Lower respiratory infections, such as pneumonia and bronchiolitis, are the sixth leading cause of death in all age groups, resulting in ~2.4 million deaths worldwide in 2016 (1). The burden of lower respiratory infections is unevenly distributed across the world and is primarily born in the developing countries with socioeconomically disadvantaged communities, where proper nutrition, clean fuel, sanitation, and clean air are unavailable or inadequate (2, 3). China has experienced a staggering economic growth in the past 30 years, resulting in a steady increase in the life expectancy and improvement in the health outcomes in the country. From 1990 to 2019, the number of cases and mortalities of lower respiratory infections declined by 21.98 and 65.94%, respectively. In 2019, there were still 55.84 million cases, with 185,264.33 mortalities attributed to lower respiratory infections in China, making it the country's leading cause of mortality in children under-five (4).

Exposure to ambient particulate matter has been widely reported to be associated with lower respiratory infections (5–7). However, evidence on the association between size-specific particulate matter and lower respiratory infections, especially that from the developing countries where the level of air pollution is high, is relatively limited (8–10). Based on particle diameter, inhalable particulate matter (PM₁₀) can be divided into fine particles (PM_2.5) and coarse particles (PM_c). Most studies only focused on the health effects of PM_2.5, while the effects of PM₁₀ and PM_c remain inconclusive.

The previous time-series studies that examined the association between the size-specific particulate matter and the risk of adverse health outcomes often reported the odds ratio or excess risk (ER) estimates per 1 or 10 μg/m³ (11– 13). However, these effect size estimates ignore the underlying statistical distributions of the air pollutants and may not be comparable across the size-specific particulate matter. In this study, we introduced the counterfactual analyses to effectively compare the potential reduction of acute lower respiratory infection (ALRI) hospitalizations (counterfactual outcomes) associated with the size-specific particulate matter (14). These counterfactual outcomes, which accounted for the statistical distributions of air pollutants, can be directly comparable for different particulate matter and, therefore, have more public health implications for policymakers.

In this current study, we investigate the association between the size-specific particulate matter and the outpatient visits of ALRI. Beyond these analyses, we further employed a counterfactual approach to investigate the potential percent reduction of ALRI outpatient visits if the levels of particulate matter were as low as those recommended by the WHO.

Figure 1 presents the geographical location of Guangzhou and the sample hospital, as well as the geographical distribution of the air monitoring stations in Guangzhou. A total of 105,639 pediatric outpatient visits were included in the study, with the following breakdown of cases: 35,310 pneumonia, 68,218 bronchiolitis, and 2,111 asthma. Table 1 shows the summary statistics of ALRI subtypes, size-specific particulate matter (PM₁₀, PM_c, and PM_2.5), and gaseous pollutants (SO₂, NO₂, and O₃). The daily averages (SD) of pneumonia, bronchiolitis, and asthma cases were 12.5 (9.1), 24.3 (11.5), and 0.8 (1.4), respectively. The mean concentrations of PM₁₀, PM_c, and PM_2.5 in our study were 58.3, 21.0, and 37.8 μg/m³. The mean (SD) of temperature and relative humidity was 22.8°C (5.9) and 81.8% (10.2%), respectively.

Figure 2 shows the correlation plot of the air pollutants and meteorological variables in our sample. All the Pearson's correlation coefficients were statistically significant except for the correlation between NO₂ and O₃. PM₁₀ was significantly and strongly correlated with PM_c and PM_2.5 (Pearson's correlation coefficients: 0.81 and 0.93); NO₂ was moderately correlated with particulate matters (Pearson's correlation coefficients for PM₁₀, PM_c, and PM_2.5: 0.78, 0.66, and 0.70). Meteorological variables were negatively correlated with air pollutants except for the positive correlation between temperature and O₃.

Table 2 exhibits the ER of pneumonia, bronchiolitis, and asthma outpatient visits associated with per 10 μg/m³ increase in PM_2.5, PM_c, and PM₁₀ at lag03. The results revealed that size-specific particulate matter were significantly associated with pneumonia, bronchiolitis, and asthma, respectively, in single-pollutant models, where the ER of PM_c was the largest, followed by that of PM_2.5 and PM₁₀. The results were consistent and robust in two-pollutant models with further adjustment for SO₂, NO₂, and O₃, except for those asthma models controlling for NO₂. The corresponding exposure–response non-linear curves for the daily particulate matter and log relative risk are shown in Supplementary Figure 1.

