Abstract

Background

Pulmonary space-occupying lesions are typical chronic pulmonary diseases that contribute significantly to healthcare resource use and impose a large disease burden in China. A time-series ecological trend study was conducted to investigate the associations between environmental factors and hospitalizations for pulmonary space-occupying lesions in North of China from 2014 to 2022.

Methods

The DLNM was used to quantify the association of environmental factors with lung cancer admissions. The heating-, age-, gender-, malignancy-specific effects were further estimated to identify the susceptible groups.

Results

During the study period, fluctuations in air pollutants and climate conditions closely mirrored changes in hospitalizations for pulmonary space-occupying lesions. Totally, the distributed lag surface showed clear positive associations between pulmonary tumor hospitalization and PM_2.5 (RR_lag30: 1.000912; 95%CI: 1.000076, 1.00175), PM₁₀ (RR_lag30: 1.002246; 95%CI: 1.000474, 1.004021), SO₂ (RR_lag30: 1.002714; 95%CI: 1.001071, 1.004414), CO (RR_lag30: 1.002231; 95%CI: 1.000592, 1.003873). Additionally, the associations between air pollutants and hospitalizations for pulmonary space-occupying lesions were significantly stronger during the heating season. Population aged 65 or older, females and those diagnosed with malignancies were more vulnerable for the risk of pulmonary space-occupying lesions diseases due to air pollution exposure.

Conclusions

The present study illustrated risk and burden for pulmonary space-occupying lesions hospitalization associated with air pollution, especially among population aged ≥ 65, or female.

Background

The International Agency for Research on Cancer (IARC) recently announced that lung cancer, accounting for 18% of global cancer deaths, remains the leading cause of cancer mortality worldwide [26]. The American Cancer Society reports that lung cancer is the primary cause of cancer-related deaths in the U.S., with over 350 individuals dying from the disease each day [24]. In China, lung cancer also has the highest incidence and mortality rates among all types of cancer [26]. There are multiple environmental factors except genetic ones that have been reported as main causes of lung cancer, including cigarette smoke, radon exposure, and air pollution [17].

Exposure to air pollution has been linked to increased morbidity and mortality, making it a worldwide public health concern. It has been reported that exposure to air pollution is associated with the risk of respiratory diseases such as lung cancer [28, 30]. Air Pollutants contain substantial quantities of particulate matter (PM) and gases, including PM₁₀ (aerodynamic diameter ≤ 10Ìýμm), PM_2.5 (aerodynamic diameter ≤ 2.5Ìýμm), toxic metals, sulfur oxides, nitrogen oxides, and microorganisms, which can be harmful to the lungs. The IARC has classified PM_2.5 as group 1 carcinogen to humans [16]. Radiology-identified lung lesions measuring less than 30Ìýmm in diameter are defined as pulmonary nodules, while those exceeding 30Ìýmm are referred to as masses [6]. It is reported that lung cancer frequently manifests as pulmonary mass lesions. Studies have demonstrated a positive correlation between PM2.5 exposure and the risk of pulmonary nodules in China [3, 7]. Additionally, previous research suggests that PM exposure can cause pulmonary lesions in vivo [18]. Although there are studies showing an association between PM_2.5 and lung cancer, the effect of PM_2.5 on lung cancer varies depending on the concentration of pollution and different pollution sources.

Extensive literature indicates that severe air pollution and increasing haze episodes in China frequently occur in various major cities, especially during the heating season [14, 32]. The significant emissions resulting from coal combustion in winter give rise to the most severe air pollution, as heating is commonly required during this season in northern China. In fact, heating-related energy consumption, which serves as the primary source of haze pollution, shows a very strong correlation with air pollution levels [15]. Compared to the much less polluted summer months, winter air pollution increases the risk of respiratory diseases, particularly in individuals aged 1 to 59Ìýyears [29]. It is hypothesized that lung cancer risk might be elevated during winter compared with summer months. Due to varying levels of physical development across different age groups, sensitivity to air pollution may differ according to age [11, 23]. Additionally, the Global Burden of Disease (GBD) study reported that tobacco smoking remains the leading risk factor for lung cancer, with smoking rates among males remaining higher than among females [21]. Therefore, it is essential to further analyze and predict lung cancer trends based on age and gender.

