GRAPHICAL ABSTRACT

INTRODUCTION

The respiratory disease known as coronavirus disease 2019 (COVID-19) was first identified in Wuhan City. The World Health Organization (WHO) declared it a global pandemic due to its rapid spread [1]. As of late 2024, the pandemic has resulted in over 700 million confirmed cases and nearly 7 million deaths worldwide, imposing severe burdens on public health systems, economies, and societies globally [2,3]. Although individual susceptibility to COVID-19 is influenced by social and pathological factors such as age, comorbidities, and behavior [4,5], increasing evidence highlights the critical role of environmental determinants in viral transmission and disease severity [6].

Among various environmental factors, ambient air pollution has received considerable attention. Pollutants such as nitrogen dioxide, sulfur dioxide, and particularly particulate matter with an aerodynamic diameter of 2.5 micrometers or less (PM_2.5) are suspected to exacerbate both the spread and severity of respiratory viruses such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [7]. Due to its small size, PM_2.5 can penetrate deeply into the lungs, inducing inflammation, impairing alveolar function, and potentially increasing susceptibility to severe respiratory infections [8,9]. This biological plausibility is supported by numerous studies linking PM_2.5 exposure to adverse outcomes in other respiratory diseases [10].

The association between PM_2.5 and COVID-19 can be conceptualized through 2 distinct temporal pathways. Short-term exposure, typically assessed over periods of days or weeks, is thought to trigger acute inflammatory responses and transiently impair local immune defenses in the respiratory tract, thereby increasing immediate susceptibility to infection following viral exposure [11]. In contrast, long-term exposure, reflecting chronic exposure over months or years, may contribute to the development of underlying comorbidities, including cardiovascular disease, chronic respiratory conditions, and diabetes [12-14]. These pre-existing conditions are well-established risk factors for severe COVID-19 outcomes, including hospitalization and mortality [15,16].

Early research conducted during the pandemic in high-income countries (HICs) provided compelling evidence supporting this association. Studies from Italy, the United States, and Milan consistently demonstrated significant positive associations between ambient PM_2.5 concentrations and increased rates of COVID-19 cases and mortality [17-19]. These findings raised urgent questions regarding the potential magnitude of risk in regions experiencing substantially higher levels of air pollution.

This concern is particularly acute in the low-income and middle-income countries (LMICs) of Asia. Recent air quality reports [20] underscore the severity of the problem, indicating that major Asian countries such as India (58.1 μg/m³) and China (31.1 μg/m³) report annual PM_2.5 concentrations that are approximately 6 times to more than 11 times higher than the WHO’s air quality guideline of 5 μg/m³ [21]. The region contains many of the world’s most polluted cities and countries, a situation frequently driven by rapid urbanization, economic development pressures, and limited capacity for air quality management [20,22-24]. Despite this elevated vulnerability, quantitative evidence linking PM_2.5 exposure to COVID-19 outcomes in this specific context remains fragmented. Although several individual studies have been published, to our knowledge, no systematic review and meta-analysis has synthesized this evidence to generate consolidated risk estimates.

Therefore, to address this critical knowledge gap, this study aimed to systematically review and meta-analyze the available evidence from Asian LMICs. Based on the proposed biological mechanisms, we formulated the following hypotheses: (1) both short-term and long-term exposure to PM_2.5 are positively associated with an increased risk of COVID-19 infection; and (2) long-term exposure to PM_2.5, through its contribution to the development of underlying comorbidities, is positively associated with an increased risk of COVID-19 mortality.

METHODS

This systematic review and meta-analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [25].

Search Strategy

A comprehensive literature search was conducted across 7 electronic databases: PubMed, Scopus, BioMed Central, the Cochrane Library, ProQuest, SAGE Journals, and Google Scholar. The search strategy combined Medical Subject Headings (MeSH) terms and relevant keywords related to 3 core concepts: (1) air pollution (e.g., “particulate matter,” “PM_2.5”); (2) COVID-19 (e.g., “COVID-19,” “SARS-CoV-2”); and (3) health outcomes (e.g., “infection,” “mortality,” “morbidity”). The full search string is provided in Supplemental Material 1. The search included articles published up to December 31, 2024, and was restricted to English-language publications. Two reviewers (FR, AR) independently screened titles and abstracts, followed by full-text review to determine final eligibility.

