Short-term association of PM2.5/PM10 on lung cancer mortality in Wuhai city, China (2015–2019): a time series analysis : European Journal of Cancer Prevention

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Lung cancer

Short-term association of PM2.5/PM10 on lung cancer mortality in Wuhai city, China (2015–2019): a time series analysis

Liu, Yinga,*; Liu, Changhaia,*; Song, Yangb; Zhao, Jinga; Gao, Yumina; Zhao, Lingyana; Cao, Ninga; Xu, Dannia; Li, Hongweia; Li, Hailinga

Author Information
European Journal of Cancer Prevention: November 2022 - Volume 31 - Issue 6 - p 530-539
doi: 10.1097/CEJ.0000000000000764
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Abstract

Introduction

Air pollution has already become one of the public concern in the modern era. As the primary contaminant in air pollution, particulate matter has prompted a slew of health problems for the population (Iriti et al., 2020). Particulate matter is generated mostly by human activities and natural processes (Guo et al., 2019). Over 2013-2015, the annual average concentration of short-time fine particulate matter (PM2.5) in China has exceeded the WHO air quality guidelines (air quality standards, 0-10μg/m3), and the concentration of PM2.5-10 (between 2.5 and 10 μm in diameter) was higher in the north than in the south (Chen et al., 2017). Regional aerosols, biomass burning, and gasoline combustion accounted for 70% of particulate matter generation creating health problems for humans (Sadeghi et al., 2020).

However, the risk of particle matter proving hazardous to human health is not negligible. Environmental PM was ranked as the fourth risk factor for disease burden in China, accounting for around 0.90 million premature deaths per year (GBD 2015 Risk Factors Collaborators). A study of 272 cities in China observed that each 10-μg/m3 increase in PM2.5 concentration was associated with a significant increase in mortality from cardiovascular disease (0.27%) and chronic obstructive pulmonary disease (0.38%)(Chen et al., 2017). Particulate matter had also been attributed to cardiovascular disease and bad pregnancy outcomes in New York and Canada, according to studies (Enders et al., 2019; Chen et al., 2020; Croft et al., 2020; Shahpoury et al., 2021). Further epidemiological studies have revealed that PM increases morbidity and mortality associated with a wide variety of respiratory disorders, most notably lung cancer (Santibáñez-Andrade et al., 2019). Similarly, research by Hsu et al. (2019) indicated that PM may contribute to human oxidative stress, inflammatory response, fibrosis, and cancer. Lung cancer was one of the five leading causes of death in terms of years of life lost (Zhou et al., 2019). As a result, it is vital to examine the connection between particulate matter and lung cancer mortality in order to strengthen ambient air pollution management strategies and limit the harm caused by atmospheric pollutants to individuals.

In summary, time series analysis can be required to evaluate the short-term influence of particulate matter and lung cancer mortality in Wuhai, China, where coal mining was a pillar industry. This work may serve as a foundation for further understanding the mechanism of lung cancer production and the formulation of public health interventions related to air pollution.

Materials and methods

Study area

Wuhai is a city in Inner Mongolia Autonomous Region, China (39°04′–39°92′N, 106°60′–107°14′E), which covers an area of 1754 km2. Located in the continent’s mid-latitude zone, Wuhai City is at the confluence of three deserts, and its climate is characteristic of the semiarid and semihumid sections of the middle temperate zone. Owing to its inland location and little rainfall, the yearly average precipitation is only 160 mm, yet the annual average evaporation can reach 3300 mm. Wuhai is also a new industrial city in Northwest China, focusing on industrial production. The primary causes of pollution are heavy industrial pollution from sources such as energy, manufacturing, and metal smelting, as well as home waste gas pollution (Zhang et al., 2021). Given the city’s unique geographical setting, its topological layout of ‘three mountains and two valleys’ is not favorable to pollution dilution and dispersal. These factors have exerted significant pressure on the ecosystem.

