Influenza-like illness, as an important hotspot in public health areas, can cause substantial morbidity and mortality each year. Air pollution can increase the incidence of air pollution-related diseases, especially respiratory disease. Previous studies have illuminated that changes in air pollutants can influence the incidence of ILI or respiratory disease [4, 7, 21, 23]. However, different studies provided different results; the cause might be the different study location and the different air pollutants involved. In this study, we brought all six air pollutants into the study and analysed the possible association between the different air pollutants and ILI in Jinan from 2016 to 2017.

With the rapid growth of the population and industry, the increase in PM2.5 concentration in some cities of China is getting unacceptably high, and air pollution has become an extremely critical public health problem in China. In our study, we found that PM2.5 was positively associated with ILI after Spearman’s analysis. The results of the wavelet analysis assessed a potentially positive impact of PM2.5 on daily ILI in the winter, and the change in PM2.5 concentration preceded ILI for two days. After GAM analysis, we further observed that the RR value of ILI was 1.0137 (95% CI: 1.0083–1.0192) and 1.0119 (95% CI: 1.0058–1.018) with a 10 μg/m3 increase in PM2.5 concentration on the current day and lag01 days, respectively, which verified the result of wavelet analysis. The observed results were consistent with previous epidemiology studies [4, 23, 32]. PM, a mixture of particles and droplets in the air, can raise the virus attachment to respiratory epithelial cells and deposit deep in the lung due to the small size and larger surface-to-volume ratio. Exposure to PM2.5 not only led to airway epithelial damage and barrier dysfunction but also decreased the ability of macrophages to phagocytize viruses, which raised the susceptibility of an individual to viruses [33].

Although we did not observe a relationship between PM10 and ILI with the cross wavelet approach, we found that PM10 was significantly related to ILI on lag0 and lag02 days through GAM, which was in accordance with previous studies [4, 32, 34] but was different from the relative studies in Hefei for ILI [23] and Hong Kong for hospital admission [35]. One possible reason is that the investigated subjects were different. Liu XX et al. [23] observed changes in weekly ILI, and Wong et al. paid attention to influenza hospitalization cases, while our reports focused on daily ILI [35]. Another reason might be in the linkage of ultraviolet radiation to PM10, which can partly attenuate the effect caused by PM10 [35].

The results of cross wavelet analysis and GAM indicated there was a positive relationship between SO 2 and ILI cases, which was in accordance with the results of Hwang et al. [34], but different from some previous studies [23, 32]. Some studies have found that the cause of the negative association between SO 2 and ILI is that the acidic environment influences virus survival and decreases virus transmission [36, 37]. In contrast, an experimental study demonstrated that inhalation of SO 2 at 26 mg/m3 after influenza virus infection can raise the risk of pneumonia in mice [38]. Furthermore, some studies have indicated that SO 2 can damage the human pulmonary defence system through nonspecific airway reactivity, such as deceasing the mucociliary transpiration rate and alveolar clearance of deposited particles and dysfunction the ability to pulmonary macrophages, which increased the susceptibility to viruses [39]. At the same time, the RR value was the highest in the single-pollutant model, showing people were sensitive to SO 2 , which was in accordance with the previous studies with higher RR or ER values of SO 2 effects on influenza or other respiratory tract infection [18, 23]. The possible reason might be that the majority of SO 2 can be dissolved and absorbed easily in the upper respiratory tract, which decreases the immunity of human beings and the resistance to influenza or other respiratory viruses. As for the reason in detail, there need more experimental or epidemiological studies to assessed the effects.

As for the effect of O 3 on ILI, exposure to O 3 led to a variety of diverse effects. In our study, the results of both cross wavelet analysis and GAM showed a negative effect of O 3 on ILI, which was consistent with some previous results [22, 40]. For example, Ali ST et al. illustrated that an increase in O 3 concentration decreased the transmissibility of influenza virus [22]. However, Wong et al. demonstrated a significantly positive association between O 3 and pneumonia and influenza admission, with a 10 μg/m3 increase resulting in a relative risk of 1.028 (P < 0.001), suggesting that exposure to O 3 would increase the susceptibility to influenza and influenza-related disease [35]. Furthermore, Hwang and Chan et al. and Li YR did not observe an association between respiratory tract illness among children and O 3 [18, 34]. Because of the diverse results, some studies considered the different concentrations of O 3 in different cities and different susceptibilities to air pollutants as possible reasons [18]. Some laboratory-based studies have also examined the impacts of O 3 exposure on respiratory illness. Selgrade et al. [41] found that the mortality of mice infected with influenza virus increased twofold only after day 2 of continuous exposure to 1 ppm O 3 for 3 h/days, and the mortality had no change on the other days. When the concentration decreased to 0.5 ppm, the effects were not observed. Kesic MJ et al. [42] showed that short exposure to O 3 (0.4 ppm for 4 h) can enhance influenza virus replication but did not affect the cellular antiviral response. However, Jakab et al. [43] observed that for mice during the course of infection with influenza, exposure to 0.5 ppm O 3 could reduce the severity of the disease with less widespread infection and decreased pulmonary morbidity. Therefore, the effect of exposure to O 3 mainly depended on the exposure duration and time during infection. Recent studies have shown that the effects of O 3 on reduced influenza transmissibility may be associated with O 3 ’s virucidal activity and the impact of O 3 on the host defence [22].

