-pollutant model showed varying degrees of lagged and cumulative lagged effects of air pollutants on children’s lung function, with significant decreases in lung function FEV₁ and PEF (Figure 1, Table S2). At lag3, FEV₁ decreased by 1.05 mL (95% CI: −2.02, −0.08), 1.18 mL (95% CI: −1.94, −0.42), 4.96 mL (95% CI: −8.08, −1.84), 4.94 mL (95% CI: −7.59, −2.28) and 0.11 mL (95% CI: −0.20, −0.01) for every 10 µg/m³ increase for PM_2.5 , PM₁₀, SO₂ , NO₂ , and CO, respectively; for every 10 µg/m³ increase in PM₁₀, SO₂, NO₂, and CO, PEF decreased by 3.80 mL (95% CI: −6.51, −1.08), 16.73 mL (95% CI: −27.83, −5.63), 17 mL (95% CI: −26.44, −7.55), and 0.39 mL (95% CI: −0.72, −0.05), respectively. Furthermore, all five lung function indices were significantly decreased in children when exposed to SO₂ and NO₂ (P<0.05).

Figure 1 Association of short-term air pollution exposure with children’s lung function. Estimates are presented as coefficients with 95% CI per 10 µg/m³ increase in each pollutant. Red line indicates P<0.05, black line indicates P>0.05. Results are shown for current day exposure (lag0) to 5 days before exposure (lag-5) and for cumulative lag exposure up to 5 days (lag05). The multiple linear regression models were adjusted for age, weight, height, sex, relative humidity, and average temperature. CI, confidence interval; CO, carbon monoxide; FEF₂₅, forced expiratory flow at 25%; FEF₇₅, forced expiratory flow at 75%; FEV₁, forced expiratory volume in 1 second; FVC, forced vital capacity; NO₂, nitrogen dioxide; O₃, ozone; PEF, peak expiratory flow; PM_2.5, particulate matter with an aerodynamic diameter ≤2.5 µm; PM₁₀, particulate matter with an aerodynamic diameter ≤10 µm; SO₂, sulphur dioxide.

Multi-pollutant model

Table 4 shows the results of the multi-pollutant models. According to the principle of statistically significance in the single-pollutant model, the optimal effect day of lung function indices FVC, FEV₁, PEF, FEF₇₅, and FEF₂₅ was determined to be lag3.

The dual-pollutant model showed a significant (P<0.05) decrease in FEV₁ and PEF when NO₂ was jointed with PM_2.5, SO₂ and O₃. The multi-pollutant model showed a significant (P<0.05) decrease in PEF when particulate matter was combined with gaseous pollutants (Table 4).

After correction of the P value by the Bonferroni test (Table S3), both the two-pollutant model and the multi-pollutant model showed that the children’s lung function indices still showed significant decreases of varying magnitude (P<0.05) when irritant gases were jointed with particulate matter.

Gender analysis

The results showed (Table 5) that pollutants had inconsistent effects on lung function in boys and girls, with boys showing a significant decrease in the lung function index FVC compared to girls (P<0.05). For every 10 µg/m³ increase in SO₂ and NO₂, the boys’ FVC decreased by 8.05 and 5.63 mL, respectively.

Discussion

This study explored the relationship between short-term exposure to air pollutants and children’s lung function by measuring lung function in healthy children. The results of the study showed that children’s lung function decreased after short-term exposure to pollutants. Further studies found that the effects were not limited to the moment of exposure, but that there was a significant lag effect and a cumulative lag effect. In particular, the pulmonary function index showed a significant decrease on the lag 3 d. In order to gain a more comprehensive understanding of the effects of different pollutants on children’s lung function, we used a multi-pollutant model for our analysis. The results showed a significant decrease in FEV₁ and PEF when NO₂ was combined with PM_2.5, SO₂ and O₃. In addition, the study conducted a stratified analysis to explore changes in lung function in children of different genders in response to pollutant exposure. It was found that boys’ lung function was more sensitive to pollutant exposure.

Our results showed a statistically significant association between increasing pollutant concentrations and decreasing lung function indices in children, indicating that short-term exposure to air pollutants decreases lung function in healthy children. This finding is consistent with previous studies (25-27), all of which confirm the potential adverse effects of air pollutants on lung function in children. More importantly, we also observed a lagged and cumulative lagged effect of pollutants on lung function, a result supported by previous studies (7,14,28), confirming the persistence of the health risks posed by pollutants on children’s lung function.

Compared with previous studies, we were more comprehensive in the selection of study indicators. While previous studies focused on the core indices of FVC, FEV₁, and PEF, our study included important indices such as FEF₂₅ and FEF₇₅ in addition to FVC, FEV₁, and PEF. For example, a study of 315 schoolchildren aged 9 to 16 years in Bangladesh revealed that PEF and FEV₁ showed a decrease of −4.19% and −2.05%, respectively, for every 20 µg/m³ increase in PM_2.5 concentration (29). Another panel study of 86 children in China similarly showed that for every 10 µg/m³ increase in the 1-day moving average concentration of PM_2.5, the values of FVC, FEV₁, and PEF decreased by 23.22 mL, 18.93 mL, and 29.38 mL/s, respectively (30). In addition, a Japanese study also found a significant −3.67 L/min decrease in PEF for every 13.6 ppb increase in O₃ exposure over the interquartile range (31). The comparison of these findings further highlights the comprehensiveness of our study in terms of indicator selection. And in clinical practice (32,33), FVC is often used to assess the function of the large airway, FEV₁ is used to reflect the obstruction of large and small airways, and PEF is used to reveal airway obstruction and respiratory muscle strength. FEF₂₅ and FEF₇₅ are often regarded as proxies for peripheral small airway function. In this study, we found that all lung function indices of children decreased as the concentration of air pollutants increased; thus, it is evident that exposure to air pollutants not only affects children’s large airway function, but also has a significant negative effect on their small airway function. This finding further emphasizes the importance of good air quality in maintaining respiratory health.

