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Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China

BMC Public Health volume 25, Article number: 1456 (2025) Cite this article

Abstract

Background

Emerging evidence links air pollution to respiratory infections, yet systematic assessments in cold regions remain limited. This study evaluates the short-term effects of six major air pollutants on influenza-like illness (ILI) incidence in Changchun, Northeast China, with implications for air quality management and respiratory disease prevention.

Methods

ILI surveillance data from Changchun were extracted from “China Influenza Surveillance Network” and the ambient air quality monitoring data of the city were collected from 2017 to 2022. A generalized additive model (GAM) with quasi-Poisson regression analysis was employed to quantify pollutant-ILI associations, adjusting for meteorological factors and temporal trends.

Results

Among 84,010 ILI cases, immediate exposure effects were observed: each 10 µg/m³ increase in PM2.5 (ER = 1.00%, 95% CI: 0.63–1.37%), PM10 (0.90%, 0.57–1.24%), and O3 (1.05%, 0.44–1.67%) significantly elevated ILI risks. Young and middle-aged individuals (25–59 years old) exhibited the highest susceptibility to five pollutants (PM2.5, PM10, SO2, O3, and CO), and age subgroups under 15 years old exhibited susceptibility to NO2. Post-COVID-19 outbreak showed amplified effects across all pollutants (p < 0.05 vs. pre-outbreak). The effects of PM2.5, PM10, SO2 and O3 on ILI cases were greater in the cold season (October to March) (p < 0.05).

Conclusions

PM2.5, PM10, and O3 exposure significantly increases ILI risks in Changchun, particularly among young/middle-aged populations during cold seasons and post-pandemic periods. These findings underscore the urgency for real-time air quality alerts and targeted protection strategies during high-risk periods to mitigate respiratory health burdens.

Peer Review reports

Background

Influenza-like illness (ILI) is defined as fever (≥ 38 °C) plus cough or sore throat [1]. ILI is a common acute respiratory syndrome that can be caused by pathogens such as respiratory pathogenic bacteria, influenza virus (IFV), severe acute respiratory syndrome coronavirus 2 (SARS‑CoV-2), respiratory adenovirus (RAdV), respiratory syncytial virus (RSV), rhinovirus (RV), seasonal coronavirus (sCoVs), and human metapneumovirus (HMPV) [2,3,4]. In China, ILI surveillance constitutes a critical component of the national acute respiratory infection surveillance system. This comprehensive approach holds particular public health significance as it tracks both notifiable diseases (e.g., influenza, COVID-19) and non-notifiable respiratory infections (e.g., RSV, RAdV infections), providing early warning capabilities through symptom-based surveillance.

Currently, ILI is undoubtedly dominated by influenza and coronavirus disease 2019 (COVID-19) [5]. An estimated 1 billion cases of influenza are reported globally each year, and seasonal influenza alone may cause between 290,000 and 650,000 deaths from respiratory illnesses each year [6]. Since the beginning of the COVID-19 pandemic, more than 775 million confirmed cases and more than 7 million deaths have been reported globally [7]. The coexistence and coinfection of influenza viruses and coronaviruses could potentially accelerate viral evolution and disease dynamics and burden, which will lead to new public health challenges [8]. In addition, nonpharmacological interventions during the COVID-19 pandemic resulted in a significant decline in respiratory infections other than COVID-19, with a concomitant increase in population susceptibility to the pathogen. With the adjustment of epidemic prevention and control measures, the transmission of respiratory viruses such as RSV, RAdV and others may return to or even exceed the pre-COVID-19 pandemic levels, and an atypical seasonal pattern of respiratory viruses may occur [9, 10]. These epidemiological shifts present substantial challenges to the prevention and control of respiratory infections. Although vaccination [11, 12] and nonpharmacological interventions (restricting travel, wearing face masks, washing hands frequently, and observing cough etiquette [13, 14]) are the most effective measures for preventing respiratory infections, identifying and controlling risk factors associated with ILI is particularly important in further reducing the risk of ILI incidence, as well as in disease pre-warning.

Beyond individual susceptibility and meteorological influences, environmental factors also influence the occurrence of ILI [15], and the associations between air pollutants and ILI have received much attention. Air pollutants can increase the susceptibility and severity of respiratory pathogen infections by disrupting respiratory barrier functions, altering macrophage functions, altering the immune response, and disrupting respiratory microbiota homeostasis [16]. A large amount of epidemiologic and clinical evidence suggests that air pollutants are associated with respiratory infections [17, 18]. However, the relationship between air pollution and ILI has been inconsistent in previous studies.

