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Association and interaction of O3 and NO2 with emergency room visits for respiratory diseases in Beijing, China: a time-series study

BMC Public Health volume 22, Article number: 2265 (2022) Cite this article

Abstract

Background

Ozone (O3) and nitrogen dioxide (NO2) are the two main gaseous pollutants in the atmosphere that act as oxidants. Their short-term effects and interaction on emergency room visits (ERVs) for respiratory diseases remain unclear.

Methods

We conducted a time-series study based on 144,326 ERVs for respiratory diseases of Peking University Third Hospital from 2014 to 2019 in Beijing, China. Generalized additive models with quasi-Poisson regression were performed to analyze the association of O3, NO2 and their composite indicators (Ox and Oxwt) with ERVs for respiratory diseases. An interaction model was further performed to evaluate the interaction between O3 and NO2.

Results

Exposure to O3, NO2, Ox and Oxwt was positively associated with ERVs for total respiratory diseases and acute upper respiratory infection (AURI). For instance, a 10 μg/m3 increase in O3 and NO2 were associated with 0.93% (95%CI: 0.05%, 1.81%) and 5.87% (95%CI: 3.92%, 7.85%) increase in AURI at lag0-5 days, respectively. Significant linear exposure–response relationships were observed in Ox and Oxwt over the entire concentration range. In stratification analysis, stronger associations were observed in the group aged < 18 years for both O3 and NO2, in the warm season for O3, but in the cold season for NO2. In interaction analysis, the effect of O3 on total respiratory emergency room visits and AURI visits was the strongest at high levels (> 75% quantile) of NO2 in the < 18 years group.

Conclusions

Short-term exposure to O3 and NO2 was positively associated with ERVs for respiratory diseases, particularly in younger people (< 18 years). This study for the first time demonstrated the synergistic effect of O3 and NO2 on respiratory ERVs, and Ox and Oxwt may be potential proxies.

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Introduction

In China, respiratory emergencies are one of the major pre-hospital emergency medical service (EMS) demand and show an increasing trend in recent years [1]. Respiratory diseases are one of the leading causes of both morbidity and mortality worldwide, seriously threatening global health. Pneumonia, chronic obstructive pulmonary disease (COPD) and asthma are main contributors of both respiratory-related mortality and morbidity [2].Recent studies have shown positive associations between short-term exposure to air pollution and emergency room visits (ERVs) for respiratory diseases and cause-specific mortality [3,4,5,6].

Ozone (O3) pollution in China has been on the rise in recent years, and in most regions of the world, it is also not optimistic; Nitrogen dioxide (NO2) is a traffic-related pollutant with high levels in most parts of the world because of increasing traffic, and its production is closely related to O3 [7, 8]. Previous studies revealed that air pollutants had different effects on ERVs for respiratory diseases and they may pose a combined effect [9]. Particulate matter showed a dominant effect on respiratory visits, however, few studies have focused on the short-term effects of O3 and NO2 on ERVs for respiratory diseases, and the results varied by city, age and sex [9, 10].

Both O3 and NO2 are major gaseous pollutants with strong oxidative ability. The health effects of O3 and NO2 may not be independent, because of their common oxidative properties that can lead to oxidative stress, as well as the inextricably chemical conjunction that result from their rapid reactions in the atmosphere [8]. Therefore, there is increasing interest in using the sum of O3 and NO2 (Ox) as an indicator of the combined oxidant capacity [8] [11, 12]. In addition, it is well known that the oxidation potential of O3 is much stronger than NO2, so the term ‘redox-weighted oxidant capacity’ (Oxwt) is derived as a weighted average using redox potentials as the weights [11]. Several previous studies have been conducted on the relationship between air pollutants and disease morbidity and mortality, in which the combined atmospheric oxidant capacity is represented by the redox-weighted average of O3 and NO2 [12,13,14,15,16]. However, evidence regarding the effects of Ox and Oxwt on respiratory emergency visits is lacking.

