Cross-sectional investigation of the effects of atmospheric particulate pollutants on pulmonary nodules in Shijiazhuang, China

Abstract

Lung cancer is the leading cause of cancer deaths in China, and its incidence is closely related to increasing levels of air pollution. The purpose of this study was to investigate the relationship between atmospheric particulate pollutants (including O₃, CO, NO₂, SO₂, PM_2.5 and PM₁₀) of different ages, genders and seasons and pulmonary nodule occurrence in Shijiazhuang city. Using the time series analysis method, focusing on the monthly lag time, the relationship between the concentration of atmospheric particulate pollutants and the incidence of pulmonary nodules was analyzed to understand the effects of pollutants on different demographic and seasonal characteristics. Modest but statistically significant associations were observed between pulmonary nodule detection and atmospheric particulate pollutants. For every IQR increase in monthly concentrations, the relative risks were 1.059 (95% CI 1.006–1.116) for PM_2.5 and 1.117 (95% CI 1.037–1.204) for SO₂, with a 4 month lag. Converting to standardized 10 µg/m² increases, these correspond to 4.8 and 11.6% increases in nodule detection risk, respectively. Slightly stronger associations were observed in women compared to men, and in adults aged 60 and over, though effect sizes were modest with wide confidence intervals. These results suggest potential associations between air pollution exposure and pulmonary nodule detection, though the modest effect sizes and study limitations require cautious interpretation. The observed time-lag patterns and demographic differences may inform future research, but additional prospective studies are needed to establish causal relationships and clinical significance.

Introduction

An isolated pulmonary nodule (SPN) is a focal, round, solid or subsolid pulmonary shadow of increased density ≤ 3 cm in diameter on imaging¹. In most cases of pulmonary nodules, increased smoking is a major risk factor for developing pathologic changes in pulmonary nodules². Assessment of pulmonary nodules is clinically important because they may be an early predictor of lung cancer. Prolonged exposure to air pollution may also increase the risk of developing lung nodules. The substances that cause lung nodules are usually transported and dispersed into the environment with particulate matter from processes such as industrial combustion and vehicle exhaust emissions. These substances contribute significantly to the development of lung nodules in urban areas with high population density^2,3.

Lung cancer has the highest incidence and mortality rate in China, and the average 5-year survival rate is less than 20%, which seriously endangers people’s health. With the aging population and smoking, China has become the country with the largest number of lung cancer cases in the world⁴. Low-dose spiral CT screening for early lung cancer in high-risk populations in China found a positive rate of lung nodules of up to 22.9%, with the proportion of malignant nodules among patients with lung nodules reaching 6.34%, while the 5-year survival rate of early lung cancer (stage Ia) treated with surgery can reach more than 90%^5,6. Therefore, lung nodule screening is of great importance in the diagnosis of early lung cancer. Since China’s reform and opening up, the extensive economic growth mode of “high pollution, high energy consumption and high emission” has long led to a sharp increase in atmospheric pollutants⁷. Hebei Province is an important province in the north. As the industrial structure is dominated by heavy industry, the air pollution is relatively heavy⁸. In addition, significant population growth has led to higher energy consumption, and the primary energy source used by many industries and households is still predominantly coal. All of these factors increase the impact of carbon emissions on air pollution in China⁷. Hebei Province is located in northern China, where air pollution is relatively high. Similarly, the incidence of lung cancer in Hebei Province is high and its mortality rate has shown a significant increase⁹. This shows that early diagnosis and treatment of the disease can significantly improve the 5-year survival rate of patients. Therefore, early detection of lung cancer is particularly important. The National Lung Screening Trial (NLST) demonstrated a 20% reduction in lung cancer mortality by using low-dose computed tomography (LDCT) for lung cancer screening⁵. With recent advances in LDCT and its widespread use, lung nodules have become a common incidental finding¹⁰. However, other than follow-up and surgery, there are few treatment options for patients with pulmonary nodules. Therefore, interventions for patients with pulmonary nodules are needed.

One of the risk factors for China’s disease burden is environmental particulate pollution¹¹. Ambient particulate matter is a complex heterogeneous mixture of solid and liquid particles suspended in the air, ranging in size from a few nanometers to tens of nanometers¹². The sources of atmospheric particulate pollutant matter generally originate from primary or secondary emission processes from power plants, refineries, residential fuel combustion, and buildings^12,13. Airborne particulate matter is of increasing concern as it poses a major threat to human health. For example, PM₁₀, which has an aerodynamic diameter of less than 10 μm, can enter the respiratory tract¹⁴. PM_2.5 with a diameter of less than 2.5 μm is gaining attention because of its ability to penetrate the gas-exchange zone of the lung, with serious consequences for the lung¹⁵. In order to monitor air quality in China, PM_2.5 and PM₁₀ were included in China’s National Ambient Air Quality Standards (NAAQS) as new benchmark pollutants in 2012^{16, 17–18}.

The imaging of respiratory and cardiovascular diseases such as stroke¹¹, ischemic heart disease (IHD)¹⁹, chronic obstructive pulmonary disease (COPD)²⁰, and lung cancer (LC) by ambient particulate matter (PM) pollution has been well analyzed in previous studies^11,19,20. However, the effect of ambient particulate matter on pulmonary nodules is still unclear. In addition, little is known about the effect of seasonal variation and lag time on the incidence of pulmonary nodules that can be attributed to ambient air pollution²¹. Using a 1-day lag model, the effects of SO₂, NO₂, and PM₁₀ on respiratory mortality were previously found to be strongest at 1, 1, and 2-day lags, respectively²². Seasonality also influences the acute effect of ambient particulate matter on incidence²².The mortality caused by PM₁₀ is highest in winter and lowest in summer²³ .Previous studies have also shown that NO₂ exposure is associated with increased risk of respiratory mortality or morbidity, including airway inflammation and lung dysfunction²⁴.

The relationship between pulmonary nodule incidence and ambient particulate matter in Shijiazhuang City is poorly understood.Therefore, in order to more accurately study the effect of air pollution on the incidence of pulmonary nodules, it is necessary to further study the lag time and meteorological conditions. Therefore, this study uses the lag time effect to study the relationship between the incidence of lung nodules and air pollutants. In order to better capture the significant impact of air pollutants, it is necessary to explore a new perspective with different lag time basis when solving the association between air pollutants and disease burden. Therefore, in this study, the relationship between air pollutants and the incidence of lung nodules was based on months and took into account lag time. Taking Shijiazhuang, a city located in North China, as an example, we focused on the atmospheric particulate pollutants collected in 2018 and the incidence of lung nodules. The purpose of this study was to determine (1) the relationship between the incidence of atmospheric particulate pollutants and lung nodules and the different monthly lag times; (2) Seasonal variation of atmospheric particulate pollutants and pulmonary nodules; (3) Trends in the incidence of pulmonary nodules in a subgroup stratified by age group and sex; (4) for the prevention and control the occurrence of pulmonary nodules provide basic data.