In a nationwide study in China, particulate matters (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) were positively associated with changes in ILI, and this association varied by season and region, with higher impact estimates in the colder seasons, in the eastern and central regions, and in provinces with wetter conditions and larger populations [19]. In a study in 30 provinces in China, there was a significant positive correlation between influenza transmissibility and five different air pollutants including PM2.5, PM10, SO2, CO and NO2 [20]. Yang et al. investigated the association between O3 and influenza transmission in 8 provinces in temperate and subtropical regions of China, and reported that O3 is an important driver affecting influenza transmission, and that the correlation between O3 and influenza transmission is U shaped [21]. We searched for more relevant studies in Beijing, China. Li et al. reported that air pollutants (AQI, PM2.5, PM10, CO and O3) increase with an increased risk of ILI outpatient visits during influenza nonoutbreaks, whereas air pollutants (PM2.5, SO2 and CO) are associated with a decreased risk during outbreaks [22]. Feng et al. reported that there was a strong positive association between PM2.5 and ILI risk during the influenza season (October to April), whereas no significant association was detected during the noninfluenza season (May to September) [23]. Zhang et al. demonstrated that urbanization had significantly aggravated the impact of air pollutants on ILI outpatient visits. In the early period of urbanization, the effects of PM2.5, PM10, SO2, O3 and CO on ILI were not significant. In the later period of urbanization, the AQI and the concentrations of PM2.5, PM10, SO2, NO2, and CO were significantly associated with an increased risk of ILI outpatient visits, but O3 was significantly associated with a decreased risk of ILI outpatient visits [24]. Research on the effects of air pollutants on ILI is also gaining attention in some major cities in China. In a study in Guangzhou, PM1, PM2.5 and PM10 were all risk factors for ILI, and the health effects of particulate pollutants varied by particle size [25]. The results of a study by Tang et al. in Xi’an revealed a positive correlation between ILI cases and AQI, and that the risk of respiratory infections increased progressively with increasing AQI [26]. Liu et al. in Hefei reported that PM10 was negatively correlated with ILI and laboratory-confirmed influenza cases, whereas PM2.5 was positively correlated with ILI and confirmed influenza cases, SO2 had a significant effect on confirmed influenza cases, but had no significant linear relationship with ILI, and NO2 had a negative correlation with confirmed influenza cases, but had no significant effect on ILI [27]. The results of a study in Jinan by Su et al. revealed that air pollutants, especially PM2.5, PM10, CO, and SO2, can increase the risk of ILI, and a negative correlation between O3 and ILI cases was also found [28]. Huang et al. in Nanjing reported that increased IQRs of PM2.5, PM10 and NO2 levels were associated with increased daily ILI, but for patients over 25 of age, the effects of air pollutants were negatively correlated [29]. The results of a study by Huang et al. in Ningbo revealed that PM2.5, NO2 and SO2 were positively associated with hospitalization visits for adults with acute upper respiratory tract infections (AURTIs), with the exception of O3. Females and young adults (18–60 years old) are more susceptible to PM2.5 and SO2, and the effects are greater in rural areas and the urban-rural interface [30]. In Poland, the results of a study by Lindner-Cendrowska et al. in Warsaw showed that climate and weather have a significant impact on the incidence of ILI, with PM2.5 and PM10 positively correlated with ILI visits [31]. A study by Toczylowski et al. in the city of Białystok revealed that there was an exponential relationship between PM2.5 pollution and the incidence of ILI, and that all measures implemented to reduce the concentration of PM2.5 contributed to a reduction in the spread of SARS-CoV-2 and other respiratory infections [32].

We have observed inconsistent and even contradictory results concerning the effects of air pollutants on ILI from different studies, suggesting that there may be temporal or regional differences in the effects of different air pollutants on ILI. Notably, there remains a paucity of studies quantifying the effects of air pollutants on ILI in Northeast China’s unique climatic context, which limits the generalizability of the results to local and cold regions, and there is almost no research-based assessments of the effects of air pollutants on ILI during the COVID-19 epidemic. Therefore, the aim of this study was to systematically assess the relationships between six major air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) and ILI in Changchun, a major city in northeastern China, as well as to evaluate the impacts of the COVID-19 outbreak, which will have high public health significance in reducing pollutant emissions and public health hazards in the area.

Methods

This is an ecological study conducted in Northeast China in Changchun, Jilin Province. The study data included surveillance data on ILI, air pollutants and meteorology.