Mortality data

Daily lung cancer mortality data were obtained from the Wuhai Center for Disease Control and Prevention’s Cause of Death Registration and Reporting Information System for the period 1 January 2015–31 December 2019. We collected information on the age, sex, and date of death of patients with lung cancer classified as C33-C34 by the International Classification of Diseases10 (WHO, 2016).

Ambient air pollutant data

The air pollution data came from hourly feedback from three Environmental Monitoring Center-operated monitoring sites in Wuhai (Municipal Forestry Bureau, Juying Middle School, and Zhonghaibowan School). We collected 24-h averages of PM2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). PM2.5 and PM10 were absent from the air pollutant data for 45 days, whereas O3 was missing for 12 days. EM interpolation was used to fill in the missing numbers (Song and Wan, 2020).

Meteorological data

Meteorological data were collected from the China Meteorological Data Network (http://data.cma.cn/), which primarily collected 24-h average temperature and pressure intensity data (hPa). No meteorological data were omitted.

Statistical analysis

Daily lung cancer fatalities, meteorological conditions, and air contaminants were all described statistically. Additionally, the Spearman rank correlation analysis was used to analyze the relationship between climatic parameters and air contaminants. Given the fact that the daily count of each lung cancer death outcome follows an over-dispersed Poisson distribution, a generalized additive models (GAM) with a Quasi-Poisson connection function was used (Feng et al., 2019). As a result, we employed a time series analysis methodology that included GAM and Quasi-Poisson regression to investigate the correlations between short-term exposure to air pollution and daily lung cancer mortality. This strategy has been frequently employed in past time series analyses (Liu et al., 2019; Qi et al., 2020; Zhou et al., 2021). Hence, the fundamental model is as follows:

LogE(Yt) = α + β*Cpollution + ns(time,df) + ns(temperature,df) + ns(pressure,df) + DOW

In the above formula, Yt denotes lung cancer mortality on day t, E(Yt) denotes the expected lung cancer mortality rate on day t, α denotes the residual, and β denotes the regression coefficient. Cpollution denotes the concentration of air pollutants, and ns() denotes a smooth function constructed with natural cubic splines (Tian et al., 2019), which is used to control the effect of nonlinear confounding variables such as temperature and pressure intensity on mortality. The degree of freedom is denoted by df, and DOW (day of the week) is the variable determining the influence of the day of the week. The model can modify temperature and pressure as meteorological variables that may affect pollution.

We began by running a complete model, including only meteorological variables, and then proceeded to choose variables based on a stepwise backward procedure to get the optimal model. The General Cross Verification Intersection (GCV) was used as a selection criterion to determine the best basic model with the lowest GCV (Talmoudi et al., 2017). Finally, the model was rebuilt using the daily average temperature and pressure. According to prior study, time has a degree of freedom of 7/year, whereas meteorological elements have a degree of freedom of 3 (Feng et al., 2019; Sun et al., 2020).

The present study examined the link between air pollution and lung cancer death rates using two categories of indicators: single-day lag effect and multiday cumulative average lag effect. The daily lag concentrations of pollutants are lag 1-lag 7, where lag0 indicates the pollution level on the day of death, lag1 represents the pollution level the day before death, and so on. Use lag 01-lag 07 to suggest the cumulative average lag effect over many days, where lag 01 is the average pollutant concentration on the day of death, lag 02 is the average pollutant concentration on the day before deaths, and so forth. To calculate the impact of varying lag days, the single-day lag concentration and multiday cumulative lag concentration of pollutants were added into the model (Cheng et al., 2019; Wang et al., 2021).

After establishing the optimal lag days for each pollutant in the single-pollutant model, extra air pollutants from the same time were introduced to test the stability of their association.

Further explore the connection between pollutant concentrations and lung cancer mortality after controlling for sex, age, and seasons.

The relationship between air pollution and lung cancer mortality was assessed by calculating the excess risk (ER) and 95% confidence interval (CI) for each 10 μg/m3 rise in PM2.5/PM10 concentrations. ER = (exp (Δc*β) − 1) × 100%, ER(95% CI) = (exp (Δc × (β ± 1.96SE) − 1) × 100%, where Δc denotes the increased unit pollutant concentration and SE is the standard error of the pollutant concentration data.