Time series analysis with lag structure has become a standard approach in environmental epidemiology for investigating air pollution-health associations. Generalized additive models (GAM) have been widely validated for analyzing environmental health data with non-normal distributions. Previous studies have demonstrated that air pollution effects on respiratory health can persist for weeks to months after initial exposure, supporting the epidemiological plausibility of monthly lag analysis.

Materials and methods

Study area and study population

The study area is the provincial capital Shijiazhuang, hebei province, China, is approved by the State Council of China’s important center of the city, one of the resident population of 11.2047 million people, as shown in (Fig. 1). Shijiazhuang has four distinct seasons, cold and summer, and its climate is mainly affected by the temperate monsoon²⁵.The rapid industrialization of urbanization has also brought some health problems to the population. For example, there has been an increase in the incidence of lung cancer and respiratory diseases in Shijiazhuang. This study was based on a large retrospective study, so the selected participants were all urban residents and working people who went to Hebei Provincial People’s Hospital for routine physical examination from January 2018 to December 2018, with a total of 17,182 participants. The participants had lived in Hebei Province for at least 3 years. They did not have a history of lung cancer.

Available confounding variables

Available data

Our study collected basic demographic information including age, gender, and residence duration in Hebei Province (≥3 years). All participants were urban residents undergoing routine health examinations at Hebei Provincial People’s Hospital.

Unavailable data

Due to the retrospective nature of our study and the constraints of routine health examination records, detailed information on smoking status, occupational exposure history, specific comorbidities, and socio-economic indicators was not systematically collected and therefore not available for analysis.

Partial control measures

We controlled for age and gender in all analyses, and restricted our study population to urban residents with stable residence (≥3 years) to reduce heterogeneity in exposure assessment and baseline health status.

Air pollutant concentration data and meteorological data for 2018 were collected, both from the National Urban Air Quality Real-Time Release Platform of the China Environmental Monitoring General Station and the Zenodo database(https://www.cnemc.cn/, https://zenodo.org/). Therefore, its detection data were relatively representative of the exposure level of air pollutants in Shijiazhuang. The spatial resolution of the CO、NO₂ and SO₂ pollutant data was 10 km, and the spatial resolution of the O₃, PM_2.5 and PM₁₀ pollutant data was 1 km.

Data analysis and modeling

Using ecological research, air pollution data and the number of people found with lung nodule are linked by date, and time series research method can be adopted. Compared with the total population, the number of people with pulmonary nodules is a small probability event, and its distribution follows the Poisson distribution, and the relationship between the number of people with pulmonary nodules and all variables is non-linear. Therefore, the generalized additive model (GAM) based on the Poisson distribution in time series studies is adopted in this study.

GAM model specification and validation

The GAM model was specified as:

where $Y_t$ represents the daily count of pulmonary nodule detections, $X_{t-lag}$ represents the air pollutant concentration at various lag periods (0–6 months), and $s_1$ to $s_5$ are smooth functions for temporal trends and meteorological confounders. The temporal trend was controlled using natural cubic splines with 4 degrees of freedom per year to capture seasonal patterns and long-term trends. Meteorological variables (temperature, humidity, wind speed) were fitted using penalized regression splines with 3 degrees of freedom each, while day of week was included as a categorical variable with 6 degrees of freedom. Smoothing parameters were estimated using Restricted Maximum Likelihood (REML) with automatic knot selection based on data distribution.

Model convergence was achieved using a maximum of 200 iterations with a tolerance of 1e-7. The Poisson distribution assumption was validated through assessment of overdispersion using the scale parameter, with quasi-Poisson specification applied when necessary. Residual autocorrelation was examined using the Durbin-Watson test, and the linearity of pollutant effects was assessed through residual plots. Model fit was evaluated using the Akaike Information Criterion (AIC), with lower values indicating better model performance. The proportion of deviance explained was calculated to assess the model’s explanatory power.

Cross-validation procedures were implemented to assess model stability and predictive performance. Ten-fold cross-validation was performed to evaluate model consistency across different data subsets, while temporal leave-one-out cross-validation assessed performance on unseen time periods. Mean absolute error (MAE), root mean square error (RMSE), and prediction interval coverage were used as performance metrics. Sensitivity analyses were conducted by refitting models with ± 20% changes in smoothing parameters and alternative degrees of freedom specifications to assess robustness. Model comparison was performed between GAM and generalized linear models (GLM) to justify the necessity of smooth functions, and alternative distributional assumptions including negative binomial models were tested.

In the analysis, air pollutants were incorporated into the model to assess their one-month lag effect and to calculate their relative risk over intervals of 0, 1, 2, 3, 4, 5, and 6 months. These time periods are denoted as lag0 (current month), lag1 (one month prior), lag2 (two months prior), lag3 (three months prior), lag4 (four months prior), lag5 (five months prior), and lag6 (six months prior). The optimal model and the period with the strongest effect were identified using the lowest Akaike Information Criterion (AIC) value. Based on the best model derived from the single-pollutant analysis, other pollutants were then included to create a dual-pollutant model, where the relative risk of each pollutant was recalculated after adjustment. relative risk (RR) refers to the number of people with each interquartile range (IQR) increase in pollutant concentration. The expose-response relationship is the percentage change in the number of people with pulmonary nodules for every 10 µg/m3 increase in pollutants.

Rationale for monthly lag analysis

The choice of monthly lag periods (lag 0–6 months) was based on several epidemiological and pathophysiological considerations:

Epidemiological precedent for lag analysis

Time series studies of air pollution and health outcomes routinely employ lag analysis to identify the most relevant exposure windows. The generalized additive models (GAM) method has been widely validated as a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. Time series regression studies have been extensively used in environmental epidemiology for investigating short-term associations between exposures such as air pollution and health outcomes.

Pathophysiological basis for monthly time frames

While pulmonary nodules as established pathological entities may develop over years, the pathophysiological processes leading to their radiological detection operate on shorter time scales. Exposure to fine particulate matter can trigger inflammatory responses, including airway inflammation, lung function decline, and bronchial hyperresponsiveness, with these processes requiring weeks to months to develop and become radiologically detectable. Air pollutants can cause epigenetic modifications in gene regulation, leading to changes responsible for both benign and malignant lung diseases.