Class 3 hospitals represent the pinnacle of medical institutions within China’s healthcare system, equivalent to tertiary hospitals in the United States and Europe. These hospitals also provide primary and secondary care for those with minor illnesses. Consequently, hospital admission records can serve as a reliable and timely source of information on the health status of a geographically defined population in China [27].

In this study, we utilized data collected from 2014 to 2022 to The Second Hospital, Hebei Medical University, a Class 3 hospital in China. Information on six different levels of air pollution (PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃) was interpolated from monitoring stations to the residential addresses of individuals at the time of diagnosis. We conducted a distributed lag non-linear model (DLNM) analysis based on hospital admissions to investigate the association between air pollutants exposure and the risk of hospitalization for pulmonary mass lesions. Additionally, we explored risk factors such as heating, age, gender and other demographic characteristics.

Methods

Exposure data collection

Shijiazhuang is a typical inland city located in northern China. Due to its geographical characteristics, with lower elevations in the east and higher elevations in the west, the movement of pollutants is limited, resulting in poor air circulation and pronounced seasonal variation in air quality.

Daily data on air pollution, including PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃, were obtained from Qingyue database of Shijiazhuang city, Hebei. The daily average temperature and daily RH (%) were sourced from the China Meteorological Data Service Center.

Clinical case definition

We obtained records of admission to the Second Affiliated Hospital of Hebei Medical University (1 January 2014 to 31 December 2022). The Second Affiliated Hospital is equipped with an electronic medical record system that can provide daily data for 2014–2022. Therefore, hospital admission data can accurately reflect the condition of hospitalization.

Screening of cases: we pooled patients whose primary diagnoses were pulmonary space-occupying lesions, including lung space occupying lesion (R91. × 00, R91. × 03), lung mass (¸é91. ×â¶Ä‰02), lung malignant tumor (C34.00-C34.902, C85.910), bronchial malignant tumor (°ä33. ×â¶Ä‰00), lung benign tumor (Q85.901, D14.300, J98.409), pulmonary cavity ( J98.410), pulmonary tuberculosis (A15.100-A15.300, A15.407), pulmonary sarcoidosis (D86.000, D86.200), pulmonary atypical hyperplasia(J98.402), and pulmonary hamartoma (Q85.901). All codes mentioned were diagnosed according to the International Classification of Diseases-10 (ICD-10). Here, we present a table that outlines the ICD-10 codes used in our study:

Condition	ICD-10 Code
Lung space occupying lesion	R91. × 00, R91. × 03
Lung mass	¸é91. ×â¶Ä‰02
Lung malignant tumor	C34.00-C34.902, C85.910
Bronchial malignant tumor	°ä33. ×â¶Ä‰00
Lung benign tumor	Q85.901, D14.300, J98.409
Pulmonary cavity	J98.410
Pulmonary tuberculosis	A15.100-A15.300, A15.407
Pulmonary sarcoidosis	D86.000, D86.200
Pulmonary atypical hyperplasia	J98.402
Pulmonary hamartoma	Q85.901

Study design

The ecological trend study was projected to explore the associations of air pollution on lung tumor prevalence. The environmental factors and corresponding daily admissions were arrayed for the time-series analysis. Taking account of heterogeneity of subcluster, we employed stratified analysis based on time-series to clarify the associations of environmental factors with pulmonary tumor explicitly. Subgroup population included: heating season (November 1 to March 31), age (< 65 y and ≥ 65 y), gender (female and male), malignancy.