The statistical analysis was carried out using the R4.1.0 software (Statistical Computing, Vienna, Austria), and GAM was run using the MGCV package (Simon Wood, Bath, UK).

Results

Descriptive statistics

Table 1 summarized the daily deaths from lung cancer, air pollution, and meteorological conditions. Between 2015 and 2019, this study documented a total of 1271 lung cancer deaths in Wuhai city. The daily average number of lung cancer deaths was 0.70, and Males (0.49) had a greater daily death number than females (0.20). During the research period, the daily average values of air pollutants were 41.94 μg/m3 (ranging from 9.00 to 274 μg/m3) for PM2.5 and 105.02 μg/m3 (ranging from 19.00 to 581 μg/m3) for PM10. The daily mean temperature and pressure were, respectively, 10.93 °C (range: −16.90 °C to 30.70 °C) and 892.57 hPa (range: 827.00–911.00 hPa).

Table 1 - Daily lung cancer mortality, air pollution, and meteorological conditions in Wuhai, China, 2015–2019
Variables Mean SD Min P25 P50 P75 Max IQR
Daily deaths from lung cancer
Total 0.70 0.86 0.00 0.00 0.00 1.00 5.00 1.00
Sex
Male 0.49 0.74 0.00 0.00 0.00 1.00 4.00 1.00
Female 0.20 0.45 0.00 0.00 0.00 0.00 3.00 0.00
Age
<65 0.23 0.48 0.00 0.00 0.00 0.00 4.00 0.00
≥65 0.47 0.69 0.00 0.00 0.00 1.00 4.00 1.00
Air pollutants
PM2.5(μg/m3) 41.94 25.09 9.00 25.00 36.00 50.00 274.00 25.00
PM10(μg/m3) 105.02 62.41 19.00 63.00 93.00 128.00 581.00 65.00
Meteorological conditions
Temperature (°C) 10.93 11.56 −16.90 0.50 12.45 21.43 30.70 20.90
Pressure (hPa) 892.57 7.81 827.00 887.0 893.00 898.00 911.00 11.00

Time series analysis

Figure 1 depicted the trends in air pollution concentrations and mortality from lung cancer over the study period. It revealed that PM2.5/PM10 concentrations were higher in the spring and winter, whereas the daily deaths remained stable.

F1
Fig. 1:
Time trends for PM2.5, PM10, and lung cancer fatalities in Wuhai city from 2015 to 2019.

Correlation analysis

Figure 2 depicts Spearman’s correlation between pollutants and meteorological variables. Pressure correlated positively with NO2 and CO, but negatively with PM2.5, PM10, SO2, and O3 (P < 0.01). Apart from the positive association with O3, temperature (temp) had a negative correlation with the remaining five pollutants (PM2.5, PM10, NO2, SO2, and CO) (P < 0.01). On the contrary, all air contaminants except O3 were positively correlated with one another. Notably, a strong association existed between PM10 and PM2.5, NO2 and CO (r = 0.85 and r = 0.88, respectively) (P < 0.01).

F2
Fig. 2:
Analysis of Spearman correlations between air contaminants and meteorological variables in Wuhai City.

Lagged effects of PM2.5/PM10 on lung cancer mortality

The correlations of PM2.5/PM10 with mortality from lung cancer varied across delays in the single-day lag model of the lag table (Table 2). On lag 1, lag 5, lag 01, lag 03, lag 04, lag 05, lag 06, and lag 07 for PM2.5 outcomes, and on lag 05, lag 06, and lag 07 for PM10 outcomes, there were significant relationships. Then, for a 10 μg/m3 rise in PM2.5 concentrations, the largest estimate was 7.92% (95% CI, 2.22–13.95%) (P < 0.01), whereas the largest estimate for PM10 was 2.44% (95% CI, 0.32–4.62%) (P < 0.05).