In real-world scenarios, people are always exposed to a range of harmful air pollutants simultaneously. It is biologically plausible for the potential interaction of different pollutants on human health. For example, a case-crossover study conducted in Canada found that the association between fine particulate matter (PM2.5) and emergency room visits for myocardial infarction was stronger in areas with higher Oxwt (P-interaction < 0.001) [12]. Another time-series analysis observed a positive interaction between inhalable particles (PM10) and NO2 on non-accidental mortality in Guangzhou, China [17]. Furthermore, a panel study found that the effect of O3 on cardiac autonomic function were stronger at high levels of black carbon (BC), another surrogate indicator of traffic emissions, in children in Beijing, China [18]. It is important for assessing the overall health risk of air pollution to understand these possible interactions. But so far, the interaction between O3 and NO2 has not been investigated. To address these gaps, this time-series study was conducted to estimate the association of short-term exposure to O3, NO2 and their combined indicators (Ox and Oxwt) with ERVs for respiratory diseases and explore the interaction of O3 and NO2.

Materials and methods

Study design and population

We conducted a time-series study based on the emergency room visits data of Peking University Third Hospital (PUTH) from Jan 1, 2014 to Dec 31, 2019. For more than ten years, the number of outpatients and emergency visits of PUTH has always been in the forefront in Beijing. In 2019, the hospital served more than 4.22 million outpatients and over 300,000 emergency patients with different kinds of demographical characteristics. The data of daily hospital ERVs for respiratory diseases were collected from the hospital information system from January 2014 to December 2019. The cases were classified according to the 10th edition of the International Classification of Disease (ICD-10): (1) total respiratory diseases (TRDs, J00-J99); (2) acute upper respiratory infection (AURI, J00-J06); (3) lower respiratory tract infection (LRTI, J12-J18&J20-J22); (4) pneumonia (J12-J18); (5) chronic obstructive pulmonary disease (COPD, J41-J44); and (6) asthma (J45).

Air pollutants and meteorological data

Hourly concentrations of six major ambient air pollutants were obtained from the Chinese Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/). There were 35 air quality monitoring stations in Beijing, which could well represent the level of air pollution in Beijing. Daily average concentrations of PM2.5, PM10, NO2, sulfur dioxide (SO2), carbon monoxide (CO) and 8-h maximum concentration of O3 (O3-8 h max) were calculated during the study period. Meanwhile, daily meteorological data were collected from the National Meteorological Information Center (http://data.cma.cn/), including temperature and relative humidity (RH) in Beijing. In addition, O3 and NO2 are the two main oxidative atmospheric pollutants, and there is a mutual chemical transformation between them. This study calculated two composite indicators (Ox and Oxwt) based on previous studies. Ox was defined as the sum of NO2 and O3 concentrations indicating combined oxidant capacity [8]. Oxwt was defined as the weighted average of NO2 and O3 indicating redox-weighted oxidant capacity because O3 is more oxidizing than NO2 [8]. The calculation formula is as follows:

$$O_x=O_3+NO_2$$
(1)
$$O_x^{\mathrm{wt}}=(1.07volts(\mathrm V)\times\;{\mathrm{NO}}_2+\;2.075\mathrm{volts}(\mathrm V)\times\;{\mathrm O}_3)/3.145$$
(2)

Statistical analysis

Generalized additive models with quasi-Poisson regression were performed to analyze the association between air pollutants and ERVs for respiratory diseases according to the formula (3):

$$\mathrm{Log}\lbrack\mathrm E({\mathrm Y}_{\mathrm t})\rbrack\;=\;{\mathrm\beta}_0\;+\;{\mathrm\beta}_1{\mathrm Z}_{\mathrm t}\;+\;\mathrm{Cb}.\mathrm{temp}\;+\;\mathrm{Day}\;\mathrm{of}\;\mathrm{Week}\;+\;\mathrm{Holiday}\;+\;\mathrm{ns}(\mathrm{time},\;7\;\mathrm{df}\;\times\;\mathrm{year})\;+\;\mathrm{ns}(\mathrm{RH},\;3\;\mathrm{df})$$
(3)

where t is the day of the observation; E(Yt) is the expected value of ERVs for respiratory diseases on day t; β0 is the intercept; β1 is the regression coefficient of exposure; Zt is the moving average concentration of pollutants at different lag days; Cb.temp is a cross-basis matrix of temperature generated by the distributed lag nonlinear model (DLNM) with 4 df and a maximum lag days of 14; Day of Week (DOW) and Holiday variables are used to adjust the short-term variation; ns() is a natural cubic spline function; time is the calendar time on day t; RH is the relative humidity. The dfs for the temperature, time and RH was determined based on the common dfs used in previous studies [19,20,21,22]. Then, we plotted the exposure–response curves to characterize the associations between air pollutants and daily ERVs for respiratory diseases at different exposure concentrations [23]. We evaluated the lag effects for a maximum of 0–7 days after exposure to air pollutants and found the strongest cumulative effects at lag 0–5 days, which were finally reported.