Data analysis

The DLNM (Distributed Lag non-Linear Model) [5] was used to quantify the association of environmental factors with lung cancer admissions. In the DLNM models, hospitalizations for pulmonary space-occupying lesions were considered as dependent variables, and a cross-basis function of PM_2.5, PM₁₀, SO₂, NO₂, CO, O₃, temperature and humidity built by DLNM was introduced as the fixed-effect independent variable. In the cross-basis function, we empirically selected a maximum lag of 30Ìýdays. Subsequently, the cross-basis function was incorporated into the quasi-Poisson regression models. We adjusted for the long-term trend and day of the week by employing a natural cubic spline with 7 df of freedom per year for time. This approach is flexible enough to simultaneously capture the non-linear relationship of exposure–response and the delayed effects. The median of PM_2.5 (50Ìýμg/m³), PM₁₀ (100Ìýμg/m³), SO₂ (15Ìýμg/m³), NO₂ (40Ìýμg/m³), CO (0.8Ìýmg/m³), O₃ (75Ìýμg/m³), temperature (15℃) and humidity (58%) were computed to assess the lag-response effects of environmental factors on Pulmonary tumor. A 2-degree B spline distribution lag model was adopted to take into account the potential non-linear relationships among the different lag days. A 2-degree B-spline provides an excellent balance between flexibility and smoothness, making it well-suited for capturing nonlinear relationships. This spline type helps to avoid over-fitting while maintaining robustness against extreme data points. B-splines are known for their numerical stability, especially when dealing with long lag periods. This stability helps maintain reliable estimation results in high-dimensional settings [1].

The associations between PM_2.5, PM₁₀, SO₂, NO₂, CO, O₃, temperature and humidity (for each unit increase, respectively) with lung tumor admissions was presented by relative risk: RR [95% confidence intervals (CIs)]. The 3-D graph was employed to exhibit the associations of air pollution with the hospital admissions of pulmonary tumor from lag 0 to 30Ìýdays. The RR value changes and 95% confidence interval of the lag response curve of various environmental factors on lung tumor admissions with different lag days was computed. Exposure response curves for the associations of environmental factors with the hospital admissions of pulmonary tumor at lag 1 and lag 30 were presented in further detail. The heating-, age-, gender-, malignancy-specific effects were further estimated to identify the susceptible groups.

The R software (version 4.3.1) was used to perform all statistical tests and modeling. Distributed lag nonlinear models were fitted using the "dlnm" package.

Results

Descriptive statistics

From 2014 to 2022, there were 8588 reports of pulmonary tumor and 71.18% of admitted patients suffered malignant tumor, especially lung malignant tumor (70.45%). The proportion of males was higher than that of females with male–female ratios of at least 1.82: 1 among admissions. The composition was higher in people younger (< 65Ìýyears) than in those 65Ìýyears and older (≥ 65Ìýyears old). Admissions in heating days accounted for 37.67% of the overall pulmonary tumor patients (TableÌý1).

Table 1 Characteristics of participants included in the study

Hospital admissions, air pollutants (SO₂, NO₂, CO, O₃, PM₁₀, PM_2.5) and climates (RH, Temperature) trends were assessed in terms of average daily change in Fig.Ìý1. The hospitalization rate fluctuates over time, but due to the lag effect, it does not completely align with the fluctuations in pollutants and meteorological conditions.A seasonal trend of elevated SO₂ in winter was observed, but as going further into 2017, we failed to see that anymore. Mean daily ambient SO₂ was 28.47Ìýμg/m³, ranged between 9.79Ìýμg/m³ (25th percentile) and 34.00Ìýμg/m³ (75th percentile) and peaked at 332Ìýμg/m³ (TableÌý2). On top of this overall study, we found significant difference between heating and non-heating period. The highest values occurred during the heating season and at least 25% was above 40Ìýμg/m³ (WHO Global Air Quality Guidelines) (WHO, 2021). NO₂ levels (total: 43.95Ìýμg/m³; heating: 55.96Ìýμg/m³) were higher than 25Ìýμg/m³ which was accounted by WHO for roughly 25% of our duration. The CO trend was consistent with that of SO₂ and maintained a mild pollution level (1.13Ìýmg/m³). Contrary to SO₂, O₃ went through a seasonal increase in summer and at least 50% (107.47Ìýμg/m³) was over 100Ìýμg/m³ (WHO Global Air Quality Guidelines) in non-heating period. As similar with SO₂, PM₁₀ and PM_2.5 had seasonal elevations in winter. Specifically for heating days, mean daily ambient PM₁₀ (175.77Ìýμg/m³) and PM_2.5 (105.31Ìýμg/m³) were well exceeded 3.8 and 7 times of WHO limitations, respectively. Mean daily ambient RH and temperature were 57.47% and 13.70℃ respectively.

