Air pollution and COVID-19 mortality in Chinese cities: insights from a multi-city analysis during the pandemic’s first wave

In this study, we employed a generalized additive model with a negative binomial distribution to examine the impact of short-term air pollution exposure on COVID-19 mortality in 45 Chinese cities. The results from the single-pollutant model indicated a positive association between NO₂ concentrations and COVID-19 mortality across the 45 cities. Exposure to elevated concentrations of SO₂ increased the risk of COVID-19 mortality, which was more apparent in lagged effects, while O₃ exposure showed a protective effect. The adverse health effect of PM_2.5 was statistically significant only in Wuhan, where the effect of NO₂ on COVID-19 mortality was notably stronger than in the other cities. Furthermore, we found that the cumulative lagged effects in the single pollutant model were stronger than the single-day lagged effects, especially for NO₂. In the two-pollutant model, NO₂ estimates remained statistically significant after adjusting for SO₂, PM_2.5, and O₃, with a stronger effect compared to the single-pollutant model. Stratified analysis reveals that NO₂ has a greater impact on COVID-19 mortality in southeastern cities compared to other regions, while the protective effect of O₃ is generally observed, except in the northern region.

Although the pandemic has ended, there have been relatively few studies investigating the impact of air pollution on COVID-19. Most studies have focused on the relationship between pollution and incidence rather than mortality. Our findings on the short-term positive effects of NO₂ on COVID-19 mortality in both Wuhan and 45 cities in China were consistent with previous research in China and evidence from other countries. Firstly, a study of 120 cities in China, using a generalized additive model, demonstrated a significant positive association between NO₂ and COVID-19 incidence. Specifically, the daily case count of COVID-19 increased by 6.94% for every 10 μg/m³ rise in NO₂ concentration at lag 0–14, consistent with our findings⁶. Another study conducted in Wuhan and Xiaogan, China, similarly found a significant association between COVID-19 incidence and NO₂ concentration, aligning with our research results for Wuhan¹⁶. Studies from other countries such as Britain and Germany also indicated a positive association between NO₂ concentration and both incidence and mortality of COVID-19^12,15,17. A large population-based cohort in Spain found that short-term exposure to NO₂ can significantly increase the risk of COVID-19 mortality¹⁸. However, another nationwide study in the Netherlands did not observe a significant effect of NO₂ on the risk of COVID-19¹⁶.

A number of studies have documented the adverse health effects of NO₂ on the respiratory system due to both short-and long-term exposure¹⁸. Children, who are particularly vulnerable, exhibit heightened sensitivity to respiratory infections triggered by NO₂ exposure^19,20,21. Moreover, studies have identified coronavirus as one of several viruses linked to NO₂ induced respiratory infections²¹. Animal studies have further highlighted that NO₂ exposure can compromise human immune responses, potentially increasing susceptibility to viral infections. Previously infected animals are more prone to reinfection upon subsequent NO₂ exposure²². Future research efforts should perhaps focus on further elucidating the specific mechanisms through which NO₂ increases the risk of COVID-19.

There is inconsistent evidence regarding the association between exposure to PM_2.5 and COVID-19 mortality. Specifically, a positive association was observed only in Wuhan, whereas analysis across 45 cities did not reveal a consistent association between PM_2.5 and COVID-19 mortality. Studies in other Chinese cities like Shanghai and Xiaogan have identified PM_2.5 as a significant risk factor for transmission and morbidity in COVID-19^23,24. Similarly, another research conducted in Wuhan has linked PM_2.5 to increased COVID-19 incidence²⁴. In London, England, a study confirmed that every 1 μg/m³ increase in PM_2.5 concentration was associated with a 1.1% rise in COVID-19 cases and a 2.3% increase in mortality. However, nationwide studies in the U.S. and the United Kingdom (U.K.) aligned with our multi-city findings, showing uncertainty regarding the impact of PM_2.5 on COVID-19²⁵. Previous studies indicate that fine particles can serve as carriers for viruses, including influenza, facilitating their inhalation into the human body. Research in Taiwan, China suggests two potential mechanisms through which PM_2.5 may promote COVID-19 transmission: First, PM_2.5 can directly carry COVID-19 particles; Second, PM_2.5 exposure increases expression of the ACE2 receptor in the lungs, enhancing COVID-19 adhesion. Additionally, PM_2.5 may disrupt the respiratory barrier, exposing deeper lung tissues to pathogens¹⁹.

Our study found that O₃ is a protective factor against COVID-19, which contrasts with the majority of previous research findings¹⁶. Other studies in China and other parts of the world have identified O₃ as a risk factor for COVID-19, contributing to increased incidence and mortality of the disease²⁶. For instance, a study in Italy found that O₃ might facilitate the spread of COVID-19⁶. Increased oxidative stress is widely recognized as a key mechanism by which pollutants exert toxicity²⁷. O₃ reacts directly with unsaturated fatty lipids in the respiratory tract, generating reactive oxygen species such as hydrogen peroxide, as well as lipid ozonation products like lipid peroxides and reactive aldehydes²⁸. This oxidative stress can lead to mitochondrial dysfunction, DNA damage, and subsequent inflammatory responses²⁹, potentially increasing susceptibility to COVID-19.