Table 2 - Excess risk percentage and 95% confidence interval for lung cancer mortality associated with a 10 μg/m3 rise in PM2.5 and PM10, respectively, using the single-day lag model
Lag PM2.5 PM10
Excess risk (%) (95% confidence interval [%]) Excess risk (%) (95% confidence interval [%])
0 1.34 (−1.33 to 4.08) 0.58 (−0.45 to 1.62)
1 3.51 ** (0.92–6.16) ** 0.70 (−0.29 to 1.71)
2 0.41 (−2.20 to 3.09) 0.45 (−0.55 to 1.45)
3 1.63 (−0.91 to 4.23) 0.67 (−0.30 to 1.66)
4 0.90 (−1.63 to 3.50) 0.04 (−0.97 to 1.05)
5 2.82 * (0.34–5.36) * 0.76 (−0.20 to 1.74)
6 1.24 (−1.32 to 3.86) 0.57 (−0.41 to 1.56)
7 −0.56 (−3.16 to 2.11) −0.23 (−1.25 to 0.80)
01 3.78 * (0.48–7.18) * 1.03 (−0.26 to 2.32)
02 3.68 (−0.14 to 7.64) 1.25 (−0.23 to 2.76)
03 4.60 * (0.31–9.06) * 1.64 (−0.02 to 3.31)
04 5.04 * (0.33–9.96) * 1.59 (−0.22 to 3.44)
05 6.98 ** (1.78–12.43) ** 2.09 * (0.12–4.10) *
06 7.92 ** (2.22–13.95) ** 2.44 * (0.32–4.62) *
07 7.77 * (1.57–14.34) * 2.32 * (0.04–4.66) *
*P < 0.05.
**P < 0.01.

In the two-pollutant model, Table 3 specifies the percentage changes in lung cancer mortality estimates associated with PM2.5 and PM10. When PM2.5 was adjusted for PM10 and O3, the risks with lung cancer mortality diminished, as did the connections with SO2, NO2, and CO, although only PM10 remained significant. For PM10, however, all estimates declined somewhat and remained statistically significant, even after correcting for PM2.5. As a result, when the effects of other contaminants were eliminated, the single-day model remained constant, indicating that the associations between air pollution and lung cancer death rates.

Table 3 - Excess risk percentage with 95% confidence interval for the two-pollutant model with PM2.5 and PM10
Pollutant Excess risk (%) (95% confidence interval [%])
PM2.5 7.92 ** (2.22–13.95) **
+PM10 6.87 (−1.52 to 15.97)
+NO2 8.83 ** (2.99–14.99) **
+SO2 8.92 ** (3.10–15.08) **
+CO 11.05 ** (3.68–18.95) **
+O3 7.81 ** (2.10–13.83) **
PM10 2.44 * (0.32–4.62) *
+PM2.5 0.51 (−2.62 to 3.74)
+NO2 2.51 * (0.39–4.69) *
+SO2 2.63 * (0.49–4.82) *
+CO 2.45 * (0.32–4.62) *
+O3 2.52 * (0.26–4.84) *
*P < 0.05.
**P < 0.01.

Subgroup analysis

In Fig. 3, we performed stratification analyses by age (<65 and ≥65 years), sex (male and female), and season (warm: May–October; cold: November–April the following year) to identify potential effect modifiers for the associations between PM2.5/PM10 and lung cancer mortality derived from the single-pollutant model. In sex stratification analysis, PM2.5 affects both men and women, and men are exposed to pollutants for a greater number of significant days than women. PM10 has an effect on men but has no effect on women; the longest lag time for PM10 was lag06, indicating that the ER of males increased by 2.99% for every 10 μg/m3 rise in pollutants (P < 0.05). Similarly, when age was added, both PM2.5 and PM10 had a significant influence on the elderly (≥65), but had no effect on the young (<65). Moreover, the most significant day on which they had an effect was lag06 (ER%, 11.12; 95% CI, 4.22–18.48%; ER%, 3.53; 95% CI, 1.00–6.12%) (P < 0.01). In both the cold and warm seasons, the largest lag day for PM2.5 was lag5 (ER%, 3.97; 95% CI, 0.98–7.04%); however, in the warm season, it was lag06 (ER%, 21.71; 95% CI, 7.46–37.85%) (P < 0.01). PM10 had no influence on lag07 during the cold season but had an effect on lag0, lag01, lag02, lag03, lag04, and lag05 during the warm season. And the longest lag day was lag05 (ER%, 4.30; 95% CI, 0.47–8.28%) (P < 0.05). There was no discernible difference in the effects of pollutants on lung cancer mortality by age or season.