We performed additional analyses stratified by sex (male and female), age (< 18, 18–64 and > 64 years) and season (warm: May to October and cold: November to April) with reference to previous studies [24, 25]. The formula (4) was used to test the statistical differences between different groups by calculating the 95% confidence interval (CI), which was used widely in previous studies [13, 23, 26]. For age stratification analysis, 18–64 years group was used as a reference.

$$Q_{1}-Q_{2}\pm1.96\sqrt{{\left(SE_{1}\right)}^{2}+{\left(SE_{2}\right)}^{2}}$$
(4)

For interaction analysis, NO2 or O3 concentrations were classified as low, medium, and high levels according to the 25% and 75% quartiles based on previous studies [17, 19], and then the associations between one pollutant and ERVs for respiratory diseases under different levels of the other pollutant were explored based on the formula (5) [17, 19]:

$$\mathrm{Log}\lbrack\mathrm E({\mathrm Y}_{\mathrm t})\rbrack\;=\;{\mathrm\beta}_{0}\;+\;{\mathrm\beta}_{1}{\mathrm Z}_{1}\;+\;{\mathrm\beta}_{2}({\mathrm Z}_{1}:{\mathrm Z}_{2})+\;\mathrm{Cb}.\mathrm{temp}\;+\;\mathrm{DOW}\;+\;\mathrm{Holiday}\;+\;\mathrm{ns}(\mathrm{time},\;7\;\mathrm{df}\;\times\;\mathrm{year})\;+\;\mathrm{ns}(\mathrm{RH},\;3\;\mathrm{df})$$
(5)

where Z1 and Z2 are O3 and NO2 levels (or NO2 and O3 levels), respectively; β2 is the effect of the interaction between Z1 and Z2. Other parameters are the same as in formula (3). The low concentration group of pollutants was used as the reference, so the estimated effect of O3 at a low NO2 level (or NO2 at a low O3 level) was the same as the β1 of formula (5), and the estimated effects of one pollutant at medium and high levels of another pollutant were generated based on both β1 and β2.

Sensitivity analyses were also performed to check the robustness of the results by: (1) constructing two-pollutant models by including PM2.5, PM10, SO2 or CO; (2) modifying the maximum lag time for temperature from 14 to 30 days [19, 27]; and (3) modifying the degree of freedom for calendar time from 4 to 10 [28,29,30]. We calculated the variance inflation factor (VIF) for the two-pollutant models and found the corresponding VIFs were all less than 5, indicating that the collinearity was not an issue for the two-pollutant models.

All statistical analyses were performed using R software (Version 4.0.3) with “mgcv” and “dlnm” packages. A two-sided P < 0.05 was considered statistically significant.

Results

Descriptive statistics

A total of 144,326 emergency room visits for respiratory diseases were included in this study from 2014–2019, among which 80,302 (55.6%) were acute upper respiratory infection cases, and 24,621 (17.1%) were lower respiratory tract infection cases (Table 1). During the study period (2014–2019), the average daily emergency room visits were 65.9 (range: 12 to 444). Pneumonia was the most common cause of lower respiratory tract infection. COPD and asthma cases were 2553 (1.8%) and 2740 (1.9%), respectively (Table 1). The daily mean (standard deviation, SD) concentrations of 8 h maximum O3 and NO2 were 98.1 (62.8) and 45.5 (22.5) μg/m3, respectively. The mean (SD) of temperature and relative humidity were 13.9 (11.2)°C and 50.8(19.8)%, respectively (Table 2). The daily concentration changes of air pollutants were shown in Figure S1. The O3 had high concentrations in warm season and in contrast, NO2 had high concentrations in cold season. According to the results of Spearman correlation analysis, O3 was negatively correlated with NO2 (coefficient = -0.35, P < 0.001, Figure S2A) throughout the study period, and this correlation was stronger in the cold season (coefficient = -0.45, P < 0.001, Figure S2C).