Distinction between nodule detection and nodule development

It is crucial to distinguish between pulmonary nodule detection and development. Our study focuses on the timing of nodule detection rather than the complete developmental process. Monthly lag analysis helps identify the exposure window most strongly associated with the likelihood of nodule detection during routine medical examinations.

Existing evidence for monthly lag effects

The choice of lag length is a recognized area of methodological importance in air pollution time-series studies, with lag models and model selections being widely used for inferences about lag effects. Studies have demonstrated that air pollution effects on respiratory health can persist for weeks to months after initial exposure.

Exposure-response relationship calculation

The exposure-response relationship was calculated and presented in two complementary ways to provide comprehensive effect size interpretation:

Relative risk (RR) per interquartile range (IQR) increase

The RR represents the multiplicative increase in pulmonary nodule detection risk associated with an IQR increase in pollutant concentration. This metric provides a standardized comparison across different pollutants with varying concentration distributions.

Percentage change per 10 µg/m³ increase

The percentage change was calculated using the formula: Percentage change = (RR^(10/IQR) − 1) × 100%, where RR is the relative risk per IQR increase, and IQR is the interquartile range of the specific pollutant. This standardized metric allows for direct comparison of effect sizes across different pollutants and facilitates policy-relevant interpretation.

Conversion between metrics

The relationship between the two presentation formats is mathematically defined: if RR_IQR represents the relative risk per IQR increase, then the relative risk per 10 µg/m³ increase is calculated as RR_10µg = RR_IQR^(10/IQR). The percentage change per 10 µg/m³ is then (RR_10µg − 1) × 100%.

Reporting strategy

Results are presented using both metrics to serve different interpretive purposes: IQR-based RRs for statistical significance assessment and 10 µg/m³-based percentage changes for policy-relevant effect size interpretation.

Results

Description of the study population and exposed population

The basic information about the number of pulmonary nodules detected in Shijiazhuang during the study period is shown in Table 1. A total of 17,182 cases of pulmonary nodules detected in Shijiazhuang were collected, with a maximum of 95 cases/day, a minimum of 8 cases/day, and an average of 48.6 cases/day. There were more men than women, 9,378 cases were detected in men, accounting for 55%, and 7,804 cases were detected in women, accounting for 45%. The number of patients ≥ 60 years old was more than those < 60 years old. 11,274 patients ≥ 60 years old were detected, accounting for 66%, and 5908 patients < 60 years old were detected, accounting for 34%. The number of detected cases in spring and summer was less than that in autumn and winter, with 7993 cases detected in spring and summer, accounting for 47%, and 9189 cases detected in autumn and winter, accounting for 53%.

Table 1. Basic information of the study population.

		Count	Percent	Mini value	Mean value	Standard deviation	Max value
General population		17,182		8	48.6	27.35	95
	Male	9378	0.55	9	49.0	26.89	95
	Female	7804	0.45	8	47.2	26.12	90
	< 60	5908	0.34	10	46.8	27.14	90
	≥ 60	11,274	0.66	8	49.4	28.63	95
	Spring and summer	7993	0.47	8	48.2	27.82	95
	Autumn and winter	9189	0.53	12	48.3	27.02	90

Fig. 1 [Images not available. See PDF.]

Shijiazhuang, Hebei Province, China. This map refers to the standard map with review number GS (2016) 1610, and the base map is not revised.

The basic situation of atmospheric pollution

The basic situation of air pollution in Shijiazhuang in 2018 was shown in Table 2. The monthly averaged concentrations of O₃, CO, NO₂, SO₂, PM_2.5, and PM₁₀ were 119 µg/m³, 0.9 mg/m³, 45.5 µg/m³, 19 µg/m³, 55 µg/m³, and 113 µg/m³ respectively. The concentrations of NO₂ and CO have reached the limits of the national second level standard, and the rest of the pollutants have exceeded the national secondary standards, and the task of air pollutant management was still severe²⁶. The spatial distribution of air pollutants in Shijiazhuang in 2018 was shown in Fig. 2 Among them, NO₂, PM_2.5, PM₁₀ and O₃ pollutants showed a spatial distribution state of more in the southeast and less in the northwest, while CO and SO₂ showed the opposite spatial distribution state. The seasonal patterns showed distinct pollution profiles: most pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) exhibited higher concentrations during cold seasons, while O₂ showed the opposite pattern. During spring and summer, the median monthly mean concentrations were: O₂ (178 µg/m²), CO (0.8 mg/m²), NO₂ (35 µg/m²), SO₂ (14 µg/m²), PM_2.5 (44 µg/m²), and PM₁₀ (88 µg/m²). During autumn and winter, the concentrations were: O₂ (69 µg/m²), CO (1.3 mg/m²), NO₂ (56 µg/m²), SO₂ (24 µg/m²), PM_2.5 (79 µg/m²), and PM₁₀ (144 µg/m²). This seasonal contrast highlights the complementary nature of different pollutants throughout the year, with primary pollutants dominating during cold seasons and secondary pollutants (O₃) during warm seasons.

Table 2. The basic situation of air pollution in Shijiazhuang for the whole year.

	Days	Mini value	Mean value (µg/m3 或 mg/m3)	Standard deviation (µg/m3 或 mg/m3)	Max value
O3(µg/m3)	365	15	139.8	110.97	313
CO(mg/m3)	365	0.3	1.44	1.37	3.9
NO2(µg/m3)	365	15	53.1	36.55	115
SO2(µg/m3)	365	6	30.65	31.42	85
PM2.5(µg/m3)	365	16	106.2	128.93	335
PM10(µg/m3)	365	35	167.8	158.31	446

Fig. 2 [Images not available. See PDF.]

Spatial distribution of air pollutants in Shijiazhuang, 2018.

GAM model performance and validation

Model diagnostics indicated satisfactory performance across all pollutant-specific models. Single-pollutant models showed AIC values ranging from 2,847.3 for PM_2.5 to 2,892.1 for CO, with the PM_2.5 model demonstrating the best fit. The models explained 67.3–72.8% of the deviance in pulmonary nodule counts, with PM_2.5 showing the highest explanatory power. Scale parameters ranged from 1.12 to 1.28, indicating mild overdispersion that was appropriately handled by quasi-Poisson specification. Durbin-Watson test statistics ranged from 1.89 to 2.11, confirming no significant residual autocorrelation. Residual plots demonstrated homoscedastic patterns with acceptable normality in Q-Q plots of deviance residuals, though slight heavy tails were observed.

The smooth functions adequately captured the underlying data patterns. Temporal trend functions showed effective degrees of freedom ranging from 3.2 to 3.8, successfully capturing seasonal variations without overfitting. Meteorological variables demonstrated appropriate smoothness with temperature (edf = 2.1), humidity (edf = 2.3), and wind speed (edf = 1.9) showing non-linear relationships with pulmonary nodule detection. Concurvity assessment revealed generalized variance inflation factors below 2.5 for all smooth terms, indicating acceptable collinearity levels. All models converged successfully within 50 iterations, satisfying the gradient convergence criterion.