Date collection

ILI surveillance data

Daily outpatient and emergency ILI cases from one general hospital and one specialized children’s hospital in Changchun (2017 to 2022), were extracted from the “China Influenza Surveillance Network” managed by the Chinese Center for Disease Control and Prevention (CDC). Surveillance of influenza-like cases was carried out in general hospitals in internal medicine outpatient clinics, internal medicine emergency clinics, fever clinics and/or pediatric internal medicine outpatient clinics and pediatric emergency clinics, and in children’s hospitals in pediatric internal medicine outpatient clinics, pediatric emergency clinics and/or fever clinics. Physicians in the hospital’s surveillance clinic diagnosed cases seen daily at the clinic according to the definition of an ILI, where the time when fever appears in ILI should be within the course of the current acute febrile illness, and the temperature determination includes the patient’s self-tested temperature and the healthcare provider’s tested temperature.

Air quality data

Daily concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) were obtained from nine national air quality monitoring stations distributed across Changchun’s urban areas. Daily.

Data collection followed national standards:

24-hour average concentrations for PM2.5, PM10, SO2, NO2, and CO.

Maximum 8-hour moving average for O3.

Data underwent rigorous quality control by the local Department of Ecology and Environment.

Meteorological data

Daily meteorological parameters, including temperature (t), relative humidity (RH), precipitation (precip.) and wind speed (WS), were provided by the local governments’ Meteorological Departments. Monitoring stations covered the city’s urban core, with data validated through official quality assurance protocols.

Statistical analysis

Descriptive analysis

Descriptive statistical analysis was used to reveal the characteristics of ILI, meteorological data, and air pollutants. All variables were tested for normality, and since most of these variables were non-normally distributed, the correlations between ILI, air pollutants, and meteorological parameters during the study period were estimated using Spearman’s correlation coefficient.

Generalized additive model (GAM)

A generalized additive model (GAM) with quasi-Poisson regression analysis was used to further elucidate the associations between air pollutants and daily ILI, since daily ILI counts approximately followed the Poisson distribution. The model structure:

$$\eqalign{{\rm{log}}\left[ {E\left( {{Y_t}} \right)} \right]{\rm{ }} = & \beta {X_i} + {s_{(time,\,df)}} + {s_{(t,\,df)}} \cr& + {s_{(RH,\,df)}} + {s_{(precip,\,df)}} + {s_{(WS,\,df)}} \cr& + DOW + {\rm{ intercept}} \cr} $$

where,

Yt represents the number of ILI counts on day t; s represents the smooth spline function; df represents the degree of freedom; DOW is a variable for the day of the week; β is the regression coefficient for each air pollutant; and Xi represents the concentration of air pollutants.

In the model, a natural cubic spline function of nonlinear variables including the time trend, mean temperature, mean relative humidity, mean precipitation, and mean wind speed was introduced into the GAM to exclude longer seasonal trends and potential nonlinear associations between weather conditions and the ILI. Six degrees of freedom were used to control for long-term trends and seasonal effects, and 3 degrees of freedom were used to control for the effects of meteorological factors. DOW was introduced to control for short-term trends. The association was expressed as the estimated percentage increase (95% CI) in the ILI per 10 µg/m3 (0.1 mg/m3 for CO) increase in air pollutant concentration. Expressed as the percentage increase in the relative risk (RR), it is called the excess risk (ER). ER = (RR-1) × 100%.

Subgroup and sensitivity analyses

To adequately calculate the lagged impacts, we modeled the impacts of air pollutants on the current day (lag 0) and the previous 1–7 days (lags 1–7). To calculate the effects of air pollution on ILI in different age subgroups, we categorized the age subgroups of ILI into toddlers (0–4 years old), children (5–14 years old), adolescents (15–24 years old), young and middle-aged adults (25–59 years old), and older adults (60 years old and above), and estimated the differences between the age subgroups by fitting a model for each of these age subgroups. To explore whether COVID-19 had an impact on the assessment results, we divided the study time period into pre-outbreak (2017 to 2019) and post-outbreak (2020 to 2022) periods, and estimated the air pollutant-ILI associations for particular periods by fitting models for different periods. To explore whether the effects of air pollutants on ILI vary seasonally, we divided the year into a cold season (October to March) and a warm season (April to September) based on the city’s average monthly temperatures, and estimated season-specific air pollutant-ILI associations by fitting models for different seasons.

In addition to this, several sensitivity analyses were conducted, including the construction of a two-pollutant model to test the independent effects of air pollution on ILI. The robustness of the model was verified by changing the degree of freedom of the smoothing function of the meteorological factor from 2 to 4 and controlling the degree of freedom of the time trend from 5 to 8.

Software

The data were analyzed using the R programming language V.4.4.0, the “mgcv” package was used for GAM model construction, and the “ggplot2” package was used for plotting.