Study Selection

Studies were included if they met the following criteria: (1) Study design: Observational studies (e.g., cohort, time-series, ecological, cross-sectional, case-control); (2) Population: Studies conducted in Asian countries classified as low-, lower-middle-, or upper-middle-income by the World Bank [26]; (3) Exposure: Outdoor ambient PM_2.5 concentration. For the purpose of this review, studies were categorized according to their exposure assessment window. Short-term exposure was defined as studies assessing the effects of PM_2.5 concentrations measured over daily or weekly periods. Long-term exposure was defined as studies assessing the effects of average PM_2.5 concentrations measured over several months or years; (4) Outcome: COVID-19 infection (e.g., cases, incidence) and/or mortality (e.g., deaths, case-fatality rate); (5) Effect estimate: Reported an effect estimate (e.g., risk ratio [RR], odds ratio [OR], or hazard ratio) with a 95% confidence interval (CI) for the association.

The exclusion criteria were: non-original research (e.g., reviews, editorials), non-English articles, studies focusing exclusively on indoor air pollution, or studies not providing a quantitative effect estimate.

Data Extraction and Risk of Bias Assessment

Two reviewers independently extracted data using a standardized, pre-piloted data extraction form. Extracted variables included study identification, study design, geographic location, population characteristics, PM_2.5 exposure metrics, outcome definitions, effect estimates, and adjusted covariates. The risk of bias for each included study was assessed using the Office of Health Assessment and Translation (OHAT) Risk of Bias tool, which was selected for its suitability in evaluating environmental epidemiology studies [27,28]. This tool assigns studies to 1 of 3 confidence tiers (tier 1: high, tier 2: moderate, tier 3: low) based on an evaluation of multiple methodological subdomains. For this review, the assessed subdomains were: (1) exposure assessment, (2) outcome assessment, (3) confounding adjustment, (4) selection bias, (5) attrition or exclusion bias, (6) selective reporting bias, and (7) other potential sources of bias. Any discrepancies during screening, data extraction, or risk-of-bias assessment were resolved through discussion with a third reviewer.

Data Synthesis and Analysis

Pooled effect sizes (RR/OR) were calculated using a random-effects model to account for anticipated heterogeneity across studies [29]. Statistical heterogeneity was quantified using the I² statistic, with values of <40%, 40–75%, and >75% interpreted as low, moderate, and substantial heterogeneity, respectively [30]. To explore potential sources of heterogeneity, subgroup analyses were conducted based on geographic location and mean background PM_2.5 concentration, categorized as ≤50 μg/m³ versus >50 μg/m³.

Potential publication bias was evaluated through visual inspection of funnel plot asymmetry and was formally assessed using the Egger regression test. To assess the robustness of the primary findings, 2 sensitivity analyses were performed: a leave-one-out analysis and an analysis excluding studies assessed as having a high risk of bias. Statistical analyses were primarily conducted using the meta package in R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria), while selected data visualizations were generated using Python.

Ethics Statement

The study protocol was preregistered with the International Prospective Register of Systematic Reviews (PROSPERO; registration No. CRD42022316008). Institutional review board approval was not required, as this study was a meta-analysis of previously published data.

RESULTS

Study Search and Selection

The initial database search yielded 649 articles. After the removal of 249 duplicate records, 400 unique articles were screened based on titles and abstracts. Following full-text assessment of 78 articles, 14 studies met all inclusion criteria and were included in the systematic review and meta-analysis. The detailed study selection process is illustrated in the PRISMA flow diagram (Figure 1).

Study Characteristics

Of the 14 included studies, most were conducted in China (11 studies), with the remaining studies originating from India (2 studies) and Iran (1 study). No studies from low-income Asian countries met the inclusion criteria. Time-series designs were the most common study type (10 studies), followed by ecological (2 studies) and cohort (2 studies) designs. The key characteristics of each included study are summarized in Table 1 [31-44].

Association between short-term PM_2.5 exposure and COVID-19 infection

The meta-analysis of 10 studies assessing short-term PM_2.5 exposure identified a statistically significant positive association with the risk of COVID-19 infection. An increase in short-term PM_2.5 concentration was associated with a 12% higher risk of infection (pooled RR, 1.12; 95% CI, 1.07 to 1.18; p<0.001). Substantial heterogeneity was observed across studies included in this analysis (I²=76.49%). The corresponding forest plot is presented in Figure 2A.

Association between long-term PM_2.5 exposure and COVID-19 infection

Similarly, the meta-analysis of 3 studies evaluating long-term PM_2.5 exposure demonstrated a statistically significant positive association with COVID-19 infection risk. Long-term PM_2.5 exposure was associated with a 41% increased risk of infection (pooled RR, 1.41; 95% CI, 1.28 to 1.56; p<0.001). This analysis exhibited very high heterogeneity (I²=99.27%). The forest plot for this analysis is shown in Figure 2B.