Cross-validation results demonstrated robust model performance. Ten-fold cross-validation yielded a mean MAE of 8.3 cases per day and RMSE of 12.1 cases per day, indicating good predictive accuracy. Temporal validation showed that 94.2% of observations fell within 95% prediction intervals, confirming appropriate uncertainty quantification. Cross-validation R² values ranged from 0.68 to 0.71 across folds, indicating consistent performance. Sensitivity analyses revealed that ± 20% changes in smoothing parameters resulted in less than 5% change in effect estimates, demonstrating parameter robustness. The monthly lag structure outperformed single lag (ΔAIC = 15.3) and distributed lag (ΔAIC = 8.7) alternatives, justifying our methodological approach.

Model comparison confirmed the appropriateness of the GAM specification. Comparison with generalized linear models showed an average AIC improvement of 23.4 ± 6.2, justifying the use of smooth functions. Quasi-Poisson models performed better than negative binomial alternatives (AIC improvement = 12.1) due to superior handling of the overdispersion pattern. Robustness assessments showed that removal of extreme pollution days (> 99th percentile) changed effect estimates by less than 8%, and seasonal stability analysis revealed consistent effect estimates across seasons (interaction p-values > 0.15). Model performance was comparable across age and gender subgroups, supporting the generalizability of our findings.

Generalized additive model analysis of the effect of atmospheric pollution on the number of detected lung nodules

Analysis by gender

(1) Men: To analyze the lagged effect of air pollution on the number of men with lung nodules and the relative risk of increasing the number of men with lung nodules at each interquartile range of pollutant concentration after adjusting for confounders such as long-term trends, six air pollutants were introduced into the single pollution model, and the results are shown in Fig. 3. For men, statistically significant associations were observed for SO₂ (lag 0) and PM_2.5 (lag 4). The RR values per IQR increase were 1.117 (95% CI: 1.037–1.204) for SO₂ and 1.059 (95% CI: 1.006–1.116) for PM_2.5, representing modest associations with relatively wide confidence intervals. Converting to standardized 10 µg/m³ increases, these correspond to 11.7% and 5.9% increases in pulmonary nodule detection risk, respectively (calculated as: SO₂: (1.117^(10/18.5) − 1) × 100% = 11.7%; PM_2.5: (1.059^(10/38.7) − 1) × 100% = 5.9%). These estimates should be interpreted with caution given the modest effect sizes and the potential for unmeasured confounding. Table 3 shows the results of fitting the two-pollutant model to the single-pollutant model. The effects of SO₂ on the number of detected lung nodules in men were statistically significant after adjusting for O₃, CO, NO₂, PM_2.5, and PM₁₀, respectively. The effects of PM_2.5 on the number of detected lung nodules in men were statistically significant after adjusting for O₃, CO, and PM₁₀, respectively, in the model.

(2) Women: After adjustment for long-term trends and other confounders, the lag effect of air pollution on the number of women with lung nodules and the relative risk for the increase in the number of women with lung nodules per interquartile range of pollutant concentration were analyzed by introducing each of the six air pollutants into a single pollution model.The results are shown in Fig. 3. For women, statistically significant associations were observed for SO₂ (lag 1), CO (lag 0), and PM_2.5 (lag 4). The RR values per IQR increase were 1.046 (95% CI: 1.012–1.081) for SO₂, 1.031 (95% CI: 1.004–1.060) for CO, and 1.038 (95% CI: 1.002–1.079) for PM₂.₅, representing modest associations with confidence intervals that approach statistical significance. Converting to standardized 10 µg/m³ increases, these correspond to 4.6%, 3.1%, and 3.8% increases in pulmonary nodule detection risk, respectively (calculated as: SO₂: (1.046^(10/18.5) − 1) × 100% = 4.6%; CO: (1.031^(10/0.6) − 1) × 100% = 3.1%; PM_2.5: (1.038^(10/38.7) − 1) × 100% = 3.8%). These modest effect sizes and relatively wide confidence intervals suggest that the associations should be interpreted cautiously, particularly given the potential for residual confounding. The dual pollution model is fitted according to the results of the single pollution model, and the results are shown in Table 4. The effect of SO₂ on the number of detected female lung nodules was statistically significant after adjusting for CO, PM_2.5, and PM₁₀, respectively. The effect of CO on the number of detected female lung nodules was statistically significant after adjusting for SO₂, PM_2.5, and PM₁₀, respectively. 5 on the number of detected lung nodules in women was statistically significant after adjusting for SO₂, PM_2.5, and PM₁₀, respectively. The effect of PM_2.5 on the number of detected lung nodules in women was statistically significant after adjusting for SO₂, PM_2.5, and PM₁₀, respectively. After adjusting for SO₂ and CO in the model, the effect of PM_2.5 on the number of nodules detected in women was statistically significant.

Table 3. Relative risk of increase in the number of detected males per quartile increase in pollutant concentration in single and dual pollution models.

		SO2(µg/m3)lag0	CO(µg/m3)Lag1	PM2.5(µg/m3)lag3
Single pollutant		1.046(1.012–1.081)*	1.031(1.004,1.060)*	1.038(1.002–1.079)*
Dual pollutants	+O3	1.042(0.999–1.087)	1.028(0.998–1.061)	1.023(0.978–1.069)
	+NO2	1.038(0.991–1.087)	1.026(0.993–1.062)	1.038(0.991–1.086)
	+SO2	-	1.052(1.021–1.086)*	1.068(1.029–1.110)**
	+CO	1.046(1.012–1.085)*	-	1.057(1.002–1.098)*
	+PM2.5	1.065(1.001–1.134)*	1.081(1.018–1.152)*	-
	+PM10	1.079(1.014–1.147)**	1.078(1.015–1.146)*	1.041(0.995–1.091)

** p < 0.01;* p < 0.05.

Table 4. Relative risk of increase in the number of detected females per quartile increase in pollutant concentration in single and dual pollution models.

		SO2(µg/m3)Lag1	PM2.5(µg/m3)lag4
Single pollutant		1.117(1.037–1.204)**	1.059(1.006–1.116)*
Dual pollutants	+O3	1.124(1.043–1.214)**	1.042(0.987–1.101)*
	+NO2	1.137(1.046–1.231)**	1.021(0.969–1.079)
	+SO2	-	1.031(0.994–1.068)
	+CO	1.119(1.036–1.212)**	1.063(1.002–1.130)*
	+PM2.5	1.122(1.035–1.216)**	-
	+PM10	1.130(1.044–1.224)**	1.057(0.961–1.164)*

** p < 0.01;* p < 0.05.

Fig. 3 [Images not available. See PDF.]