Results

Table 1 summarizes daily ILI cases, air pollutant concentrations, and meteorological parameters in Changchun during 2017 to 2022. The study included 84,010 ILI cases, with an average of 38 patients across two hospitals. The average concentrations of PM2.5, PM10, SO2, NO2, O3, and CO were 36 µg/m3, 62 µg/m3, 13 µg/m3, 33 µg/m3, 81 µg/m3, and 0.8 mg/m3, respectively. Meteorological averages included temperature (7.2 °C), relative humidity (62.2%), precipitation (1.9 mm), and wind speed (2.7 m/s).

Table 1 Descriptive statistics of ILI, air pollutants, and meteorological data in Changchun, 2017 to 2022
Full size table

ILI cases exhibited distinct seasonal patterns, with higher incidence during cold seasons (October to March) and lower rates in warm seasons (April to September). Notably, a pronounced peak occurred in January to March 2020 across all age subgroups ≥ 15 years, coinciding with the COVID-19 outbreak. Post-2020, ILI cases in these subgroups remained elevated compared to 2017 to 2019 levels, while cases in children (0–4 and 5–14 years) declined. We also observed a decrease in ILI cases from March to June 2022 and an increase in ILI cases at the end of 2022 (Fig. 1).

Fig. 1
figure 1

Time series of ILI cases in Changchun by age subgroups, 2017 to 2022

Full size image

Figure 2 illustrates temporal trends of six air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and four meteorological parameters (t, RH, precip., and WS). Consistent with the pattern of ILI changes, the concentrations of the air pollutants PM2.5, PM10, SO2, NO2, and CO are generally higher during cold seasons, whereas O3 levels were higher in warm seasons. Longitudinal analysis revealed declining trends for SO2, NO2, and CO from 2017 to 2022. PM2.5, PM10 and O3 frequently exceeded national ambient air quality standards, while other pollutants remained within limits [33].

Fig. 2
figure 2

Time series of six air pollutant concentrations in Changchun, 2017 to 2022 (the yellow dashed line indicates the II concentration limit of air pollutants as stipulated in the National Ambient Air Quality Standards (GB3095-2012) [33], while the red dashed line represents the I concentration limit)

Full size image

The correlations between the ILI, air pollutants and meteorological parameters are presented in Supplementary Table 1. The ILI cases were positively correlated with PM2.5, PM10, SO2, NO2, CO, and WS, and negatively correlated with O3, t, RH, and precip.. There are mostly positive correlations between pollutants, except for O3, and negative correlations between pollutants and t, RH, and precip.. SO2 and O3 were positively correlated with WS. NO2 and CO were negatively correlated with WS. The correlations are statistically significant (p < 0.01).

Figure 3 demonstrates lagged associations between air pollutants and ILI. We found that PM2.5, PM10, and O3 had a significant positive association with ILI cases, and the association persisted on the same day and with a lag of 1–7 days (p < 0.05). In terms of the estimated values, the highest association effect was observed on the same day. Specifically, for every 10 µg/m3 increase in PM2.5, PM10, and O3 on the day, the ILI cases increased by 1.00% (95% CI: 0.63–1.37%), 0.90% (95% CI: 0.57 − 1.24%), and 1.05% (95% CI: 0.44 − 1.67%), respectively. SO2, NO2, and CO showed nonsignificant trends, though SO2 and CO exhibited marginal effects at lags 0–2. NO2 displayed increasing trends with longer lags. In addition, we chose the lag time with the highest estimate as the optimal lag time, i.e., we analyzed the subsequent results on this basis. Supplementary Table 2 shows all the estimates of the effects of air pollutants on ILI for different lags.

Fig. 3
figure 3

Estimated percentage increase of ILI per 10 µg/m3 (0.1 mg/m3 for CO) increase in air pollutant concentration on different lag days. * p value < 0.05

Full size image

Figure 4 reveals age-specific susceptibility. We found that all five pollutants except NO2 had a significant effect on ILI cases in the adolescent (15–24 years old) and young and middle-aged adult (25–59 years old) subgroups (p < 0.05), and the estimates in the young and middle-aged adult (25–59 years old) subgroup were significantly greater than those in the other subgroups. In addition, there was a significant positive correlation (p < 0.05) between PM2.5, PM10, and O3 and ILI in the older adult (60 years old and above) subgroup, and between NO2 and ILI cases in the toddlers (0–4 years old) and children (5–14 years old) subgroups. Supplementary Table 3 shows all the estimates of the effects of air pollutants on ILI for different age subgroups.