For NO 2 , we did not find any association with daily ILI in either approach, which was consistent with the results of Liu XX in Hefei [23], XZZ in Brisbane [20]. However, Huang et al. [4] and Wong et al. [44] found that NO 2 was significantly associated with ILI or influenza. The experimental results suggested that the NO 2 -exposed subjects were more easily to be infected with influenza virus because the ability of macrophage-dependent inactivation of the invading pathogen decreased [45]. However, Goings et al. did not observe the statistically significant effect of NO 2 exposure on influenza infections [46]. Therefore, it is difficult to find an association between NO 2 and ILI, which need more epidemiological and experimental studies to examine the relationship.

The analysis of the lag effect of air pollution on the occurrence of daily ILI can help explain the mechanisms behind the association and propose a strategy for the control and prevention of ILI. Similar to previous studies, we observed that air pollutant (PM2.5, PM10, CO) exposure on the current day (lag 0) and SO 2 (lag01), O 3 (lag05) had the strongest effect on daily ILI. The results of wavelet analysis and GAM analysis showed that there was a 2-day lag for ILI following PM2.5 or PM10 concentration change, which is consistent with the incubation period of the influenza virus and the previous studies in Beijing [7], Nanjing [4], and Hong Kong [35] but different from Xu’s reports with ten lag days in Brisbane, Australia [20]. For this reason, Xu’s report mainly focused on the impact of air pollutants on influenza virus, but we observed the results of ILI caused by different respiratory viruses. At the same time, the results of GAM also showed a 2-day delay for ILI following PM10 and SO 2 concentration variance and 5-day delays for CO, but we did not observe the same results from the wavelet coherence analysis.

Epidemiology studies have demonstrated that children and the elderly are more likely to be affected because of their weak immune system under bad circumstances of air quality [47]. However, for air pollutants (PM2.5, PM10, CO and SO 2 ), the people aged 25–59 was shown to have a higher risk of ILI compared with the other age groups. Similar results were also found by Feng C et al. in a study of the short-term effects of PM2.5 on ILI in Beijing [7]. Our study also showed air pollutants (PM2.5, PM10 and CO) were strongly associated with ILI risk of the groups aged 5–14 and 0–4 (p < 0.001) and SO 2 was strongly related with ILI risk across all age groups (p < 0.001). This might be because the 25–59 year group is the main work group, who spend more time outdoors than the young and the elderly, thus increasing the incidence of exposure to air pollutants. At the same time, we found a significantly negative relationship between O 3 and ILI under 4 years old, 5–14 age groups and > 60 age groups, which was consistent with the results of Li YR’ et al. in Hefei [18] and the results of Bono et al. [40] and Wang YY et al. in Shanghai [48]. However, Samoli et al. [49] did not find a relationship between O 3 and paediatric emergency asthma admissions under 4 years old, but they observed a significant association for the 5- to 14-year-old age group in 2001–2004 in Athens, Greece.

The results of our study have important public health implications. First, air pollutants have become an important public health problem because of their adverse health effects. Our results provide evidence that air pollutants can increase the incidence of ILI, and it is essential to take measures to decrease the level of air pollutants and incidence of ILI, including plant or work site shutdown, reduction in the amount of outside activities, school shutdown, or promotion of the use of personal protective equipment (e.g., respirator). Second, our results quantified the relationship between short-term exposure to air pollution and ILI among different age groups. The estimated percent variation can help monitor the adverse health effects caused by air pollution and further assess the risk factors. Finally, our results highlighted the importance of air quality surveillance, which can publish the relative early warning information in due time and take essential measures to protect people’s health and decrease the incidence of ILI and other related diseases.

There are still several limitations in our study. One limitation was the ILI case data. In the study, we only analysed the ILI counts data from three influenza surveillance sentinel hospitals, instead of all hospitals, which may be reduce the effects of air pollutant on ILI or influenza. The three hospitals continues to monitor the change of ILI, the data has a certain coherence and consistency, and can represent perfectly the influenza trend of Jinan recognized by National Health Commission and World Health Organization, which can be used to exploit the correlation between air pollutants and ILI. The second is that the simple daily averaging method might result in measurement errors for pollutants that have a correspondingly large spatial variability with different air pollutant concentrations among 28 surveillance locations. However, if the surveillance location and surveillance method did not change systematically with time, the exposure measurement data collected can perfectly reveal the association based on territory-wide time-series data of both influenza and air pollutants. The third is that the data were collected from Jinan and over two years, which may not perfectly illustrate the association between air pollutants and ILI; thus, the long-term data and more cities would be added for further study.