Fig.Ìý1

Characteristics of hospital admissions of pulmonary tumor, air pollution, and weather conditions during the study period (from 2014 to 2022)

Table 2 Baseline Characteristics of various environmental factors in Shijiazhuang City from 2014 to 2022

Relationship between air pollutants or climates and the hospital admissions

The 3-D plots of relative risks (RRs) of pulmonary tumor admissions along single-pollutant models over lag0 ~ lag30 days were showed in Fig.Ìý2. The distributed lag surface revealed a distinctively positive relationship of pulmonary tumor hospitalization with air pollutants such as PM_2.5, PM₁₀, SO₂, and CO, but not RH, and Temperature. The most intensive efficiency stage was lag30 for PM_2.5 (RR: 1.000912; 95%CI: 1.000076, 1.00175), lag30 for PM₁₀ (RR: 1.002246; 95%CI: 1.000474, 1.004021), lag0 for O3 (RR: 1.001056; 95%CI: 1.000246, 1.001868), lag30 for SO₂ (RR: 1.002714; 95%CI: 1.001071, 1.004414), lag30 for CO (RR: 1.002231; 95%CI: 1.000592, 1.003873), respectively (Fig.Ìý3). Exposure–response curves at lag1 and lag30 of PM_2.5, PM₁₀, O₃, SO₂, CO, NO₂, RH, Temperature were showed in Fig. S1. The RRs showed an upward trend with the increase of concentrations of PM_2.5, PM₁₀, O₃, SO₂, CO, NO₂, RH, but not temperature.

Fig.Ìý2

The 3D graph for the associations of air pollution with the hospital admissions of pulmonary tumor from lags 0 to 30Ìýdays

Fig.Ìý3

The RR value changes and 95% confidence interval of the lag response curve of various environmental factors on lung tumor admissions with different lag days. Abbreviations: RR, relative risk; RH, Relative humidity

Potential modification by heating and non-heating period on the relationship of air pollutants or climates with the hospital admissions

The 3-D plots showed the non-linear associations between air pollutants (SO₂, NO₂, CO, O₃, PM₁₀, PM_2.5) or climates (RH, Temperature) and pulmonary tumor admissions (Fig. S2). The relationship between air pollutants or climates and the hospital admissions varied strongly in heating season. We observed potential modification on the association of air pollutants or climates with lung tumor admissions by heating season (Fig.Ìý4). The lag30 effects of PM_2.5, PM₁₀, SO₂, CO on lung tumor admissions were significant in the heating season [RR: 1.01486 (95%CI: 1.001952, 1.027934); 1.014272 (95%CI: 1.001686, 1.027015); 1.016187 (95%CI: 1.001035, 1.031569); 1.02352 (95%CI: 1.007391, 1.039906)], respectively. Exposure–response curves of heating season at lag1 (Fig. S3) and lag30 (Fig. S4) showed that the increased concentrations of PM_2.5, PM₁₀, SO₂, CO, but not O₃ presented the rising RRs.