Fig. 4
figure 4

Estimated percentage increase of ILI per 10 µg/m3 (0.1 mg/m3 for CO) increase in air pollutant concentration on different age subgroups. * p value < 0.05

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Figure 5 shows the estimates of the effects of air pollutants on ILI over the time period and across different seasons. The effect estimates of all six pollutants on ILI cases increased significantly in the post-COVID-19 outbreak period, which were significantly greater than the pre-outbreak level (p < 0.05). In terms of seasonal differences, the effects of PM2.5, PM10, SO2 and O3 on ILI cases were greater in the cold season (p < 0.05), the effect of CO was greater in the warm season (p < 0.05), and the effect of NO2 on ILI cases was not significant in either the cold or warm season. Supplementary Table 4 shows all the estimates of the effects of air pollutants on ILI for different periods and seasons.

Fig. 5
figure 5

Estimated percentage increase of ILI per 10 µg/m3 (0.1 mg/m3 for CO) increase in air pollutant concentration on different periods and seasons. * p value < 0.05

Full size image

We also performed sensitivity analyses to verify the robustness of the model. The pollutant-ILI associations that were originally significant remained significant after additional adjustment for another pollutant (Supplementary Table 5). However, we also observed an increase in the impact estimates for the other pollutants after controlling for NO2, and for PM2.5 and PM10 after adjusting for CO. The estimates did not change substantially when we changed the time trend df (5–8 per year) and the df (2–4) for temperature, relative humidity, precipitation and wind speed (Supplementary Table 6).

Discussion

With accelerating urbanization, population growth, and rapid industrial development, air pollution has emerged as a critical public health challenge. Accumulating evidence links air pollutants to respiratory diseases [34,35,36], especially respiratory infections [37,38,39]. Therefore, we suggest that there may be an association between changes in air pollutants and the development of ILI. This association may be important for the future management of air pollutants as well as for the surveillance and early warning of respiratory infections. In this study, we collected concentration data for six common air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Changchun from 2017 to 2022, systematically evaluated the possible associations between different air pollutants and the ILI, analyzed their lagged effects, and compared the effects from the perspectives of different ages and different time periods. The results of our study can be used to assess the effects of air pollutants on the incidence of ILI and provide a reference for the effective prevention and control of respiratory infections. In this study, we employed the GAM to conduct data analysis. As a flexible data analysis tool, the GAM can effectively reveal the complex associations among different variables. Traditional linear regression models assume that there is a simple linear relationship between variables, that is, they present a linear relationship. However, many relationships in the real world are often more complex. They are not simple linear patterns but may be complex non-linear curve relationships. From a theoretical perspective, the GAM constructs a prediction model by summing up multiple smooth functions. These smooth functions can flexibly fit various patterns in the data. Whether they are linear or non-linear patterns, they can all be effectively adapted. Thus, we can more accurately depict the relationships between variables and then make more reliable predictions. The GAM has been widely applied in many fields such as environmental science and medicine. In the field of environmental health, this model can be used to analyze the associations among pollutant concentrations, meteorological factors, and disease incidence rates [40,41,42].

Our results revealed that the six air pollutants exhibited a cyclic pattern with distinct seasonality, with PM2.5, PM10, SO2, NO2, and CO concentrations generally higher in the cold season (October to March) and lower in the warm season (April to September). O3 showed the opposite trend, with higher concentrations in the warm season than in the cold season, which was consistent with the results of Yang et al. for all of China [19]. Like the distribution pattern of most air pollutants, ILI cases is also characterized by higher levels in the cold season and lower levels in the warm season. However, we also found an irregular pattern of ILI cases, which may be inextricably linked to the 2020 COVID-19 outbreak, justifying a subgroup assessment of pre-outbreak and post-outbreak cases.

We observed an extremely high peak in the total number of ILI cases from January to March 2020, and hypothesized that this may be related to the increased willingness of the population to seek medical attention at the beginning of the COVID-19 epidemic. Since the transmission and pathogenicity of SARS-CoV-2 are not widely recognized, leading to serious psychosocial problems such as fear and anxiety [43], those with mild respiratory symptoms, who wanted to be treated effectively as early as possible, visited hospitals, though it was not necessary for them to do so, and this led to a sudden increase in ILI cases in a short period of time. This anomaly was mainly observed in the population above 15 years of age, whereas ILI cases in the age subgroups of 0–4 and 5–14 years of age, on the contrary, declined to a certain extent, which is consistent with the findings of Grosso et al. [44]. The apparent fluctuation in ILI cases was associated with the adjustment of governmental outbreak prevention and control measures [13]. During the March to June 2022 outbreak in Changchun caused by Omicron, the government implemented extremely strict closure and control measures, such as school closures, restrictions on group activities, reductions in outings, and quarantine of patients, effectively reducing the scale of the outbreak, which was similar to the outbreak in Shanghai during the same period [45, 46]. The upward fluctuation of ILI cases at the end of 2022 was attributed to the relaxation of nonpharmacological interventions (NPIs) [47].