Similar patterns of ER of ALRI outpatient visits associated with per 10 μg/m³ increase in the size-specific particulate matter could be observed in Figure 3. Each 10 μg/m³ increase in PM_2.5, PM_c, and PM₁₀ was associated with the outpatient visits for pneumonia, bronchiolitis, and asthma on different lag days. In contrast, the effects of the size-specific particulate matter on asthma are less robust: the moving average lags of PM_c, and PM_2.5 were still significantly associated with the ALRI outpatient visits, but the effects of lag0 to lag5 of PM_c and PM_2.5 and different lags of PM₁₀ were non-significant or at borderline significant. Sensitivity analyses using the different degrees of freedom for splines of temporal trends and temperature showed a generally consistent pattern (Supplementary Table 2).

Table 3 presents the estimated ER with 95% CI of pneumonia, bronchiolitis, and asthma stratified by sex, age group, and season, where the bold numbers indicate the significant differences across strata. We observed that each 10 μg/m³ increase in PM₁₀, PM_c, and PM_2.5 was consistently associated with significantly different effects on pneumonia outpatient visits by sex and season groups. Similar differential effects were observed for the bronchiolitis associated with increases in PM₁₀ and PM_c by different season strata, but not for PM_2.5. However, the differential effects across strata were much less significant for the asthma outpatient visits: it was only significantly different between the warm and cold seasons.

Table 4 shows the proportion reduction of ALRI (pneumonia, bronchiolitis, and asthma) outpatient visits attributable to PM_2.5, PM_c, and PM₁₀ in Guangzhou from 2013 to 2019 using a counterfactual analysis framework (15 μg/m³ for PM_2.5, 30 μg/m³ for PM_c, and 45 μg/m³ for PM₁₀). We found that PM_2.5 was associated with the largest decline in ALRI outpatient visits (pneumonia: 11.07%, 95% CI: [7.99, 14.30%]; bronchiolitis: 6.30% [4.17, 8.53%]; asthma: 8.14% [2.65, 14.33%]) if the levels of air pollution were reduced to the level of the reference guidelines.

In this study, we observed statistically significant ERs and a potential decline of ALRI (such as pneumonia, bronchiolitis, and asthma) outpatient visits associated with the size-specific particulate matter. The results were consistent in exposure assessment using different lags (lag 0–5 and moving averages of 1–3 days), two-pollutant models adjusting for SO₂, NO₂, and O₃, and various degrees of freedom. In counterfactual analyses that are of more public health significance, PM_2.5 was associated with the largest decline in the ALRI outpatient visits if the exposure was as low as the WHO reference guideline.

Consistent with a previous study (18), we observed dissimilar effect estimates associated with the size-specific particulate matter, and the largest ER was found to be that of PM_c, followed by that of PM_2.5 and PM₁₀. However, these results should be interpreted with caution as PM_2.5, PM_c, and PM₁₀ have different means and standard deviations: the mean of PM_c in our sample (21.0 μg/m³) was lower than the reference level (30 μg/m³); the SD of PM_c (9.9 μg/m³) was much smaller compared with that of PM_2.5 (21.2 μg/m³) and PM₁₀ (28.1 μg/m³). Therefore, the ER of ALRI associated with PM_c appeared to be the largest, which likely resulted from its smaller SD.

We found a larger effect of particulate matter–ALRI association among men than women, which is similar to the results of the sex-specific effects of particulate matter pollution reported previously (19). This may be due to the biological differences between men and women populations, such as hormones, sizes of airway diameters and lung sizes, and build, which will, in turn, result in the difference in the transport of pollutants and tissue deposition (17, 32). In addition, the observed associations between particulate matter and ALRI were stronger during the cold season, which is in line with the several previous studies (26, 33–35). There are several possible biological mechanisms, such as season-specific behavior, differences in PM_2.5 levels, constituent, and etiologic agents, which may be responsible for this seasonal difference.

The effects of particulate matter pollution on ALRI did not seem to be confounded by SO₂ and O₃. However, the associations between particulate matter pollution and ALRI decreased after adjusting for NO₂, in particular, the particulate matter-asthma associations became non-significant. It was difficult to ascertain their potential effects especially given the potential multicollinearity issue, possibly because NO₂ was highly correlated with particulate matter (Figure 2).