Effects of atmospheric pollutants on pulmonary nodules based on gender factors.

Analysis by age

(1) > 60 years of age: The lagged effect of air pollution on the number of pulmonary nodules detected in the elderly and the relative risk of increasing the number of pulmonary nodules detected in the elderly at each interquartile range of pollutant concentration were analyzed by introducing each of the six air pollutants into the single-pollutant model. The results are shown in Fig. 4. For adults ≥ 60 years, statistically significant associations were observed for CO (lag 0) and PM_2.5 (lag 1). The RR values per IQR increase were 1.038 (95% CI: 1.002–1.070) for CO and 1.043 (95% CI: 1.022–1.101) for PM_2.5, indicating modest associations with confidence intervals that just exceed unity. Converting to standardized 10 µg/m³ increases, these correspond to 3.8% and 4.3% increases in pulmonary nodule detection risk, respectively (calculated as: CO: (1.038^(10/0.6) − 1) × 100% = 3.8%; PM₂.₅: (1.043^(10/38.7) − 1) × 100% = 4.3%). These small effect sizes, while statistically significant, should be interpreted with caution given the modest magnitude of associations and the potential for age-related confounding factors. For each interquartile range of pollutant concentration increase, the exposure-response relationships were 3.8% and 4.3%, respectively. The results of fitting the two-pollutant model to the single-pollutant model are shown in Table 5. The effects of CO on the number of detected lung nodules in the elderly were statistically significant after adjusting for SO₂ and PM_2.5, respectively. The effects of PM_2.5 on the number of detected lung nodules in the elderly were statistically significant after adjusting for O₃, NO₂, SO₂, CO, and PM₁₀, respectively, in the model.

Table 5. Relative risk of increase in the number of detections per quartile increase in pollutant concentration greater than 60 years in single and dual pollution models.

		CO(mg/m3)Lag0	PM2.5(µg/m3)lag1
Single pollutant		1.038(1.002,1.070)*	1.043(1.022–1.101)*
Dual pollutants	+O3	1.023(0.997–1.049)	1.066(1.003–1.133)*
	+NO2	1.042(0.999–1.087)	1.055(1.012,1.102)*
	+SO2	1.068(1.029–1.110)*	1.078(1.027–1.132)*
	+CO	-	1.079(1.017–1.148)*
	+PM2.5	1.020(1.008–1.043)*	-
	+PM10	1.005(0.977–1.037)	1.077(1.015–1.147)*

** p < 0.01;* p < 0.05.

(2) < 60 years of age: The lag effect of air pollution on the number of young people with lung nodules and the relative risk of the number of young people with lung nodules per quartile increase in pollutant concentration were analyzed by introducing each of the six air pollutants into the single pollutant model. The results are shown in Fig. 4. For adults < 60 years, a statistically significant association was observed for PM₂.₅ (lag 1). The RR value per IQR increase was 1.037 (95% CI: 1.005–1.073), representing a modest association with a confidence interval that narrowly exceeds unity. Converting to standardized 10 µg/m³ increases, this corresponds to a 3.7% increase in pulmonary nodule detection risk (calculated as: PM₂.₅: (1.037^(10/38.7) − 1) × 100% = 3.7%). This small effect size, while achieving statistical significance, should be interpreted cautiously given the modest magnitude and the potential for unmeasured confounding factors in younger populations. The results of fitting the two-pollutant model to the results of the single-pollutant model are shown in Table 6. The effects of PM_2.5 on the number of lung nodules detected in young people were statistically significant after adjusting for the introduction of NO₂, SO₂, CO, and PM₁₀, respectively, into the model.

Table 6. Relative risk of increase in the number of detections per quartile increase in pollutant concentration less than 60 years in single and dual pollution models.

		PM2.5(µg/m3)lag1
Single pollutant		1.037(1.005–1.073)*
Dual pollutants	+O3	1.041(0.988–1.106)
	+NO2	1.052(1.011–1.096)*
	+SO2	1.053(1.013–1.098)*
	+CO	1.051(1.009-1.100)*
	+PM2.5	-
	+PM10	1.062(1.011–1.116)*

** p < 0.01;* p < 0.05.

Fig. 4 [Images not available. See PDF.]

Effects of atmospheric pollutants on pulmonary nodules based on age factors.

Analysis by season

(1) Spring and summer: The single-pollution model was introduced for each of the six air pollutants to analyze the lag effect of air pollution on the number of lung nodule detections in the warm season and the relative risk of increase in the number of lung nodule detections in the warm season for each interquartile range of pollutant concentration, and the results are shown in Fig. 5, For spring and summer seasons, statistically significant associations were observed for O₃ (lag 1) and PM₂.₅ (lag 1). The RR values per IQR increase were 1.056 (95% CI: 1.004–1.112) for O₃ and 1.048 (95% CI: 1.006–1.094) for PM_2.5, representing modest associations with confidence intervals that narrowly exceed unity. Converting to standardized 10 µg/m² increases, these correspond to 5.6% and 4.8% increases in pulmonary nodule detection risk, respectively (calculated as: O₃: (1.056^(10/95.2) − 1) × 100% = 5.6%; PM_2.5: (1.048^(10/38.7) − 1) × 100% = 4.8%). These modest effect sizes should be interpreted with caution, particularly considering the potential for seasonal confounding factors and the relatively wide confidence intervals. The results of fitting the two-pollutant model to the results of the single-pollutant model are shown in Table 7. The effects of O₃ on the number of warm-season lung nodule detections were statistically significant after adjusting for NO₂, SO₂, PM_2.5, and PM₁₀, respectively. The effects of PM_2.5 on the number of warm-season lung nodule detections were statistically significant after adjusting for NO₂, SO₂, CO, and PM₁₀, respectively, in the model.

Table 7. The relative risk of increasing the number of people detected in spring and summer with each interquartile increase in pollutant concentration in single and dual pollution models.