We found a significant positive correlation between PM2.5, PM10, and O3 and ILI cases, which not only existed on the same day but also persisted with a lag of 1–7 days. The results for particulate matter (PM) are consistent with those of the study by Su et al. [28], but the latter did not find a time lag effect, whereas Liu et al. - conducted their study on a weekly basis - reported that the lagged effect of air pollutants on ILI incidence persisted for a much longer time [27]. The PM plays an important role in viral infections. High concentrations of particulate matter in the air may, under certain meteorological conditions, promote the ability of viruses to remain airborne for longer periods, favoring their indirect transmission. Viruses survive longer and become more aggressive in organisms with immune systems already weakened by air pollution, which may increase the susceptibility of the organism to respiratory viral infections and the severity of the disease [48]. Moreover, the smaller the particulate matter and the greater the surface area-to-volume ratio, the easier it is for viruses to attach and enter the lower respiratory tract for deposition, which not only leads to airway epithelial damage and barrier dysfunction, but also reduces the ability of macrophages to phagocytose viruses, thus increasing an individual’s susceptibility to viruses [49]. O3 was found to be positively associated with ILI cases, and a study by Yang et al. revealed O3 as an important driver of influenza transmission, which is consistent with our results, and they further confirmed that the association between O3 and influenza transmission exhibited a U-shaped pattern [21]. A study in Wuhan suggested that exposure to O3 may disrupt the human airway protease/antiprotease balance, increasing the risk of influenza-related infections [50]. However, some studies have confirmed that increased O3 concentrations reduce the transmissibility of influenza viruses [30]. The virucidal potential of O3 and its effect on host immunity may reduce influenza transmission [51]. In addition, knowledge of the optimal lag times for different pollutants is essential for the development of effective air quality management and public health policies. We found that the effects of PM2.5, PM10, and O3 on ILI cases were most significant on the same day, which is similar to the findings in Guangzhou [25] and Beijing [52]. This suggests that real-time pollutant concentrations should be prioritized to predict ILI-related healthcare demand and optimize resource allocation. Although SO2, NO2, and CO are also common air pollutants, we did not find significant positive correlations between them and ILI cases. Our assessment differs from the results of several previous studies [19, 28]. This may be related to the sensitivity of the population to the pollutants, the length of the study, and the different parameter settings of the study model. Nonetheless, we found some characteristics of the risk estimates for these pollutants, i.e., higher estimates for SO2 and CO on the same day and with a lag of 1–2 days, and progressively higher estimates for NO2. These results imply that the public health effects of these pollutants may be both immediate and prolonged, though further epidemiological and experimental studies are required to confirm the long-term effects on ILI.

We also found that different age groups exhibited varying sensitivities to air pollutants, Five pollutants (PM2.5, PM10, SO2, O3, and CO) were significantly associated with ILI cases in the subgroup of young and middle-aged individuals (25–59 years old). This subgroup showed greater sensitivity to air pollution and demonstrated higher effect estimates, consistent with the findings of Feng et al. and Su et al. [23, 28]. Possible reasons include their higher social mobility and occupational exposure risks: commuting, working outdoors, or frequent participation in social activities, which may increase their exposure to air pollutants. In the adolescent subgroup (15–24 years old), PM2.5, PM10, SO2, O3, and CO, and in the older adults subgroup (≥ 60 years old), PM2.5, PM10, and O3 were significantly positively associated with ILI cases. Adolescents face greater exposure risks from transport-related outings and congregation-related activities. For older adults, preexisting chronic diseases (e.g., asthma and COPD) contribute to respiratory function decline, potentially heightening their susceptibility to short-term high pollution exposure and exacerbating vulnerability [53]. No positive associations were observed between air pollutants (excluding NO2) and ILI in any subgroup under 15 years old, likely due to children’s predominantly indoor lifestyle, which may which may mitigate pollutant impacts on ILI incidence. However, a positive correlation was observed between NO2 and ILI in all age groups under 15 years old, with the strongest impact in toddlers (0–4 years old) subgroup. Some research indicates that exposure to NO₂ can increase the risk of influenza in children and the hospitalization rate for respiratory diseases [54, 55]. Childhood is a crucial period for the development of the airway and immune system. Higher exposure to NO₂ may damage the respiratory mucosa of children, impair immune system function, leading to aggravated airway inflammation and increased airway hyperresponsiveness [56].