Table 1 Descriptive statistics of emergency room visits for respiratory diseases from Jan 1, 2014 to Dec 31, 2019
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Table 2 Descriptive statistics for air pollutants and meteorological conditions from Jan 1, 2014 to Dec 31, 2019 in Beijing
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Associations between ambient air pollutants and ERVs for respiratory diseases

As shown in Fig. 1, short-term exposure to O3, NO2, Ox and Oxwt was positively associated with emergency room visits for total respiratory diseases and acute upper respiratory infection. For instance, a 10 μg/m3 increase in O3 and NO2 were associated with 0.78% (95%CI: 0.14%, 1.42%) and 3.17% (95%CI: 1.82%, 4.53%) increase in total respiratory visits and 0.93% (95%CI: 0.05%, 1.81%) and 5.87% (95%CI: 3.92%, 7.85%) increase in acute upper respiratory infection, respectively. A positive correlation was observed between NO2 and COPD but not with asthma. However, NO2, O3, Ox and Oxwt were not significantly associated with lower respiratory tract infection. Figure S3 showed the exposure–response relationship between air pollutants and ERVs for respiratory diseases. O3 showed a positive correlation with total respiratory visits and acute upper respiratory infection at concentrations above 100 μg/m3, and NO2 showed a positive correlation with acute upper respiratory infection. Ox and Oxwt, on the other hand, showed positive correlations with total respiratory visits and acute upper respiratory infection over the entire concentration range, suggesting that they may be a better indicator for assessing health effects following mixed O3 and NO2 exposure.

Fig. 1
figure 1

Associations of air pollutants (O3, NO2, OX and Oxwt) with emergency room visits for respiratory diseases at lag 05 day during 2014–2019. NOTE: O3, ozone; NO2, nitrogen dioxide; OX, oxidant capacity; Oxwt, redox-weighted oxidant capacity; TRD, total respiratory disease; AURI, acute upper respiratory infection; LRTI, lower respiratory tract infection; COPD, chronic obstructive pulmonary disease

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Stratification analysis

Gender-stratified associations between air pollutants and respiratory emergency room visits were shown in Figure S4. Stronger associations were observed in females, although statistically significant gender differences were found only in the association between NO2 and lower respiratory tract infection. The results of age-stratified associations are shown in Fig. 2. We found the strongest association in the group aged < 18 years and almost all the differences were statistically significant (P < 0.05) compared with the group aged 18–64 years. Certain associations were also observed to be stronger in the group aged > 64 years than in the group aged 18–64 years, such as the association of NO2 with acute upper respiratory infection. Figure S5 showed the results of season-stratified analysis. Overall, the effects of 8 h maximum O3 were significantly greater in the warm season (all P < 0.05), while the effect of NO2 was slightly stronger in the cold season, and the difference was statistically significant only for the acute upper respiratory infection.

Fig. 2
figure 2

Associations of air pollutants (O3, NO2, OX and Oxwt) with emergency room visits for respiratory diseases stratified by age at lag 05 day during 2014–2019. NOTE: O3, ozone; NO2, nitrogen dioxide; OX, oxidant capacity; Oxwt, redox-weighted oxidant capacity; TRD, total respiratory disease; AURI, acute upper respiratory infection; LRTI, lower respiratory tract infection.*P for subgroup differences < 0.05 compared with the group aged 18–64 years

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Interaction analysis

The results of interaction analysis between O3 and NO2 were shown in Fig. 3. We found an increasing trend in the associations between O3 and total visits and acute upper respiratory infection with increasing NO2 concentrations, although no significant differences were observed. However, no similar trend was observed for NO2. The interaction between O3 and NO2 was further analyzed in various subgroups of the population. Similar results were observed in both gender groups (Figure S6). Figure 4 showed the results of interaction analysis between O3 and NO2 in different age groups. The association between O3 and total respiratory emergency room visits and acute upper respiratory infection was significantly stronger at high concentrations of NO2 in the < 18 years group (P-interaction < 0.05).