F3
Fig. 3:
The excess risk associated with a 10 μg/m3 rise in PM2.5 and PM10 lung cancer mortality in Wuhai from 2015 to 2019.

Discussion

According to statistical analysis, pollutant concentrations of PM2.5/PM10 in Wuhai City between 2015 and 2019 exceeded the secondary standard of China’s environmental Quality Standards (GB3095-2012) and were significantly higher than the WHO’s Air Quality Guideline (WHO, 2006), whose standard was more stringent than the domestic standard. The time series diagram shows that particulate matter concentrations have a seasonal distribution and that pollutant concentrations have a progressive reduction over the study period. As a coal-based industrial city, the main source of particulate matter is from industrial and mining land, as well as the local way of heating methods. Located in the continent’s interior, it features a dry winter climate, little precipitation, and sparse vegetation. Summer and autumn temperatures are warm, and rainfall is abundant. Likewise, the topography is characteristic of three mountains and two valleys, and air pollutants are difficult to spread, resulting in severe particulate pollution in this area, which has a clear seasonal distribution tendency. Meanwhile, Wuhai’s environmental governance and urban transformation management methods have been linked to a decline in particulate matter concentration in recent years. Further, it is compatible with the current state of pollution in the Beijing-Tianjin-Hebei region and nearby cities (Zhao and Xu, 2021). As noted previously, enhancing pollution management methods is critical for reducing public health damage.

The results of the single-pollutant model indicated a statistically significant correlation between PM2.5/PM10 and lung cancer mortality. At the moment, PM2.5 poses a bigger risk to the public than PM10. Similar findings have been observed in prior research. Investigations by Zhang et al. (2020) and Wang et al. (2019) established a relationship between particulate matter and the incidence and death of lung cancer. Research by Coleman et al. (2020) confirmed a consistent link between PM2.5 and an increased risk of lung cancer. Li et al. (2020) in China similarly demonstrated an elevated risk of lung cancer morbidity and mortality as a result of high PM2.5 exposure. Consonni et al. (2018) discovered a positive connection between increasing PM10 exposure and the incidence of lung cancer in populations living in severely polluted areas of Italy. Similar findings were also found in the study of Raaschou-Nielsen et al. (2016) (Bowe et al., 2019; Xing et al., 2019).

While epidemiological studies have proven a link between particulate matter and lung cancer, the molecular mechanism has received less attention. According to related research, PM2.5 can induce apoptosis by activating the jak-2/STAT-3 signaling pathway, resulting in an inflammatory response and lung injury manifested by pulmonary fibrosis (Yue et al., 2021), as well as promote lung cancer cell invasion via the ARNT2/PP2A/STAT3/MMP2 pathway (Chen et al., 2018). A new study discovered that PM2.5 can trigger inflammation and malignant transformation via the ER system (Luo et al., 2021). Additionally, lung cancer may be induced by modulating miRNA expression and H3K9 acetylation in lung tissue (Ding et al., 2016; Quezada-Maldonado et al., 2018). The following method may account for the short-term effect of Wuhai particulate matter on lung cancer mortality.