Association between short-term PM_2.5 exposure and COVID-19 mortality

For mortality outcomes, 3 studies assessed the association with short-term PM_2.5 exposure. The pooled analysis demonstrated a statistically non-significant trend toward an increased risk of COVID-19 mortality (RR, 1.37; 95% CI, 0.80 to 2.33), as illustrated in Figure 3. Although the point estimate suggests an elevated risk, the association was not statistically significant because the CI crossed the null value of 1.0. Substantial heterogeneity was also observed in this analysis (I²=89.72%). No eligible studies were identified that examined the association between long-term PM_2.5 exposure and COVID-19 mortality.

Risk of Bias Assessment

The overall confidence rating for each included study was assigned to 1 of 3 tiers according to the OHAT framework, following a structured evaluation of prespecified subdomains. Tier 1 (high confidence) was assigned to studies with a low risk of bias across all or most key subdomains, reflecting robust methodological quality. Tier 3 (low confidence) was assigned to studies with a high or critical risk of bias in 1 or more key subdomains that could substantially affect result interpretation. Tier 2 (moderate confidence) included studies that did not meet the criteria for either tier 1 or tier 3, indicating the presence of methodological concerns that were not considered fatal to the study conclusions.

The risk-of-bias assessments for the included studies are summarized in the Supplemental Material 2. Overall, 6 studies were judged to have a low risk of bias across all evaluated domains. The remaining 8 studies raised some concerns, primarily categorized as unclear or high risk of bias. A recurring issue was identified in the “Exposure Assessment” domain, frequently attributable to reliance on data from fixed ambient monitoring stations. Such approaches may inadequately capture individual-level exposure and may introduce measurement error. Despite these concerns, no studies were excluded from the meta-analysis due to high risk of bias. A detailed table presenting the ratings and justifications for each bias domain by study is provided in the Supplemental Material 3.

Publication Bias

To evaluate potential publication bias in the analysis of short-term PM_2.5 exposure and COVID-19 infection, a funnel plot was generated (Supplemental Material 4). Visual inspection indicated reasonable symmetry around the pooled effect estimate. This observation was supported by the Egger regression test, which did not indicate statistical significance (p=0.135). Collectively, these findings suggest a low likelihood of publication bias in this analysis.

Subgroup Analysis

To explore potential sources of heterogeneity, subgroup analyses were conducted according to geographical location and mean background PM_2.5 concentration. Analyses stratified by geographical location demonstrated positive associations across all regional subgroups (China, India, and Iran). However, the wide and overlapping confidence intervals suggest that broad geographical location is unlikely to be the primary driver of heterogeneity (Supplemental Material 5). A further subgroup analysis was performed for short-term exposure studies based on mean background PM_2.5 concentration, using a threshold of 50 μg/m³ (Supplemental Material 6). Statistically significant positive associations were observed in both the moderate/low concentration group (9 studies; pooled RR, 1.21; 95% CI, 1.13 to 1.29) and the high concentration group (7 studies; pooled RR, 1.10; 95% CI, 1.05 to 1.16). The test for subgroup differences was statistically significant (p<0.001), indicating that the magnitude of the association differed between exposure strata. High heterogeneity persisted in both subgroups (I²=96.53 and 82.78%, respectively). These results are summarized in Table 2.

Sensitivity Analysis

Two sensitivity analyses were conducted to assess the robustness of the primary findings. First, a leave-one-out analysis demonstrated that the pooled effect estimate for COVID-19 infection remained stable and statistically significant following the sequential exclusion of individual studies. Across all iterations, the pooled OR remained above 1.0, with 95% CIs that did not cross the null value, indicating that no single study unduly influenced the overall result (Supplemental Material 7). Second, an analysis excluding studies assessed as having a high risk of bias [33,44] also yielded a statistically significant positive association (pooled OR, 1.15; 95% CI, 1.09 to 1.21). This estimate was only modestly attenuated compared with the primary analysis including all studies (pooled OR, 1.20; 95% CI, 1.14 to 1.26). Together, these sensitivity analyses indicate that the overall conclusions are robust and not driven by individual influential studies or by those with higher risk of bias (Supplemental Material 8).