Fig.Ìý4

Lag structures for the associations of environmental factors with the hospital admissions of pulmonary tumor by heating and non-heating period. Note: The heating season is selected from January 1st to March 31st and November 1st to December 31st each year, with other times being non heating seasons. A₁-H₁ represents the heating season, A₂-H₂ represents the non-heating season

Potential modification by age on the relationship of air pollutants or climates with the hospital admissions

The 3-D plots distinguished by age of 65Ìýyears demonstrated the association between air pollutants (SO₂, NO₂, CO, O₃, PM₁₀, and PM_2.5), climates (RH, Temperature) and admissions of pulmonary tumor over the lags of 0–30Ìýdays (Fig. S5). At a lag of 24 to 30Ìýdays, the adverse effects of SO₂ on admissions emerged both in age < 65Ìýyears old [from 1.001179 (1.000045, 1.002314) to 1.002665 (1.000468, 1.004866)] or in ≥ 65Ìýyears old [from 1.001584 (1.000347, 1.002822) to 1.002722 (1.000284, 1.005167)] (Fig.Ìý5) and upward trend with increase in concentrations of SO₂ were observable (Fig. S6, Fig. S7).

Fig.Ìý5

Lag structures for the associations of environmental factors with the hospital admissions of pulmonary tumor by age < 65 or ≥ 65. A₁-H₁ represents the age < 65, A₂-H₂ represents the age ≥ 65

Potential modification by gender on the relationship of air pollutants or climates with the hospital admissions

The gender-specific 3-D plots demonstrated the association between air pollutants (SO₂, NO₂, CO, O₃, PM₁₀, and PM_2.5), climates (RH, Temperature) and admissions of pulmonary tumor over the lags of 0–30Ìýdays (Fig. S8). For the short lags {O₃: lag 0–5 [from 1.001554 (1.000299, 1.002811) to 1.000783 (1.000107, 1.001459)], RH: lag 5–11 [from 1.00004 (1.000003, 1.000078) to 1.00034 (1.000002, 1.000067)]}, gender effects (female) were apparent to admissions (Fig.Ìý6). The gender differences of the adverse effects of PM₁₀ and SO₂ on admissions were found at lag days, and the difference was that the effect appeared earlier in females [PM₁₀: lag0 (RR: 1.003161; 95%CI: 1.000379, 1.00595), SO₂: lag0 (RR: 1.003185; 95%CI: 1.000257, 1.006122)] than in males [ PM₁₀: lag20 (RR: 1.001198; 95%CI: 1.000027, 1.002371), SO₂: lag23 (RR: 1.001052; 95%CI: 1.000078, 1.002027)] (Fig.Ìý6). The ascending RR of pulmonary tumor admissions were associated with the increasing PM₁₀, SO₂, O₃ concentration and RH (Fig. S9, Fig. S10).

Fig.Ìý6

Lag structures for the associations of environmental factors with the hospital admissions of pulmonary tumor by gender. A₁-H₁ represents the male, A₂-H₂ represents the female

Associations of air pollutants or climates with the malignant admissions

For specific malignancies, longer lag effects were observed between air pollutants or climates and admissions { PM_2.5: lag23-30 [ from 1.000504 (1.000003, 1.001006) to 1.000989 (1.000044, 1.001934)], PM₁₀: lag18-30 [ from 1.001192 (1.000057, 1.002328) to 1.00258 (1.000585, 1.00458)], SO₂: lag23-30 [ from 1.001239 (1.000352, 1.002126) to 1.003103 (1.001285, 1.004925)], CO: lag20-30 [ from 1.001042 (1.000051, 1.002034) to 1.002613 (1.000768, 1.004461)]} (Fig.Ìý7). O₃, in contrast, showed a distinct acute effect [ lag0-4 from 1.001071 (1.00016, 1.001982) to 1.000565 (1.00055, 1.001108)] to the admissions of malignancy. An ascending exposure–response relationship was exhibited for PM_2.5, PM₁₀, SO₂, CO, and O₃ (Fig. S11).