The association between exposure to pollutants and decreased lung function observed in this study may be due to multiple mechanisms. It has been suggested that oxidative stress and inflammatory responses are the core mechanisms by which air pollutants adversely affect human health (34,35). Specifically, PM_2.5, due to its extremely small particle size, easily penetrates the alveoli and activates an inflammatory response (36), and even low levels of PM_2.5 may trigger airway inflammation (15), which can ultimately lead to a decreases in lung function. On the other hand, SO₂ exposure should not be ignored. Studies have shown that SO₂ can increase the expression levels of certain genes involved in inflammatory pathways, which in turn triggers oxidative damage in the lungs (37). In addition, air pollutants such as NO₂ have been shown to induce the production of reactive oxygen species (ROS), which further contributes to tissue inflammation and cell death, resulting in potential damage to lung function (38).

It is worth mentioning that the present study also revealed the existence of a lagged effect of O₃ on lung function, in particular the persistent correlation with FEF₇₅. The low water solubility of O₃, a pollutant with significant acute health effects, makes it more likely to be retained in the small airways (39); this is in line with our study, where O₃ was associated with a decrease in FEF₇₅, which represents the function of small air-ways. However, the effects of O₃ on children’s pulmonary function are not significant compared to other pollutants, which might be related to the lower temperature in winter, at which the concentration of O₃ is usually lower. An analysis of air pollution control measures and their co-benefits in Jinan showed a significant increasing trend in ozone concentrations in the city since 2013 (40), which further highlights the importance of continuous monitoring and assessment of the public health impacts of air pollution. This reminds us of the need to comprehensively consider the characteristics of different pollutants and their health impacts when formulating air quality improvement measures.

In our multi-pollutant model, the correlation coefficients between NO₂ and PM₁₀ and between NO₂ and CO are very high, suggesting the problem of multicollinearity. Therefore, we included only PM_2.5, SO₂ and O₃ in our multi-pollutant model. The modeling results showed that NO₂ was associated with a decrease in lung function indices (P<0.05), especially PEF, which is consistent with the results of a national cohort study (41). These results strongly suggest that traffic-related air pollution, represented by NO₂, has a significant impact on children’s respiratory health.

The specific mechanisms by which sex differences play a role in the response to air pollutants are currently unknown. Although existing studies have given increasing attention to this issue, the conclusions are not consistent. Notably, our study reveals that boys’ lung function is more sensitive to air pollutants compared to girls’, which is consistent with previous studies (42,43). However, there are also studies with opposite findings (9,44,45). These differences may stem from differences in methodology, measurement techniques, and age distribution of subjects across studies, while the types of air pollutants studied may also have an impact on the results. Therefore, to fully understand the role of gender in the response to air pollutants, future research needs to integrate these factors and use more standardized research methods.

Despite the insights of this study, there are some limitations. First, because air pollutant data are derived from existing and fixed monitoring stations, their measurements may deviate from the actual exposure of individuals, and this indirect assessment of exposure may lead to misclassification. Second, the study failed to adequately consider and adjust for some important confounding factors, such as home environment, parental smoking habits, and socioeconomic status, which may have an impact on children’s lung function, and therefore future studies could incorporate home environment questionnaires to collect data on household pollutants (e.g., indoor smoking, cooking practices, heating equipment, etc.) to more comprehensively assess the actual exposure of children’s levels. Third, since the pulmonary function tests in this study were performed specifically in December of each year, this temporal limitation makes the significance and generalizability of the findings somewhat limited. However, even with these limitations, the present study revealed a strong association between short-term exposure to different air pollutants and acute impairment of lung function in children and pointed out the lagged and cumulative lagged effects of air pollutants.

Conclusions

Overall, the present study reveals a significant negative association between some atmospheric pollutants and lung function in healthy children, especially those closely related to transportation, such as NO₂ and SO₂. Therefore, to effectively safeguard the respiratory health of children and adolescents, we need to adopt a series of targeted public health measures.

Acknowledgments

The authors are grateful to the Center for Disease Control and Prevention of Licheng District for sharing the information needed for this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-24-438/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-438/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-438/prf

Funding: This study was funded by 2021 Shandong Province Higher Education Institutions “Youth Innovation Talent Introduction and Cultivation Plan” (Public Health Safety Risk Assessment and Response Innovation Team), and Research project of Shandong Educational Supervision Society (No. SDJYDDXH2023-2159).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-24-438/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Shandong Second Medical University (No. 2021SDL188) and informed consent was taken from all the participants’ guardians.

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

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