Given the limitation that the calculation of ER largely depends on the statistical distribution of the exposures, we further examined the potential proportion declination that would occur if exposure to size-specific particulate matter were reduced to the WHO recommended levels (15 μg/m³ for PM_2.5, 30 μg/m³ for PM_c, and 45 μg/m³ for PM₁₀). Our counterfactual approach calculated the difference between the observed true number of hospitalizations and the estimated number of hospitalizations in counterfactual scenarios (the WHO recommended levels of air pollutants). Because the concentration of air pollutants for each person was input into the statistical models of the counterfactual analysis, this empirical approach is not subject to the underlying statistical distributions of the air pollutants. Our counterfactual analysis suggested that reducing PM_2.5 to the WHO reference was associated with the largest potential decline in ALRI outpatient visits, followed closely by the reduction of PM₁₀, while reducing PM_c to the WHO reference is associated with the lowest potential for a decline in ALRI outpatient visits, which is likely explained by the fact that the mean level of PM_c (21.0 μg/m³) in our sample is lower than that of the WHO reference level (30 μg/m³).

Our counterfactual analysis results have a more practical public health meaning than those of ER. The implication that reducing the level of PM_2.5 may be associated with the largest decline in ALRI outpatient visits is consistent with the previous studies reporting about the toxicity of smaller-sized particulate matter on lower respiratory infection hospitalizations (36–40). For example, Wang et al. specifically focused on the association between the size-specific particulate matter and childhood pneumonia, and they reported a graded impact of the size-specific particulate matter on the childhood pneumonia (PM₁ > PM_2.5 > PM₁₀). Smaller-sized particulate matter is more likely to enter the smaller airways and cause severe health consequences.

Although the air quality has been substantially improved attributable to the effort of air quality management in China over the past decade (41, 42). The average level of particulate matter (especially PM_2.5 and PM₁₀) is still above the WHO recommended level. Northern Chinese cities with the high population densities can experience anomalously high levels of air pollution during the winter (43). Our results highlight the importance of focusing on the smaller-sized particulate matter due to its harmful effects on ALRI outpatient visits.

This study should be interpreted in view of several limitations. First, we used daily aggregated data to evaluate the short-term effect of particulate matter on health outcomes, but this aggregated nature of data could be subject to ecological bias. Second, a city-wide average concentrations of air pollution was used to represent the population exposure level, which could lead to exposure misclassification. Third, we included a relatively small number of asthma outpatient visits, which led to unstable point estimates and CIs for asthma. Fourth, since we used secondary data collected from the hospital administrative database, some important confounders (such as maternal smoking, prenatal care, and BMI) were not available to us. Lastly, we only used data from a single hospital, which limits the applicability of the results to the other regions of China.

Nonetheless, this study has several strengths. First, this is the first study to investigate the association between the size-specific particulate matter and subtypes of ALRI outpatient visits, while previous studies either reported the association between PM_2.5 and subtypes of ALRI outpatient visits or the association between the size-specific particulate matter and overall ALRI hospitalization without details on subtypes (5–7). Second, we used the counterfactual analyses to estimate the potential percent reduction in ALRI outpatient visits compared with the WHO-recommended levels. The results of counterfactual analyses have more substantial public health significance compared with ER, OR, and any other estimates associated with a fixed amount of increase in particulate matter (such as per 10 μg/m³ increase in PM_2.5) (11–13).

In summary, this study suggests a larger potential percent of the reduction in ALRI outpatient visits if PM_2.5 could be lowered to the levels recommended by the WHO. The association between particulate matter and pneumonia outpatient visits was stronger among men patients and in the cold seasons. The results highlight the need for a consolidated effort to reduce the particulate matter pollution of smaller sizes and consequently improve the health outcomes of residents in China.

The data analyzed in this study is subject to the following licenses/restrictions: Ownership of the data does not belong to the individual. Requests to access these datasets should be directed to Chuming You, gd2hek@163.com.

ZL: conceptualization, investigation, visualization, writing—original draft, writing—reviewing, and editing. QM: investigation, visualization, and funding acquisition. QY and NC: investigation, writing—reviewing, and editing. CY: investigation, visualization, supervision, and project administration. All authors contributed to the article and approved the submitted version.

This study was supported by the Science Foundation of Guangdong Second Provincial General Hospital (2021BSGZ001).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.