Fig.Ìý7

Lag structures for the associations of environmental factors with the hospital admissions of pulmonary malignancy tumor

Discussion

In the present study, a time-series, ecological trend study was performed with the intention of investigate the associations between environmental factors and hospitalizations for pulmonary space-occupying lesions in North of China during 2014 and 2022. Pulmonary space-occupying lesions were not acute diseases but chronic diseases related with physiological responses like inflammation or immune reactions. Therefore, we explored the lag-30 effects. During the study, the median concentration of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ in Shijiazhuang were 50Ìýμg/m³, 100Ìýμg/m³, 15Ìýμg/m³, 40Ìýμg/m³, 0.8Ìýmg/m³, 75Ìýμg/m³, respectively, as reported by the environmental protection administration’s monitoring stations. Meanwhile, the ups and downs of air pollutants and climates matched exactly the changes of hospitalizations for pulmonary space-occupying lesions. Totally, positive associations between air pollutants (PM_2.5, PM₁₀, O₃, SO₂, CO) and hospitalizations for pulmonary space-occupying lesions were observed. Additionally, the associations of air pollutants or climates with the hospital admissions varied strongly in heating season. In gender-specific subgroup analysis, the adverse effects of PM₁₀ and SO₂ on admissions were that the effect appeared earlier in females; meanwhile, moderate larger effects in the elderly in comparison with younger group were observed. For specific malignancies, longer lag effects were observed between air pollutants or climates and admissions.

Based on increasing evidence of the connection between air pollution and disease, the annual average Air Quality Guidelines (AQG) for PM_2.5, PM₁₀, and NO₂ have been revised from 10Ìýμg/m³ to 5Ìýμg/m³, 20Ìýμg/m³ to 15Ìýμg/m³, and 40Ìýμg/m³ to 10Ìýμg/m³, respectively. Additionally, the 24-h limits for SO₂ and CO are set at 40Ìýμg/m³ and 4Ìýmg/m³, respectively. Furthermore, the WHO has proposed a new AQG level, including a peak seasonal average O₃ concentration of 60Ìýμg/m³. Long-term exposure to atmospheric air pollution contributes to the global disease burden and is associated with an increase in morbidity and mortality from respiratory diseases, particularly lung cancer, which manifests as pulmonary mass lesions. Due to the geographical feature of the low eastern and high western depressions, Shijiazhuang has poor pollutant mobility, resulting in significant seasonal variations in air quality.Time series analysis showed two peaks in hospitalizations for pulmonary space-occupying lesions during winter and spring, corresponding closely to fluctuations in air pollutants and weather conditions. In this study, the distributed lag surface demonstrated a clearly positive association between pulmonary space-occupying lesion hospitalization and air pollutants (PM_2.5, PM₁₀, SO₂, CO), while no such association was observed with relative humidity (RH), or temperature. Our previous study demonstrated pulmonary nodule prevalence, a kind of pulmonary mass lesions, were positively associated with PM_2.5 (OR = 1.06, 95% CI: 1.02, 1.11) [19]. A UK Biobank study has demonstrated that increases in PM_2.5 levels (at 1Ìýμg/m³ increments) were associated with a higher risk of lung cancer incidence (HR = 1.08, 95% CI: 1.04, 1.12) [7]. A study based on Beijing Urban Employees Basic Medical Insurance (UEBMI) system have demonstrated that exposure to per 1Ìýμg/m³ increase ambient PM₁₀ (RR = 1.010, 95% CI: 1.005, 1.014), SO₂ (RR = 1.034, 95% CI: 1.011, 1.058), and RH (RR = 1.023, 95% CI: 1.008, 1.039) was associated with lung cancer hospitalization, but failed to observed this association with NO₂ and temperature [27]. Differences in air pollution sources may have contributed to the conflicting findings in the literature and the current study.

For the associations of air pollutants or climates with the hospital admissions, the increasing magnitude of RRs in heating season was higher and stronger than those in non-heating, indicating the possible adverse effect modifications by the policy of heating in pulmonary space-occupying lesions hospitalization risk and burden due to severe air pollution. In China, heating, usually lasts from November to March, lead to the large amount emissions from the coal combustion, resulting severe air pollution. A case–control study in Nepal found that using solid fuel for heating was associated with a 3.45-fold increased risk of tuberculosis (95% CI: 1.44, 8.27) [20]. In addition, a prospective study conducted by China Kadoorie Biobank (CKB), in which 3288 individuals were diagnosed with lung cancer during the follow-up, has demonstrated that solid fuel heating is associated with an increased risk of lung cancer (HR: 1.25, 95% CI: 1.08, 1.46) [9]. Thus, new heating technology using clean energy is urgently needed.