DISCUSSION

This systematic review and meta-analysis synthesized the available evidence on the association between PM_2.5 exposure and COVID-19 outcomes, with a specific focus on the highly vulnerable context of LMICs in Asia. Our primary findings present a nuanced picture: both short-term (RR, 1.12) and long-term (RR, 1.41) exposure to PM_2.5 were significantly associated with an increased risk of COVID-19 infection, with the magnitude of the association notably larger for long-term exposure. In contrast, our analysis of studies examining short-term PM_2.5 exposure and COVID-19 mortality did not identify a statistically significant association, while evidence regarding long-term exposure and mortality was absent. A key finding from the subgroup analysis was that the association between short-term PM_2.5 exposure and infection appeared paradoxically stronger in regions with lower background pollution levels. All analyses were characterized by substantial heterogeneity, underscoring the complexity of the available evidence base.

Our finding that both short-term and long-term PM_2.5 exposure was associated with increased infection risk is consistent with the broader literature from HICs. However, the separation of short-term and long-term exposure analyses allows for a more detailed interpretation of potential biological mechanisms. The significant association observed with short-term exposure (RR, 1.12) likely reflects an acute-phase response, whereby short-term pollution spikes trigger immediate inflammatory processes and impair local respiratory immune defenses [45]. This interpretation is supported by evidence from HICs, including a test-negative case-control study conducted in the Netherlands, which reported that short-term PM_2.5 exposure was positively associated with increased odds of testing positive for SARS-CoV-2 [46], and a review noting increased emergency department visits for pneumonia in New York linked to PM_2.5 exposure [47].

Notably, the estimated effect size for long-term exposure was substantially larger (RR, 1.41), suggesting a chronic and cumulative effect in which prolonged inhalation of PM_2.5 compromises respiratory system integrity and contributes to the development of underlying comorbidities. This proposed mechanism is supported by a large cohort study in Northern Ireland, which found that long-term PM_2.5 exposure was robustly associated with increased reports of chronic illness and respiratory symptoms [48]. The stronger association observed for long-term exposure in Asian LMICs may be more pronounced than that reported in HICs, potentially reflecting higher baseline pollution levels and a greater prevalence of pre-existing health conditions in the studied populations [49]. For comparison, a nationwide study conducted in France reported that a 1 μg/m³ increase in long-term PM_2.5 exposure was associated with up to a 27.1% increase in the COVID-19 mortality rate. Although this effect size is substantial, it differs in both magnitude and outcome from our findings, which primarily relate to infection risk rather than mortality [50].

In contrast, the absence of a clear, statistically significant association between PM_2.5 exposure and COVID-19 mortality in our pooled analysis represents a critical finding that requires careful interpretation. This result should not be interpreted as definitive evidence of no effect. Rather, the included studies yielded conflicting findings. For example, high-quality research conducted by Singh [38] in Delhi reported a large and statistically significant increase in mortality risk associated with PM_2.5 exposure, whereas findings from Hadei et al. [32] in Iran did not detect a significant association between PM_2.5 and COVID-19 mortality. Additionally, another study reporting a positive association, by Jiang and Xu [34], was judged to have a critical risk of bias due to concerns regarding outcome data quality and was therefore not relied upon in the evidence synthesis. Several factors likely contribute to this lack of clarity. Most notably, statistical power was limited by the small number of studies reporting mortality outcomes. Furthermore, the very high heterogeneity observed (I²=85%) suggests that the effect of PM_2.5 on mortality may genuinely vary across populations. Once individuals develop severe COVID-19, other factors, including access to and quality of critical care, the prevalence of comorbid conditions, and individual genetic susceptibility, may become dominant determinants of survival, potentially obscuring the more subtle contribution of prior air pollution exposure [51-54].

The substantial heterogeneity observed across all analyses (I²>76%) is itself a critical finding. Although subgroup analyses based on geographical location and background PM_2.5 concentration were conducted, high heterogeneity persisted within each subgroup, suggesting that these factors alone do not account for the observed variability. Instead, heterogeneity is likely driven by other unmeasured study-level characteristics. The broader literature indicates that variables such as population density, ambient temperature, temporal COVID-19 incidence patterns, and vaccination coverage can substantially influence viral transmission dynamics and disease severity [55]. Because these data were not consistently reported across the included studies, they could not be incorporated into subgroup analyses, but they remain important potential contributors to heterogeneity. Interestingly, the subgroup analysis of short-term PM_2.5 exposure and infection risk yielded a counterintuitive result: the pooled risk estimate was significantly higher in studies conducted in settings with moderate-to-low background pollution (RR, 1.21) compared with those in high-pollution settings (RR, 1.10), with the between-group difference reaching statistical significance (p<0.001) (Table 2). This unexpected pattern may reflect a non-linear or “saturation” effect, whereby incremental increases in PM_2.5 at already high pollution levels produce a smaller relative impact on health outcomes [56,57]. Alternatively, the finding may be influenced by differences in pollutant composition, population susceptibility, or unmeasured contextual factors such as public health interventions that correlate with average pollution levels. This paradoxical result underscores an important area for future investigation.