The dual-pollutant model can be used to determine the autonomous function of a particular pollutant. Except for PM2.5 and PM10, the dual-pollutant model utilized in this study suggested that the other models were statistically significant (P < 0.05). Also, the extra risk between PM2.5 and NO2, PM2.5 and SO2, and PM2.5 and SO2 increased marginally, while CO increased significantly. PM2.5 has a strong association with CO, according to Spearman’s correlation study. Nevertheless, research by Hart et al. (2011) demonstrates that transportation and other forms of fossil fuel burning are significant causes of pollution, including dangerous pollutants such as PM2.5 and CO. There may be some synergy or interaction between the two air contaminants to a great level.

Except after controlling for PM2.5, the dual-pollutant model of PM10 revealed that the ER was not significantly different from the single-pollutant model. Similarly, Raaschou noted that the connection between PM2.5/PM10 and lung cancer mortality was mostly explained by PM2.5, which corroborated the drastically reduced impact value observed in this study after controlling for PM2.5 (Raaschou-Nielsen et al., 2016). PM10 played a role in lung cancer mortality, which was identified independently of this study. As a result, the model used in this study was capable of interpreting the relationships between contaminants and lung cancer deaths. In comparison to the single-pollutant model, the dual-pollutant model has a somewhat greater influence. This could be due to the high correlation between the cross effects of air pollutants in the model or to the increased standard error caused by the dual-pollutant model (Zhu et al., 2019).

Sex is widely accepted as a significant element in health evaluations and has been standardized to some extent as an appropriate method of population segmentation. The study’s stratified findings indicated that PM2.5 had an effect on both men and women, but was more pronounced in men. Additionally, as the cumulative average lag days grew by multiple days, the ER climbed steadily. Although this conclusion was confusing in comparison to other prior research findings (Mu et al., 2013; Guo et al., 2016; Uccelli et al., 2017; Zhou et al., 2017; Myers et al., 2021), it had the same perspective as Fu et al. (2015), Moon et al. (2020), and Nie et al. (2018). These issues were exacerbated by differences in the study population structure and lung disease caused by smoking in the male sample (Pun et al., 2017). Obviously, prior studies had shown smoking as a risk factor for lung cancer (Jeon et al., 2018; Liu et al., 2020). Some socioeconomic demographic characteristics, such as smoking history, education level, and yearly family income, were linked to PM2.5 exposure in Villeneuve’s study, and later research revealed that smoking history alone had a mixed influence on the connection between PM2.5 and lung cancer mortality (Villeneuve et al., 2011). Additionally, smoking and PM2.5 exposure may have a synergistic effect on lung cancer risk, according to evidence (Lin et al., 2018). According to a national survey in China, men smoke at a higher rate than women, with the exception of a few regions with a more balanced sex ratio, such as Kunming in Yunnan Province, where women smoke at a higher rate (21%) than other cities. Male smoking rates are as high as 78.4% in Baotou, also in the Inner Mongolia Autonomous Region, whereas female smoking rates are only 5.2% (Yang et al., 2016). In Wuhai, the male-to-female population ratio is approximately 6:5. According to the male-to-female smoking ratio in Baotou City, the male smoking population in Wuhai is around 18 times that of the female population. The male population’s higher smoking rate may have contributed to the study’s more significant results for men and the confounding effects of smoking. And, to add to that, although the mechanism of cigarette-induced lung cancer is complicated, the study discovered that inhaled cigarette smoke causes infiltration of inflammatory cells into the mucosa, submucosa, and glandular tissue, which results in matrix damage, blood supply shortages, and epithelial cell death (Hou et al., 2019). In the meanwhile, tobacco smoke also causes oxidative stress and mitochondrial damage, which leads to metabolic reprogramming and an increase in glycolytic flux. This is accompanied by a downregulation of FOXO3a, which promotes tumor growth by contributing to EMT processes and cell motility (Di Vincenzo et al., 2021). Upregulation of CBX3 caused by smoking can enhance development by activating the ARHGAP24/Rac1 pathway (Jin et al., 2022). Exposure to radon and home fuel fumes, on the other hand, raises the risk of lung cancer (Corrales et al., 2020). PM10, on the other hand, was overwhelmingly positive exclusively in men. Men’s exposure opportunities are also triggered by everyday life and job, food, professional physiological structure, and other factors, and men’s exposure is greater than women’s (Consonni et al., 2018). The aforementioned phenomena could also be explained by the fact that girls and males have different respiratory architecture (Clougherty, 2010; Wang et al., 2020). Yue et al. (2017) and Consonni et al. (2018) both observed similar findings.