However, it is not entirely implausible that O₃ could be protective against COVID-19 based on certain mechanisms. Research conducted in Biosafety Level 3 Laboratory (P3 Laboratory) has shown that ozone effectively inactivates the Severe Acute Respiratory Syndrome (SARS) virus, achieving a comprehensive inactivation rate of up to 99.22% in experiments conducted on green monkey kidney cells. Both the SARS virus and the novel coronavirus causing COVID-19 belong to the coronavirus family. Researchers have also found an 80% genomic sequence similarity between the novel coronavirus and the SARS coronavirus³⁰. Therefore, there is reason to speculate that atmospheric O₃ may play a role in reducing COVID-19 mortality rates. Additionally, the negative association between O₃ and COVID-19 mortality observed in our study could also be attributed to scenarios where elevated outdoor ozone levels trigger warnings and advisories from meteorological stations, prompting citizens to reduce outdoor activities and thus decreasing opportunities for COVID-19 transmission.

We also observed a negative association between SO₂ levels and COVID-19 mortality. Current research consistently indicates that SO₂ acts as a protective factor in relation to COVID-19³¹. The research of 120 cities in China found that higher SO₂ levels were associated with reduced COVID-19 incidence³². Similarly, the previously mentioned study in Wuhan, employing a Poisson regression model, confirmed a negative association between SO₂ and COVID-19 mortality³³. Research suggests that SO₂ possesses anti-pneumonia and lung protection properties³⁴. Its antimicrobial action involves penetrating viruses and bacteria, disrupting enzyme and protein activities³⁵. Specifically, SO₂ can deactivate the protein coat of viruses and impair internal enzymatic proteins, leading to structural damage and loss of function, thereby causing viral death³⁶. Coronaviruses, which are enveloped in a lipid membrane, are particularly susceptible to SO₂ due to their affinity, making them easier targets for SO₂ attack³⁷.

In stratified analysis, the impact of air pollution on COVID-19 is notably stronger in the southeastern region compared to the western and northern regions of the country. One possible explanation is that in northern areas, outdoor activities are generally reduced, especially during colder seasons³⁸. COVID-19 patients are isolated in hospitals, thereby limiting their exposure to outdoor air pollutants, which minimizes the influence of outdoor air pollution on COVID-19 mortality rates³⁹. Despite severe air pollution in northern regions, the pandemic began in central China (Wuhan) and spread nationwide. Consequently, as the pandemic spread from Wuhan to other regions, proactive containment measures were already in place, partially masking the impact of air pollution on the pandemic⁴⁰.

One distinctive feature of our study is the utilization of a generalized additive model assuming a negative binomial distribution for the outcome variable. In contrast, many previous studies have employed linear regression or ecological regression models⁴¹. While some studies have also used generalized additive models, they typically assume a Poisson distribution rather than a negative binomial distribution⁴². The Poisson regression model is a fundamental approach for count data but assumes that the mean and variance are equal, which may not hold true in many real-world datasets where over-dispersion (variance exceeding the mean) is common⁴³. The negative binomial model, as a generalization of the Poisson distribution, addresses this issue effectively⁴⁴. It is widely applied in diverse fields such as health statistics and econometrics due to its ability to handle data with varying dispersion levels more robustly⁴⁵. Therefore, our study opts for the negative binomial distribution, which allows for more effective statistical inference under conditions where data exhibit significant deviations from Poisson assumptions.

In addition to data analysis, the study has several strengths. Firstly, we chose the initial outbreak year as the study period to capture the early and natural development of the COVID-19 pandemic, when the influence of preventive policy and control interventions on the evaluation of the association between air pollution exposure and COVID-19 mortality was less pronounced compared to the mid to late stages of the epidemic. Secondly, every province in mainland China was represented by at least one city in the study, which reduces the risk of regional bias and provides a more accurate picture of the nationwide impact. Thirdly, the study examined the single and cumulative lag effects of six main air pollutants exposure from 0 days to 14 days. This approach captures both the immediate and delayed effects, offering a comprehensive understanding of how short-term exposure to air pollution influences COVID-19 mortality. Furthermore, the study stratified the cities into three regions, which adjusts for the region diversity in the environmental, social, health system, and economic conditions that may confound the association between air pollution levels and COVID-19 outcomes.

This study has several limitations. First, pathogen-host interactions may have influenced the results. To address this, we applied the GAM to control for meteorological factors, indirectly accounting for this confounding, as meteorological conditions can also affect pathogen-host dynamics. Second, differences in infection rates, healthcare resources, and public health interventions across cities could introduce heterogeneity. To mitigate this, we grouped cities into three categories based on environmental, social, healthcare, and economic factors, and compared differences across these groups. Third, lockdowns and industrial shutdowns may have affected air pollution levels and viral transmission, potentially introducing bias. However, by focusing on the early phase of the pandemic, before full control measures were implemented, we were able to better minimize this bias compared to later stages, when more structured interventions were in place. Fourth, the study relied on aggregated count data, lacking individual-level information such as age, gender, and residency. This limited our ability to examine the effects across specific demographic groups. Additionally, using city-level average air pollution concentrations as a proxy for individual exposure may have introduced misclassification bias, a common limitation of ecological studies. Fifth, we observed protective effects of certain pollutants on COVID-19 outcomes. While this is plausible based on existing literature, it may also reflect the study’s small sample size, highlighting the need for further validation of these findings. Finally, the generalizability of the study is limited to 45 cities in China, which may not fully represent the diversity of conditions in other regions. Future research should expand to different geographical areas, incorporate larger datasets, and collect individual-level data to provide a more comprehensive global understanding of these issues.

This research underscores the harmful effects of air pollution, particularly NO₂ and PM_2.5, on COVID-19 mortality risk during the early pandemic stages and highlights regional health disparities. Our findings emphasize the need to address air pollution as a key factor in the spread and severity of COVID-19. Although the global pandemic has subsided, its lasting impact on public health remains a concern. This study calls for ongoing health surveillance, particularly in areas with persistent air pollution, and provides valuable insights for future pandemic prevention and treatment strategies.