These findings carry substantial implications for public health policy in Asian LMICs. The evidence linking both short-term and long-term PM_2.5 exposure to higher COVID-19 infection risk reframes air quality management as a fundamental pillar of pandemic preparedness [58]. Specifically, the short-term findings support the implementation of public health alert systems that notify vulnerable populations to adopt protective measures during periods of elevated pollution. Meanwhile, the stronger associations observed for long-term exposure underscore the urgency of sustained investments in policies aimed at reducing ambient air pollution, including stricter emission standards and transitions to cleaner energy sources, as a critical strategy for lowering baseline population vulnerability to future respiratory epidemics [59-61].

To build upon these findings and inform more precise, evidence-based policies, further research is critically needed. There is a pressing need for epidemiological studies conducted across a broader range of Asian LMICs, particularly in South and Southeast Asia, to enhance the generalizability of the evidence. Future research should prioritize individual-level exposure assessment to address limitations associated with fixed-site monitoring data, a constraint explicitly acknowledged in high-quality studies such as Cao et al. [31] and Hadei et al. [32], in which this domain was rated as having a “probably low” risk of bias due to its ecological nature. Finally, more advanced analytical studies are needed to examine potential synergistic effects between PM_2.5, co-pollutants, and meteorological variables, an approach exemplified by studies such as Zhou et al. [43] included in this review.

This study has several key strengths. The systematic review followed a rigorous, preregistered protocol, and the detailed, study-by-study risk-of-bias assessment enabled a nuanced interpretation of the evidence. The robustness of the primary finding for infection risk was confirmed through 2 sensitivity analyses, including an analysis excluding studies with a high risk of bias and a leave-one-out analysis, both of which demonstrated that the overall conclusion remained stable. Furthermore, formal assessment using a funnel plot and the Egger regression test revealed no evidence of significant publication bias (p=0.135), thereby increasing confidence in the pooled estimates. Finally, an initial effort was made to systematically explore the substantial heterogeneity through subgroup analyses, which yielded informative and hypothesis-generating insights.

Nevertheless, several limitations should be acknowledged. The primary limitation is the substantial and largely unexplained heterogeneity observed across all analyses, which could not be fully accounted for even after exploratory subgroup analyses. A second key limitation is the restricted geographical scope of the included studies, which were conducted exclusively in China, India, and Iran. As a result, extrapolation of these findings to all Asian LMICs, particularly regions such as Southeast Asia, should be undertaken cautiously, given potential differences in pollution composition, population characteristics, and healthcare infrastructure. Finally, the inherent risk of ecological fallacy associated with the aggregate-level study designs included in this review means that the findings cannot be directly interpreted as confirming individual-level risk without further investigation.

CONCLUSION

This systematic review and meta-analysis provides robust and consistent evidence that exposure to ambient PM_2.5 is a significant risk factor for COVID-19 infection in LMICs in Asia. Although the available evidence was insufficient to establish a definitive association with mortality, owing to substantial heterogeneity and a limited number of studies, the clear and stable relationship with infection risk represents an important public health finding. These results underscore the urgent need to integrate air quality control policies as a core component of public health preparedness and response strategies. Reducing ambient air pollution is not only a long-term environmental objective but also an immediate and essential strategy to mitigate the burden of current and future respiratory pandemics in vulnerable populations.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Rahmadina F. Data curation: Rahmadina F, Ramadona AL. Formal analysis: Rahmadina F. Funding acquisition: None. Methodology: Rahmadina F, Ahmad RA. Project administration: Rahmadina F. Writing – original draft: Rahmadina F. Writing – review & editing: Rahmadina F, Ahmad RA, Ramadona AL.

Figure. 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of literature selection. PM_2.5, aerodynamic diameter of 2.5 micrometers or less.

Figure. 2.

A forest plot of the association between (A) short-term and (B) long-term exposure to PM_2.5 and COVID-19 infection. PM_2.5, aerodynamic diameter of 2.5 micrometers or less; COVID-19, coronavirus disease 2019; CI, confidence interval; RE, random-effects.

Figure. 3.

A forest plot of the association between short-term PM_2.5 exposure and COVID-19 mortality. PM_2.5, aerodynamic diameter of 2.5 micrometers or less; COVID-19, coronavirus disease 2019; CI, confidence interval; RE, random-effects.

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