Therefore, in view of the above circumstances, the young and middle-aged population holds significant public health importance in preventing and controlling the harm of air pollutant exposure to ILI. At the same time, it is necessary to pay particular attention to the potential harm of NO₂ exposure in children. During the winter and spring seasons when influenza and air pollution are prevalent, children should try to stay indoors, reduce travel and gathering activities. The young and middle-aged population should also pay attention to protection to reduce exposure to respiratory pathogens and human-to-human transmission. Therefore, in view of the above circumstances, the young and middle-aged population holds significant public health importance in preventing and controlling the harm of air pollutant exposure to ILI. At the same time, it is necessary to pay particular attention to the potential harm of NO₂ exposure in children. During the winter and spring seasons when influenza and air pollution are prevalent, children should try to stay indoors, reduce travel and gathering activities. The young and middle-aged population should also pay attention to protection to reduce exposure to respiratory pathogens and human-to-human transmission.

The COVID-19 pandemic and climate and ecosystem changes pose significant global challenges. Their interaction affects the social economy, causing problems such as air pollution and environmental degradation. The pandemic has altered disease patterns, and climate and ecosystem changes influence virus transmission and human health. At the same time, achieving sustainable development requires a balance between the economy and the environment, but the environment is easily overlooked under the impact of the pandemic [57]. To clearly explore whether the COVID-19 outbreak had an impact on population susceptibility, we divided the study into two periods, pre- and post-outbreak, to compare the effect estimates of the impact of air pollution on the health of the population in different periods. The effect estimates of all six pollutants on ILI cases increased significantly after the COVID-19 outbreak, and the outbreak may have made an otherwise unclear association significant, which is consistent with the findings of Lian et al. in Beijing [52]. NPIs targeted at interrupting the transmission of respiratory pathogens are also applicable to the control of air pollutants [58]. During the COVID-19 outbreak, prolonged population-based NPIs resulted in a lack of immune stimulation by pathogens, leading to “immune debt” [59, 60], which may have reduced specific immune protection and increased susceptibility to viruses and air pollutants, possibly indirectly contributing to the significant effect of air pollutants on patients with ILI. Apart from the staging of the epidemic, other time-dependent confounding factors may also significantly interfere with the results. First, the transformation of medical practice patterns may directly affect the monitoring and reporting of ILI cases. For example, the popularization of telemedicine has reduced the frequency of patients seeking medical treatment for mild symptoms [61], while the promotion of rapid molecular diagnostic technologies has improved the accuracy of pathogen detection, leading to the reclassification of some cases originally classified as ILI into specific viral infections [62]. In addition, adjustments to vaccination strategies (such as an increase in influenza vaccine coverage or the introduction of new vaccines) have been proven to significantly reduce the ILI incidence rate in high-risk populations [63]. Secondly, the temporal heterogeneity of public health intervention measures requires special attention. Research has found that the intensity of public awareness campaigns (such as hand hygiene education and mask-wearing guidelines) is negatively correlated with the transmission rate of respiratory diseases [64]. However, this behavior-change effect often fluctuates with the implementation intensity of policies [65].

Changchun, as a northern city, has obvious seasonal differences in climate throughout the year, and winter is a high season for air pollution; therefore, we also analyzed the differences in the effects of air pollutants on ILI cases in the cold and warm seasons. The effects of PM2.5, PM10, SO2, and O3 on ILI cases were greater in the cold season. Increased indoor activity and aggregation of populations during the cold season increase the risk of pathogen transmission, and it has been suggested that the combination of low humidity, cold temperatures, and sunlight may impair the body’s local and systemic antiviral defense mechanisms, leading to increased susceptibility of hosts to respiratory viruses in winter [66, 67].

To assess the stability of the results, we adjusted the variables and parameters in the model to ensure that the results of the study were reliable when multiple air pollutants are present at the same time. The fact that the initially significant pollutant’s association with ILI remained significant after adjustment for another pollutant suggests the stability of these results. Nonetheless, we also observed some special situations where the estimated impacts on other pollutants increased after controlling for NO2, and the estimated impacts on PM2.5 and PM10 increased after adjusting for CO. This further emphasizes that there are complex interactions between different pollutants, air pollutants do not usually occur in independent forms, and that pollutant emissions are an integrated process. In addition, when we changed the time trend df (5–8 per year) and temperature and relative humidity df (2–4), there is no substantial change in the estimates, which suggests that the model has good stability in adjusting for the time trend and meteorological factors, and that our model results are less affected by confounding factors.