Fig. 3
figure 3

The interaction of O3 and NO2 on emergency room visits for respiratory diseases. A Effect of O3 under different NO2 levels; (B) Effect of NO2 under different O3 levels. NO2 or O3 concentrations were classified as low, medium, and high levels according to their 25% and 75% quartiles. NOTE: O3, ozone; NO2, nitrogen dioxide; TRD, total respiratory disease; AURI, acute upper respiratory infection; LRTI, lower respiratory tract infection

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Fig. 4
figure 4

The interaction of O3 and NO2 on emergency room visits for respiratory diseases in different age groups (< 18 years, 18–64 years and > 64 years). NO2 or O3 concentrations were classified as low, medium, and high levels according to their 25% and 75% quartiles. NOTE: O3, ozone; NO2, nitrogen dioxide; TRD, total respiratory disease; AURI, acute upper respiratory infection; LRTI, lower respiratory tract infection..*P for interaction < 0.05

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Sensitive analyses

Sensitive analyses indicated that, when additionally adjusting for co-pollutants by constructing a two-pollutant model, applying different lagged patterns for temperature, and using different degrees of freedom for calendar time, the associations between air pollutants and respiratory emergency room visits were generally robust, as shown in Table S1.

Discussion

Air pollution is one of the important public health problems that threaten the health of people all over the world. Previous epidemiological studies demonstrated that air pollution is associated with respiratory diseases and other adverse health effects [31, 32]. In China and many other regions around the world, the adverse health effects of O3 and traffic-related pollution (e.g., NO2) on the respiratory system of populations are receiving increasing attention. The present study investigated the associations of short-term exposure to O3 and NO2 with ERVs for respiratory diseases based on 144,326 cases from a large general hospital in Beijing. We observed that short-term exposure to air pollutants (O3 and NO2) was positively associated with respiratory emergency room visits, particularly in acute upper respiratory infection and younger people (< 18 years). The adverse health effect of O3 was significantly strengthened at high NO2 concentration levels in the < 18 years group.

Previous studies have investigated associations between air pollution and respiratory ERVs. A study was performed in Colombia and the results showed a stronger association between NO2 concentration and the percentage increases in ERVs for respiratory diseases, especially in the 5 to 9-year-old age group [33]. An association was also found between O3 concentration and increased visits for respiratory diseases in children less than 5 years of age. Szyszkowicz M et al. [5] investigated associations between ambient air pollutants (PM2.5, NO2, O3 and SO2) and ERVs for respiratory diseases stratified by sex in Canada, and found that short-term exposure to air pollution increased the risk of ERVs for upper and lower respiratory diseases among males and females. In Chengdu, China, another study suggested that for respiratory disease visits, males were affected by the combination of PM2.5 and O3, but females were affected by PM2.5 only [9]. Previous studies have generally shown that short-term exposure to O3 and NO2 is positively associated with respiratory emergency room visits, but the results vary by cities and the sensitivity to pollutants varies by age and gender. In this study, we observed positive associations of O3 and NO2 with respiratory emergency room visits, especially in acute upper respiratory infection, which provided new evidence for the short-term effects of O3 and NO2 on the human respiratory system. There are several plausible mechanisms and pathways through which air pollutants could affect the respiratory system, including direct airway irritation causing bronchoconstriction, and oxidative stress with inflammation. The reactive oxygen species (ROS) generation of air pollutants can lead to oxidative injuries and systematic inflammatory responses, such as the generation of superoxide radical [34]. Cellular level stress and inflammation may also predispose the individual to subsequent infection and allergic sensitization [35]. O3 and NO2 are the two main oxidative gaseous pollutants in the atmosphere, and can cause systematic oxidative injuries and airway inflammatory [15, 36,37,38]. A few previous literatures [8, 34, 39] have reported that Ox and Oxwt were significantly correlated with fractional exhaled nitric oxide (FeNO), which was a biomarker of airway inflammation and could be useful for assessing the respiratory adverse effects of short-term air pollution exposure. Futhermore, Oxwt should be considered as a proxy indicator of NO2 and O3. The observation was consistent with our finding that OX and Oxwt showed significantly positive correlations with ERVs for respiratory diseases over the entire concentration range. Future studies are needed to assess whether Ox and Oxwt can be used as a better indicator to estimate the effects of oxidative gaseous pollutants on respiratory diseases than O3 and NO2. There are few studies focused on the relationship between gas pollutants and ERVs for pneumonia. A meta-analysis including 21 studies showed significantly positive association between NO2 and hospital admission or ERVs for pneumonia, although no such correlation was identified regarding O3 [40]. In our study, there was no significant relationship between air pollutants (NO2, O3, Ox and Oxwt) and pneumonia, which is not entirely consistent with the previous findings. The gaseous pollutants caused damage to the cells of the respiratory tract by impairing the membrane structures of the cell, pump structures within the cell membrane, and energy system, thus increasing the risk of infection [41]. However, these effects may depend on the levels of gaseous pollutants in lower respiratory tract, incubation period after exposure, susceptibility of population, and mixed effects between air pollutants. More high-quality studies are needed in the future to confirm the relationship between gaseous pollutants and ERVs for lower respiratory tract infection.