The RR of the associations of air pollution with pulmonary space-occupying lesions hospitalization were moderate larger in the elderly in comparison with younger group, suggesting that individuals aged 65Ìýyears or older are more susceptible to heat. The elderly have been identified as a susceptible population in previous studies [4, 12, 22]. In older people, the prevalence of preexisting respiratory diseases is higher than that of younger people,therefore, there is a degree of overlap between the elderly and those who are potentially susceptible to pulmonary disease [10]. However, in this study, the difference of pulmonary space-occupying lesions hospitalization risk in different age was very small, indicating that the age of pulmonary space-occupying lesions hospitalization morbidity is going to be younger.

The lag-response of pollutants-hospitalization associations for pulmonary space-occupying lesions diseases appeared earlier in female than that in males, indicating that females are more susceptible to air pollution exposure. In epidemiological studies contributing to the systematic reviews and meta-analyses used in the GBD 2010 study, the association between household air pollution and lung cancer prevalence showed that PM2.5 was associated with a combined OR of 1.81 (95% CI: 1.07, 3.06) for females and 1.26 (95% CI: 1.04, 1.52) for males [2, 25], which is in line with our study. Physiological and behavioral factors might explain the earlier pollutants-hospitalization association in females. The gender difference may partially attributed to the varying deposition of particles in the lungs between males and females, with females having a greater lung deposition fraction of 1Ìýμm particles in all regions [13]. Furthermore, females exhibit slightly greater airway reactivity compared to males, as well as narrower airways,consequently, greater effects of PM₁₀, SO₂, and NO₂ on respiratory mortality were observed in females than in males [10]. Our results and other evidence suggested that the males have adapted to air pollution exposure better than females.

Besides, for specific malignancies, in this study, earlier and longer lag effects of air pollution were observed than that for pulmonary space-occupying lesions hospitalization. An experiment on human non-small cell lung cancer A549 cells demonstrated that low concentrations of PM₁₀ and SO₂ resulted in synergistic damage to cell survival and apoptosis [31]. A prospective study utilizing the UK Biobank revealed significant associations between the risk of lung cancer and PM_2.5 (HR = 1.63; 95% CCI: 1.33, 2.01; per 5Ìýμg/m³), PM₁₀ (HR = 1.53; 95% CCI: 1.20, 1.96; per 10Ìýμg/m³), NO₂ (HR = 1.10; 95% CI: 1.05, 1.15; per 10Ìýμg/m³), and NOx (HR = 1.13; 95% CI: 1.07, 1.18; per 20Ìýμg/m³) [8]. Preexisting respiratory conditions, more susceptible to air pollution exposure, could explain the earlier and longer lag effects in malignancies hospitalizations.

There are also some limitations. First, the study data were from one hospital in Shijiazhuang, which could introduce selection bias. Secondly, the assessment of air pollution exposure was based on the average data from air pollution monitoring stations in Shijiazhuang, which may not accurately reflect individual exposure levels. However, most patients admitted to the Second Affiliated Hospital of Hebei Medical University lived in Shijiazhuang. Finally, pulmonary space-occupying lesions encompass a group of diseases with varying etiology and pathogenesis. This study focused solely on the overall prevalence of pulmonary space-occupying lesions, without exploring the impact of air pollutants on specific types.

Conclusion

The present study illustrated risk and burden of hospitalization for pulmonary space-occupying lesions associated with air pollution, especially during the heating season. This study provides preliminary evidence of the respiratory damage caused by heating, particularly solid fuel heating in Hebei. Besides, population aged 65 or older, females, or those diagnosed with malignancies were found to be more vulnerable for the risk of pulmonary space-occupying lesions diseases due to air pollution exposure. This research has important public health implications and highlights the need for global policies to manage air pollution, which could offer health benefits to these vulnerable groups.