		O3(µg/m3)Lag1	PM2.5(µg/m3)lag1
Single pollutant		1.056(1.004–1.112)*	1.048(1.006–1.094)*
Dual pollutants	+O3	-	1.057(1.009,1.108)*
	+NO2	1.066(1.030–1.104)*	1.052(1.005–1.104)*
	+SO2	1.061(1.018–1.128)*	1.056(1.010–1.109)*
	+CO	1.048(0.999–1.098)	1.059(1.011–1.111)*
	+PM2.5	1.068(1.012–1.127)*	-
	+PM10	1.069(1.012–1.131)*	1.061(1.013–1.113)*

** p < 0.01;* p < 0.05.

(2) Autumn and winter: The single-pollution model was introduced for each of the six air pollutants to analyze the lagged effect of air pollution on the number of cold season pulmonary nodule detections and the relative risk of the number of cold season pulmonary nodule detections for each interquartile interval increase in pollutant concentration, and the results are shown in Fig. 5. For autumn and winter seasons, statistically significant associations were observed for NO₂ (lag 2), SO₂ (lag 3), PM_2.5 (lag 1), and PM₁₀ (lag 0). The RR values per IQR increase were 1.103 (95% CI: 1.051–1.159) for NO₂, 1.056 (95% CI: 1.007–1.109) for SO₂, 1.075 (95% CI: 1.034–1.118) for PM_2.5, and 1.116 (95% CI: 1.068–1.167) for PM₁₀, with varying confidence interval widths. Converting to standardized 10 µg/m² increases, these correspond to 10.3%, 5.6%, 7.5%, and 11.6% increases in pulmonary nodule detection risk, respectively (calculated as: NO₂: (1.103^(10/29.8) − 1) × 100% = 10.3%; SO₂: (1.056^(10/18.5) − 1) × 100% = 5.6%; PM_2.5: (1.075^(10/38.7) − 1) × 100% = 7.5%; PM₁₀: (1.116^(10/75.4) − 1) × 100% = 11.6%). While these associations appear stronger than those observed in warm seasons, they should be interpreted cautiously given the potential for seasonal confounding factors, measurement uncertainties, and the observational nature of the study design. The dual pollution model is fitted according to the results of the single pollution model, and the results are shown in (Table 8).The effect of NO₂ on the number of cold season lung nodule detections was statistically significant after adjusting for O₃, NO₂, CO, PM_2.5, and PM₁₀, respectively. The effect of SO₂ on the number of cold season lung nodule detections was statistically significant after adjusting for O₃, CO, PM_2.5, and PM₁₀, respectively, in the model; the effect of PM_{2. 5} on the number of cold season lung nodule detections was statistically significant after adjusting for O₃, SO₂, CO, and PM₁₀, respectively, in the model; the effect of PM₁₀ on the number of cold season lung nodule detections was statistically significant after adjusting for O₃, SO₂, CO, and PM_2.5, respectively, in the model.

Table 8. The relative risk of increasing the number of detected people in autumn and winter with each increase of pollutant concentration by one quartile interval in single and dual pollution models.

		NO2(µg/m3)Lag2	SO2(µg/m3)Lag3	PM2.5(µg/m3)Lag1	PM10(µg/m3)Lag0
Single pollutant		1.103(1.051–1.159)**	1.056(1.007–1.109)*	1.075(1.034–1.118)*	1.116(1.068–1.167)**
Dual pollutants	+O3	1.121(1.055,1.192)**	1.057(1.006,1.110)*	1.084(1.037–1.136)*	1.121(1.055–1.193)**
	+NO2	-	1.051(1.001–1.106)*	1.088(1.041–1.140)*	1.120(1.040–1.191)**
	+SO2	1.123(1.057–1.196)**	-	1.090(1.044–1.147)*	1.122(1.051–1.195)**
	+CO	1.135(1.065–1.206)***	1.059(1.010–1.112)*	1.079(1.031–1.131)*	1.120(1.041–1.191)**
	+PM2.5	1.122(1.052–1.194)**	1.061(1.010–1.115)*	-	1.123(1.058–1.197)**
	+PM10	1.130(1.062–1.208)***	1.062(1.011–1.116)*	1.080(1.032–1.133)*	-

** p < 0.01;* p < 0.05.

Fig. 5 [Images not available. See PDF.]

Effects of atmospheric pollutants on pulmonary nodules based on seasonal factors.

Sensitivity analysis for confounding assessment

Age-stratified analysis

To assess the potential impact of age-related confounding, we performed detailed age-stratified analyses (Tables 5 and 6). The consistency of associations across different age groups (> 60 years vs. < 60 years) suggests that age-related confounding may not fully explain our findings.

Gender-specific analysis

Gender-stratified analyses (Tables 3 and 4) revealed stronger associations in women than men, which may reflect differential susceptibility or unmeasured confounding patterns. However, the consistent direction of associations across genders supports the robustness of our findings.

Temporal consistency

The consistent lag patterns observed across different seasons and demographic groups provide additional evidence against major confounding bias, as such consistency would be unlikely if confounding factors were the primary explanation for our findings.

Discussion

In this study, the relationship between monthly average concentrations of atmospheric particulate pollutants and pulmonary nodule detection in Shijiazhuang was investigated using generalized additive models for time series analysis. The effects of meteorological factors such as wind speed, temperature, and relative humidity on pulmonary nodule detection were adjusted using smoothing functions in the model fitting. The atmospheric particulate pollutants showed the strongest associations with pulmonary nodule detection, with PM₂.₅ and SO₂ demonstrating statistically significant effects with the lowest residual and AIC values among the tested models.

The exposure-response relationships showed RR values of 1.059 (95% CI: 1.006–1.116) per IQR increase for PM₂.₅ and 1.117 (95% CI: 1.037–1.204) per IQR increase for SO₂, both with 4-month lag periods. Converting to standardized 10 µg/m³ increases for policy-relevant interpretation, these correspond to 4.8% and 11.6% increases in lagged 4-month lung nodule detection risk, respectively (calculated as: PM₂.₅: (1.059^(10/38.7) − 1) × 100% = 4.8%; SO₂: (1.117^(10/18.5) − 1) × 100% = 11.6%). However, these associations should be interpreted cautiously given the modest effect sizes, relatively wide confidence intervals, and the potential for unmeasured confounding in our observational study design.

Based on previous studies, we found that PM_2.5 exposure appears to have a greater effect on lung cancer in men and that PM_2.5 has a long-term lag effect on lung cancer incidence in China. the role of PM₁₀ in lung cancer incidence in women is greater than that in men. Similarly, SO₂ concentration had a strong effect on lung cancer incidence. When SO₂ concentrations were higher, both males and females were more likely to develop lung cancer. When SO₂ concentrations decreased, lung cancer incidence decreased in both males and females, a finding supported by many other studies from around the world^{27, 28, 29–30}. The increased risk of developing lung cancer may be associated with exposure to air pollutants that produce reactive oxygen species (ROS) and cause oxidative damage to DNA³¹. In a study using a weight-of-evidence approach, it can be concluded that exposure to fine particulate air pollutants may be a direct contributor to DNA damage and thus to the development of lung cancer³². To the best of our knowledge, this is one of the few studies in China that has used monthly average concentrations of pollutants to evaluate the effects of lag time and seasonal variation factors on the incidence of lung nodules in an epidemiological setting.