After age stratification, it was discovered that PM2.5 and PM10 were only sensitive in the elderly, but there was no significant difference between people under 65 (P < 0.05). Simultaneously, the effect of PM2.5 was significantly greater than that of PM10. Some persons observed a phenomena similar to that described in this study (Guo et al., 2016; Pun et al., 2017; Wang et al., 2019; Wu et al., 2021). Further research has discovered that older persons face larger health risks (Nie et al., 2018). This outcome is attributable to two key factors. The first explanation is that the old spend more time outside than the young, who are compelled to work, resulting in the elderly being more exposed to outdoor air pollution. The second reason is that physiological processes linked to the health effects of air pollution in the elderly include, but are not limited to, a reduced ability to compensate for environmental insults, changes in plasticity and/or homeostasis, reduced immune responses, and altered responses to oxidative stress, among others (Geller and Zenick, 2005).

After seasonal stratification, it was observed that PM2.5/PM10 had greater impact values in the warm season than in the cold season, and both were positively connected with lung cancer mortality. Earlier studies corroborated this conclusion (Wang et al., 2019; Zhu et al., 2019). Chung et al. (2021), Sloan et al. (2012), and Wei et al. (2020), on the contrary, observed the inverse. As according studies, the incidence of lung cancer was higher in the high-temperature group and the high-temperature group than in the low-temperature group (Guo et al., 2021). The findings showed that the effects seen in this article during warm seasons may be explained in part by the association between temperature and pollution, as well as by other meteorological parameters such as humidity, pressure, and customs. Additionally, on the one hand, exposure times and pollutant concentrations may vary by season. On the other hand, the composition and toxicity of air pollution varied significantly by season and was a vital element (He et al., 2016).

This article does have some potential limitations. First, the air pollution concentrations we measured through monitoring do not reflect the study population’s real exposure, and indoor air pollution can also harm the human body, therefore, the research findings contain other uncontrollable risk factors. Second, other underlying conditions associated with lung cancer, such as COPD, were not considered in the lung cancer mortality population. Finally, because the mortality data gathered do not include this information, smoking as a significant risk factor for lung cancer has not been dismissed, so future research should focus on gathering this information. In conclusion, the foregoing difficulties will be addressed in future research, and the impact of pollutants on lung cancer and the disease’s causation mechanism will be investigated further.

Conclusion

Wuhai’s present investigation established that exposure to PM2.5/PM10 may increase lung cancer death rates and that persons over the age of 65 were more likely to be harmed by pollutants in overall. And the pollutant results remain solid even after the dual-pollutant model is adjusted. The research findings are consistent with the previous large-scale, longer term investigations. It also adds to the knowledge on this particular particle size range, which will aid in developing future health risk assessment regulations for particulate air pollution.

Acknowledgements

The project team would like to acknowledge the data support provided by the Wuhai Center for Disease Control and Prevention.

Y. Song provided and integrated data; C. Liu, H. Li, Y. Gao, N. Cao, and L. Zhao provided guidance on statistical methods; Y. Liu, J. Zhao, D. Xu, and H. Li conducted data analysis and result verification; Y. Liu and C. Liu wrote the manuscript and contributed equally to the article, so they should be as the co-first authors; H. Li reviewed the manuscript.

Data availability: the datasets are available upon reasonable request from the Wuhai Center for Disease Control and Prevention.

Ethics approval: this study was approved by the Ethics Committee of Inner Mongolia Medical University.

Conflicts of interest

There are no conflicts of interest.

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Keywords:

generalized additive model; lung cancer mortality; PM2.5/PM10; short-term exposure

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