In gender-stratified analysis, we found that females seem to be more susceptible to ambient air pollution than males, which is consistent with the results of previous studies [31, 42,43,44]. Hormones and structural/morphological differences in the respiratory system may affect the differences in risks of air pollution exposure between men and women [45]. Compared with males, females have smaller respiratory tract, so they would be subjected to greater airway reactivity under the same air pollution [32, 46]. In age-stratified analysis, for respiratory diseases, the influence of air pollution seemed to be more obvious in children aged < 18 years group and aged > 65 years than those aged 18–64 years, which might be explained by the fact that the children and the elderly are susceptible groups due to weaker resistance to diseases [32]. This similar results have been seen in many other studies as well. In Sichuan, China, children (≤ 14 years) and elderly (≥ 65 years) appeared to be more vulnerable to the effects of air pollutants including PM2.5, PM10, NO2, and SO2 [20]. Rodríguez-Villamizar LA et al. found that the effects of air pollutants on visits for respiratory diseases were greater for the 5 to 9-year-old group [33]. Children have higher breathing rates and are more likely to be outdoors, which results in higher exposure and inhalation of pollutants. In addition, children are more sensitive to pollutant stimulation due to underdeveloped lungs and smaller airway. In this study, we found a significant association between respiratory ERVs and O3 during warm season. On the contrary, the effect of NO2 on AURI was stronger during cold season. As a secondary air pollutant, surface O3 has higher concentrations in summer attributed to the more intensive sunlight and higher temperature, which favor the photochemical production of O3 [47]. In Beijing, the concentration of NO2 increased during the cold season due to the increased emissions of pollutants caused by winter heating. Meanwhile, low temperature would reduce the ability of the respiratory system to resist infection, which is related to the decrease of cilia clearance ability of the respiratory system and leukocyte phagocytosis [48]. These may be the reasons why NO2 caused stronger effects in the cold season.

Air pollution usually exists as a complex mixture and different pollutants may have potential interactions, therefore, it provides limited information to simply evaluate the health risk of a single pollutant. Thus, it is important to understand these possible potential interactions and synergy among air pollutants in order to evaluate the overall health effects of air pollution [49]. In this study, the effects on the association between O3 exposure and ERVs for respiratory diseases were stronger at high concentrations of NO2, particularly in younger people (< 18 years). The interaction mechanisms between O3 and NO2 on respiratory health effects are not entirely clear. First of all, through a series of complex photochemical reactions, a dynamic equilibrium is formed between NO, O3, and NO2 [50]. Furthermore, both O3 and NO2 are oxidative gaseous pollutants. O3 has a much stronger oxidation potential than NO2 and may have significant adverse effects even at low concentrations. Previous findings showed that low concentrations of O3 are associated with adverse cardiovascular outcomes in children, demonstrating that low concentrations of O3 can still have adverse effects in humans and that younger people may be a sensitive population. In addition, indoor O3 was found to pose a stronger adverse effect on heart rate variability in children at high levels of BC [18], which is consistent with our findings and implies a potential interaction between O3 and traffic-related pollutants on cardiopulmonary health of children. Excessive inhalation of O3 and NO2 can lead to imbalance of oxidation and anti-oxidation, thus causing oxidative stress and activating the release of inflammatory cytokines, resulting in oxidative stress damage and inflammatory response of respiratory epithelial cells. Moreover, synergistic effects of NO2 and O3 may also produce cumulative oxidative stress and thus causes more severe adverse respiratory effects [51, 52]. Generally speaking, the interactions and synergistic effects of O3 and NO2 cause more damage to respiratory system, but the mechanisms by which this damage occurs are still not fully understood. However, no similar trend was observed for NO2. The reason may be partially explained that NO2 concentration decreases (as Figure S7 showed) at high O3 concentration and thus the adverse respiratory effect is weaker.