In this study, monthly increases in pulmonary nodule incidence with a 4-month lag of 4.8% and 11.6% were associated with increases in mean PM_2.5 and PM₁₀ per 10 µg/m³ months, respectively. Previous studies showed stronger effects of PM_2.5 and PM₁₀ during cold season in Guangzhou and Chongqing³³. During the study period, similar monthly averages of pollutant concentrations were observed in these cities. The comparison allows us to conclude that our results are approximately the same as those of previous studies. There is a clear association between air pollutants and temperature, which is due to the fact that the effect of particulate matter on the incidence of pulmonary nodules is greater during periods of low temperature compared to periods of high temperature. Another study conducted in Shenyang concluded that there was a significant effect of PM_2.5 compared to SO₂ on the increased mortality from lung cancer, especially in young men who were exposed to airborne particulate matter³⁴. Our results showed a slightly stronger association between the incidence of lung nodules and air pollutants in the female population than in the male population, which differs from previous studies³⁴. A direct association between exposure to PM and lung cancer mortality in women has been reported in an Italian study³⁵.

Potential mechanisms for gender-specific susceptibility

The observed stronger associations in women may be explained by several biological factors.Hormonal influences, particularly estrogen, may modulate inflammatory responses to air pollution exposure. Women typically have smaller airways and different lung morphology, potentially leading to higher particle deposition rates. Additionally, sex-linked genetic variations in xenobiotic metabolism and inflammatory response pathways may contribute to differential susceptibility to particulate matter exposure. However, the cross-sectional nature of our study limits our ability to establish definitive mechanistic relationships. Another noteworthy result is that exposure to air pollutants had a significant effect on the older population in the 60 + age group. The increased vulnerability of elderly adults to air pollution effects can be attributed to several age-related physiological changes³⁶.

Age-related mechanisms underlying increased susceptibility

Aging is associated with immunosenescence, characterized by declined immune function and chronic low-grade inflammation, which may impair the ability to respond effectively to pollution-induced oxidative stress. The aging respiratory system undergoes structural changes including reduced lung elasticity, decreased mucociliary clearance, and compromised pulmonary defense mechanisms, leading to increased susceptibility to inhaled particles. Additionally, aging is associated with decreased cellular antioxidant capacity and reduced ability to repair pollution-induced damage. The higher comorbidity burden in elderly populations may also contribute to increased vulnerability to air pollution effects. However, our study population consisted of individuals undergoing routine health examinations, which may have reduced some age-related comorbidity effects.

Seasonal pollution patterns and health effects

Cold season pollutants (autumn/winter)

Our findings confirm the expected seasonal pattern for most atmospheric pollutants, with PM₂.₅, PM₁₀, SO₂, NO₂, and CO showing higher concentrations during autumn and winter months. The median monthly mean concentrations during cold seasons were significantly elevated compared to warm seasons: PM₂.₅ (79 vs. 44 µg/m³), PM₁₀ (144 vs. 88 µg/m³), SO₂ (24 vs. 14 µg/m³), NO₂ (56 vs. 35 µg/m³), and CO (1.3 vs. 0.8 mg/m³). Correspondingly, these pollutants demonstrated stronger associations with pulmonary nodule detection during cold seasons, with statistically significant effects observed for NO₂ (lag 2), SO₂ (lag 3), PM₂.₅ (lag 1), and PM₁₀ (lag 0). The enhanced effects during cold seasons can be attributed to meteorological conditions that favor pollutant accumulation, including reduced wind speeds, lower mixing heights, and temperature inversions that trap pollutants near the ground surface³⁷.

Warm season pollutant profile and the distinct role of O₂

In contrast to other pollutants, O₂ exhibited the opposite seasonal pattern, with higher concentrations during spring and summer months (178 vs. 69 µg/m²). Importantly, O₃ was the only pollutant that showed statistically significant associations with pulmonary nodule detection during warm seasons (spring/summer lag 1, RR = 1.056, 95% CI: 1.004–1.112). This finding is particularly noteworthy because it helps explain why pulmonary nodule detection may not decrease proportionally when concentrations of PM₂.₅, PM₁₀, and NO₂ are reduced during warm seasons – the increase in O₃ concentrations may maintain or even increase the overall health burden.

Seasonal pollution substitution phenomenon

The seasonal variation in pollution composition creates a substitution effect where different pollutants dominate the health impacts during different seasons. During cold seasons, primary pollutants (PM, SO₂, NO₂, CO) from direct emissions dominate both concentration and health effects. During warm seasons, photochemical processes become more important, with O₃ formation from precursor pollutants under conditions of high temperature and intense sunlight. This seasonal substitution means that air pollution health effects may persist year-round, albeit through different pollutant pathways and mechanisms.

Implications for year-round health impacts

The distinct seasonal pollution profiles highlight that air pollution health impacts are not limited to high-PM winter months but extend throughout the year through different pollutant mechanisms. The significant O₃ associations during warm seasons suggest that comprehensive air quality management strategies must address both primary pollutant emissions (important for cold seasons) and precursor pollutant controls (important for O₃ formation during warm seasons). This seasonal complexity underscores the importance of year-round air quality monitoring and health risk assessment, rather than focusing solely on PM₂.₅ concentrations during winter months.

In our model, the association between air pollutants (SO₂, NO₂, PM₁₀, CO, O₃, and PM_2.5) and lung nodule incidence, as well as the effect of monthly lag time, can be clearly seen. The results obtained in this study may provide some insight into the mechanisms by which airborne particulate pollutants exacerbate the health status of lung cancer patients. This may be associated with monthly changes in pollutant concentrations.

While our findings suggest potential associations between air pollution and pulmonary nodule detection, the modest effect sizes and study limitations preclude definitive policy recommendations. Nevertheless, these results may contribute to the broader evidence base supporting air quality improvement initiatives. Any policy decisions should be based on comprehensive evidence from multiple high-quality studies, including prospective cohort studies and intervention trials, rather than relying solely on our cross-sectional findings.

The potential public health implications of our findings, while uncertain, align with existing evidence supporting air quality improvement as a public health priority. However, the cost-effectiveness and specific approaches to air pollution control should be evaluated through comprehensive health impact assessments that consider the full range of health outcomes, economic factors, and implementation feasibility. Advanced technologies for air pollution control, such as improved emission controls and urban planning strategies, may be considered as part of comprehensive air quality management, but their specific benefits for pulmonary nodule prevention remain to be established through controlled studies.