There are several limitations in our study. First, the air pollutant data were collected from outdoor ambient air quality surveillance at fixed stations, which has some errors in assessing individual exposure levels. Second, the ILI cases were derived from sentinel hospitals and were unable to cover all the cases, which may have led to some bias in the modeling results. Third, our model accounted for meteorological factors but excluded critical social determinants of health—including s socio-economic factors, healthcare access, and public health interventions—due to data limitations. The absence of these confounders may introduce residual biases, potentially affecting the accuracy of our findings regarding pollution-ILI associations [68]. Fourth, while this ecological analysis elucidates population-level exposure-disease relationships through systemic pattern identification, its inherent design precludes individual-level causal inference. Future research should combine personal exposure tracking with individual health data to validate trends and uncover biological mechanisms of pollution-related respiratory risks. Given these limitations, caution is needed when interpreting and applying the results of the studies. Therefore, more precise exposure assessments and more representative samples would help improve the accuracy and reliability of the studies.

Conclusions

Our findings demonstrate that PM2.5, PM10, and O3 exposures elevate ILI risks in Changchun, and short-term exposure to air pollutants, outbreaks, and cold seasons are significant risk modifiers, and young and middle-aged people are the key populations to be focused on, as the risk of ILI increased by air pollutants is relatively high among this part of the population. These findings hold immediate relevance for formulating public health interventions, optimizing healthcare resource allocation, and establishing an acute respiratory infectious disease surveillance and early warning system.

Data availability

The data that support the findings of this study are available from [Chinese Center for Disease Control and Prevention (CDC)] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [Chinese Center for Disease Control and Prevention (CDC)].

Abbreviations

ILI:

Influenza-like illness

SARS‑CoV-2:

Severe acute respiratory syndrome coronavirus 2

COVID-19:

Coronavirus disease 2019

PM2.5 :

Fine inhalable particulate matters, with diameters that are generally 2.5 micrometers and smaller

PM10 :

Inhalable particulate matters, with diameters that are generally 10 micrometers and smaller

SO2 :

Sulfur dioxide

NO2 :

Nitrogen dioxide

O3 :

Ozone

CO:

Carbon monoxide

GAM:

Generalized additive model

RR:

Relative risk

ER:

Excess risk

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Acknowledgements

We are very grateful to all the technicians involved in influenza surveillance in Jilin Province, as well as to Springer Nature and Curie for editing this article.

Funding

This study was supported by the central government of China, Jilin Province Health Science and Technology Capacity Enhancement Program (2023GW009).

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Authors and Affiliations

  1. Jilin Provincial Center for Disease Control and Prevention (Jilin Provincial Academy of Preventive Medicine Sciences), Changchun, Jilin, PR China

    Yang Pan, Laishun Yao, Biao Huang, Yinghua He, Changxi Xu, Xianda Yang, Yingying Ma, Zhidi Wang, Xingyu Wang & Hong Zhu

  2. Changchun Center for Disease Control and Prevention, Changchun, Jilin, PR China

    Man Wang & Lijun Song

  3. The First Hospital of Jilin University, Changchun, Jilin, PR China

    Xiao Liu

  4. Changchun Children’s Hospital, Changchun, Jilin, PR China

    Guiping Yu

  5. School of Public Health, Jilin University, Changchun, Jilin, PR China

    Yang Pan, Lin Ye & Liting Zhou

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Contributions

All authors contributed to the study conduct. YP: Conceptualization, Formal analysis, Project administration, Software, Visualization, Supervision, Writing – original draft & Editing. LY & BH: Direction, Funding Acquisition, Resources, Supervision. YH & CX: Direction, Conceptualization, Validation. XY, YM, ZW, XW, HZ, MW, LS, XL, GY: Sampling, Data collection, Data curation, Methodology. LZ & LY: Conceptualization, Formal analysis, Software, Supervision, Writing – Review and Editing. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Lin Ye or Liting Zhou.

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In accordance with Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings, jointly issued by the National Health and Health Commission, the Ministry of Education, the Ministry of Science and Technology, and the State Administration of Traditional Chinese Medicine of the People’s Republic of China, we believe that our research will not cause any harm to human beings, and that it does not involve any sensitive personal information or commercial interests, and thus is exempted from ethical review.

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The authors declare no competing interests.

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Lin Ye is the first corresponding author and Liting Zhou is the second corresponding author.

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Pan, Y., Yao, L., Huang, B. et al. Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China. BMC Public Health 25, 1456 (2025). https://doi.org/10.1186/s12889-025-22110-x

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  • DOI: https://doi.org/10.1186/s12889-025-22110-x

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BMC Public Health

ISSN: 1471-2458

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