The strength of this study is that we provide evidence of associations between gaseous air pollutants and emergency respiratory diseases in China. First, emergency department data have unique advantages in reflecting the acute effects after short-term air pollution exposure. Second, emergency department data can greatly avoid the interference of cross-regional visits (such as hospitalization data), which brings the advantage of exposure assessment. Third, this study provides the first evidence on the association of Ox and Oxwt with respiratory emergencies and interactions between O3 and NO2. However, our study still has some limitations. First, the data of ERVs were obtained from a single center within a limited area. Second, this is an ecological study that does not elucidate causality, and the findings and potential biological mechanisms still need to be confirmed by further studies. Third, the use of air pollutant data from environmental monitoring stations also has certain exposure bias, which is an inevitable limitation of ecological studies.

Conclusions

Short-term exposure to O3 and NO2 was associated with increased emergency room visits for respiratory diseases, particularly acute upper respiratory infection. Meanwhile, younger people (< 18 years) were more sensitive to O3 and NO2, and the health risk of O3 is significantly enhanced at high NO2 concentration levels. Our findings provides new evidence for the development of targeted environmental health policies for specific diseases and populations.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Abbreviations

O3:

Ozone

NO2:

Nitrogen dioxide

ERVs:

Emergency room visits

Ox:

Combined oxidant capacity

Oxwt:

Redox-weighted oxidant capacity

TRDs,:

Total respiratory diseases

AURI:

Acute upper respiratory infection

EMS:

Emergency medical service

COPD:

Chronic obstructive pulmonary disease

PUTH:

Peking University Third Hospital

ICD:

International Classification of Disease

LRTI:

Lower respiratory tract infection

PM2.5:

Fine particles

PM10:

Inhalable particles

BC:

Black carbon

SO2:

Sulfur dioxide

CO:

Carbon monoxide

O3-8 h max:

8-h maximum concentration of O3

RH:

Relative humidity

DLNM:

Distributed lag nonlinear model

DOW:

Day of Week

CI:

Confidence interval;VIF, variance inflation factor

SD:

Standard deviation

ROS:

Reactive oxygen species

FeNO:

Fractional exhaled nitric oxide

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Acknowledgements

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Funding

The study was supported by the National Natural Science Foundation of China (No. 22076006) and the Ministry of Ecology and Environment: the research of national-level ecological and environmental planning (No. 14430019). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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  1. Yuanwei Fu, Wenlou Zhang and Yan Li contributed equally to this work.

Authors and Affiliations

  1. Emergency Department, Peking University Third Hospital, Beijing, 100191, China

    Yuanwei Fu, Yan Li & Qingbian Ma

  2. Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China

    Wenlou Zhang, Hongyu Li & Furong Deng

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  2. Wenlou Zhang
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  3. Yan Li
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Contributions

YWF and WLZ conceptualized the study, analyzed the data and drafted the manuscript. YL assisted in data acquisition and development of the manuscript. HYL conceptualized the study and assisted in analysis of data. QBM and FRD conceptualized, reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Fu, Y., Zhang, W., Li, Y. et al. Association and interaction of O3 and NO2 with emergency room visits for respiratory diseases in Beijing, China: a time-series study. BMC Public Health 22, 2265 (2022). https://doi.org/10.1186/s12889-022-14473-2

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BMC Public Health

ISSN: 1471-2458

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