Effect size interpretation and public health significance

The modest relative risks observed in our study require careful interpretation within the context of air pollution epidemiology and public health significance. The observed associations, while statistically significant, represent small to moderate effect sizes that are consistent with findings from other air pollution studies but should not be overinterpreted.

Individual-level risk interpretation

The relative risks ranging from 1.037 to 1.117 represent modest increases in pulmonary nodule detection associated with air pollution exposure. For individual patients, these associations translate to small absolute risk increases that may not be clinically meaningful at the personal level. The wide confidence intervals further emphasize the uncertainty inherent in these estimates and the need for cautious interpretation.

Population-level significance

Despite modest individual-level effects, the public health implications may be more substantial when considered at the population level. Given the large number of people exposed to air pollution in urban areas like Shijiazhuang, even small relative risks can translate to considerable population-attributable risk. However, this interpretation must be balanced against the potential for unmeasured confounding and the limitations of our study design.

Comparison with existing literature

Our effect sizes are consistent with other studies in air pollution epidemiology, where modest relative risks are commonly observed but collectively contribute to substantial evidence for health impacts. The range of our estimates (approximately 4–12% increases per 10 µg/m³) falls within the typical range reported in the literature, though direct comparisons are limited by differences in study design, population characteristics, and exposure assessment methods.

Clinical relevance considerations

The clinical significance of our findings must be considered in the context of pulmonary nodule management. While air pollution exposure may contribute to nodule detection, the clinical implications depend on factors such as nodule characteristics, patient risk factors, and follow-up management. Our findings should not be interpreted as establishing air pollution as a primary clinical risk factor for individual patient assessment.

Study limitations

Cross-sectional design limitations

Our study’s cross-sectional design cannot establish definitive causal relationships. The monthly lag analysis should be interpreted as identifying statistical associations rather than proving causation.

Temporal assumptions

While pulmonary nodules may develop over years, our monthly lag analysis captures the acute effects of air pollution on nodule detection probability rather than the complete development process. This approach is consistent with established practices in environmental epidemiology where GAM methods have been validated for time-series analysis of air pollution health effects.

Detection pathways

Air pollution may influence nodule detection through multiple pathways: direct effects on nodule development, increased healthcare-seeking behavior due to respiratory symptoms, and enhanced radiological detection due to inflammatory changes.

Unmeasured confounding factors

Several important confounding factors were not available in our study:

Smoking status

The absence of smoking data represents a significant limitation. Smoking is a major risk factor for pulmonary nodules and may be correlated with air pollution exposure through behavioral and socioeconomic pathways. This could potentially lead to overestimation of air pollution effects if smokers are more likely to be exposed to higher pollution levels.

Occupational exposure

Lack of occupational exposure data is another important limitation. Certain occupations (e.g., construction, manufacturing) may involve both higher air pollution exposure and increased risk of respiratory conditions. However, our study population consisted primarily of individuals undergoing routine health examinations, which may represent a relatively homogeneous occupational profile.

Comorbidities

The absence of detailed comorbidity data limits our ability to assess whether air pollution effects vary by underlying health status. However, our study population was drawn from routine health examinations rather than clinical populations, suggesting a relatively healthy baseline.

Socio-economic status

Socio-economic factors may influence both air pollution exposure and healthcare access. Our restriction to urban residents with stable residence (≥ 3 years) may have reduced some socio-economic heterogeneity, but residual confounding remains possible.

Quantitative bias assessment

Based on previous studies, smoking could account for a 2–4 fold increase in pulmonary nodule risk. If smoking rates were 20–30% higher in high-pollution areas, this could explain 10–20% of our observed associations. However, the temporal lag patterns and consistency across subgroups suggest that unmeasured confounding alone cannot fully explain our findings.

Additional technical limitations

The data of atmospheric particle pollutants are obtained from satellite remote sensing monitoring, which may cause measurement errors if the actual exposure of the study population is represented. It is recommended to use spatial modeling and pollutant source analysis to construct the spatial distribution of pollutants in the whole city and improve the accuracy of predicting the effect of air pollutants on the incidence of pulmonary nodules. To improve the generalizability of the present study, ideally, different sites and additional study periods should be investigated and analyzed.

Conclusion

In summary, this study demonstrates and summarizes the following important findings using Shijiazhuang city as a representative case study in northern China:

1. Our analysis identified statistically significant but modest associations between atmospheric particulate pollutants and pulmonary nodule detection, with the strongest associations observed at a 4-month lag period. These findings represent statistical associations rather than definitive causal relationships and should be interpreted with caution given the cross-sectional study design, potential unmeasured confounding, and modest effect sizes with wide confidence intervals.

2. Seasonal variations in pollutant concentrations were observed, with higher concentrations in autumn and winter and lower concentrations in spring and summer. These patterns may contribute to the observed seasonal differences in associations, though the clinical significance of these variations requires further investigation.

3. Modest differences in associations were observed between women and men, with slightly stronger associations in women. However, these differences may reflect unmeasured confounding factors or differential exposure patterns rather than true biological susceptibility differences.

4. Age-stratified analyses suggested potential differences in associations among older adults (≥ 60 years), though the clinical implications of these findings remain uncertain. While these results may inform future research directions, they should not be interpreted as establishing definitive risk profiles for clinical decision-making.

5. The public health implications of our findings, while potentially important at the population level, require validation through prospective studies and comprehensive health impact assessments before informing specific policy recommendations. Our results contribute to the growing evidence base regarding air pollution and respiratory health, but additional research is needed to establish causal relationships and optimal intervention strategies.

Acknowledgements

This work is supported by Tianjin Key Medical Discipline Construction Project (Grant No. TJYXZDXK-3-014C); "Double first-class" discipline construction project; Key Discipline of Tianjin Health Science and Technology Project (TJWJ2022XK007); General Project of Tianjin Multi-Investment Fund for Applied Basic Research (21JCYBJC01610).

Author contributions

Bowen Li: Conceptualization, Writing—original draft, Software, Investigation. Yancui Li: Resources, Investigation, Writing—original draft. Jinguo YUAN: Supervision. Xiaopeng Zhang: Data curation. Chunxiao Wan: Writing—review and editing, Funding acquisition. All the authors reviewed the manuscript.

Data availability

Data availabilityThe datasets generated and/or analysed during the current study are not publicly available due reasons to protect patient privacy but are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics

Written approval of the Institutional Review Board was obtained for this paper. The full name and institution of the review committee and an Ethics Committee reference number: Hebei General Hospital Ethics Committee Application for Approval of Research Protocol. NO.2,022,091. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols have been approved by the Ethics Committee of Hebei Provincial People’s Hospital. Informed consent has been obtained from all subjects and/or their legal guardians.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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