Background
Ambient fine particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM
2.5) is detrimental to public health [
1,
2]. Over the past decades, there has been growing evidence that ambient PM
2.5 exposure is a risk factor for developing and exacerbating asthma and allergic diseases [
1,
3‐
8]. The global population-weighted PM
2.5 concentration increased from 39.7 µg/m
3 in 1990 to 44.2 µg/m
3 in 2015 [
2]. There is growing concern that ambient PM
2.5 may contribute to the prevalence of allergic diseases and symptoms.
With rapid economic development, urbanization, and industrialization in recent decades, China has become one of the most polluted countries worldwide [
2]. PM
2.5 concentrations increased from 1990 and peaked during 2011–2013 [
9]. In the wake of the air pollution crisis, the State Council of China promulgated the toughest-ever Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 [
10]. China initiated reductions in anthropogenic PM
2.5 emissions in 2013, and population-weighted PM
2.5 concentrations rapidly decreased by 4.51 µg/m
3/year from 2013 to 2016 [
11]. However, as of the end of 2017, the entire Chinese population lived in areas with annual average PM
2.5 concentrations exceeding 10 µg/m
3 [
12] (World Health Organization (WHO) interim target 4), and 81.1% lived in areas with concentrations above 35 µg/m
3 [
13] (Chinese grade I ambient air-quality standard) [
9]. In contrast to other countries, China is currently transitioning from high to low air pollution levels, which provides an excellent opportunity to study the effects of air pollution on human health.
Short-term exposure to air pollution can exacerbate preexisting asthma [
1], allergic diseases [
4], and chronic obstructive pulmonary disease (COPD) [
14] and cause an increased risk of hospitalization or even mortality related to respiratory and allergic diseases. Long-term exposure to air pollution increases the risk of morbidity and mortality from asthma [
1,
8] and allergic diseases [
6,
15]. However, some studies did not report evidence of a positive association between ambient air pollution and asthma and allergic diseases [
16,
17]. There is an urgent need to investigate the impact of short- and long-term air pollutant exposure on allergic symptoms in a large well-documented cohort with detailed covariate data.
Most published studies on ambient PM
2.5 exposure, allergic symptoms, and asthma were mainly performed in regions with relatively low air pollution concentrations [
15,
18,
19] and young populations, such as children, adolescents, and young adults [
16,
17]. However, more than 90% of air pollution-related deaths occur in Asia and Africa, where air pollution is generally a serious problem [
10,
20]. In recent decades, the prevalence of allergic diseases such as asthma and allergic rhinoconjunctivitis has been increasing worldwide among older adults [
8,
21]. The national cross-sectional China Pulmonary Health (CPH) study revealed that the prevalence of asthma increased with age, from 2.5% in individuals aged 20 to 39 years to 5.4% in those aged 40 years or older [
21]. China, one of the countries most burdened by air pollution, is home to a fifth of the world’s older people [
22]. However, there is a lack of data with respect to the effect of ambient PM
2.5 exposure on allergic symptoms among middle-aged and elderly populations. Considering the burden of allergic diseases attributed to ambient air pollution exposure, a better understanding of whether middle-aged and elderly individuals are susceptible to this exposure should enable the design of prevention strategies.
Therefore, this study aimed to estimate the associations between allergic symptoms and short- and long-term ambient PM2.5 exposure in the Predictive Value of Inflammatory Biomarkers and Forced Expiratory Volume in 1 s (FEV1) for COPD (PIFCOPD) study, a nationwide prospective cohort study in China.
Results
The distribution of 10,142 participants (6386 females and 3756 males) by general characteristics and risk factors is summarized in Table
1. The participants had a mean age of 59.27 years (range 40–75 years), most (65%) lived in the northern region, more than half (58%) cooked at home, and only 4.9% had biomass exposure. Among the participants, 9721 had one or no allergic symptoms, and the other 421 (4.2%) had more than one allergic symptom. There were significant differences in education level, passive smoking, smoking type, cumulative smoking exposure (pack-years), household cooking, occupational exposure, family history of asthma, geographic region, and season between the two groups. The proportions of passive smokers (18% vs. 9.3%) and former or current smokers (24% vs. 19%) were higher in the ≥ 2 allergic symptoms group than in the < 2 allergic symptoms group. For education level, the proportion of participants with a high education level was low in the < 2 allergic symptoms group. A total of 256 (2.6%) of the 9721 participants in the < 2 allergic symptoms group and 39 (9.3%) of the 421 participants in the ≥ 2 allergic symptoms group had a family history of asthma. The proportion of participants who completed the allergic symptoms questionnaire in autumn was also higher in the ≥ 2 allergic symptoms group than in the < 2 allergic symptoms group (28% vs. 18%). There was a higher proportion of household cooking (71% vs. 58%) and occupational exposure (17% vs. 7.2%) in the ≥ 2 allergic symptoms group. For allergic symptoms, eye symptoms had the highest prevalence (6.1%), followed by nasal symptoms (5.6%), worsening dyspnea caused by allergens (3.2%), and wheezing (1.2%) (Table
2).
Table 1
Demographics and risk factors by allergic symptoms in the PIFCOPD population (N = 10,142)
Sex | | | | 0.797 |
Female | 6386 (63) | 6118 (63) | 268 (64) | |
Male | 3756 (37) | 3603 (37) | 153 (36) | |
Age, y | 59.27 (8.41) | 59.28 (8.42) | 59.03 (8.17) | 0.549 |
BMI group | | | | 0.684 |
< 25 kg/m2 | 5938 (59) | 5691 (59) | 247 (59) | |
25–29.9 kg/m2 | 3548 (35) | 3405 (35) | 143 (34) | |
≥ 30 kg/m2 | 656 (6.5) | 625 (6.4) | 31 (7.4) | |
Education level | | | | < 0.001 |
No schooling or primary school | 1824 (18) | 1795 (18) | 29 (6.9) | |
Middle school | 4555 (45) | 4409 (45) | 146 (35) | |
High school | 2387 (24) | 2236 (23) | 151 (36) | |
College or higher | 1376 (14) | 1281 (13) | 95 (23) | |
Passive smoking | 975 (9.6) | 901 (9.3) | 74 (18) | < 0.001 |
Smoking type | | | | 0.003 |
Never smoker | 8234 (81) | 7916 (81) | 318 (76) | |
Former or current smoker | 1908 (19) | 1805 (19) | 103 (24) | |
Cumulative smoking exposure, pack-years | | | | 0.011 |
0 | 8234 (81) | 7916 (81) | 318 (76) | |
1–19 | 706 (7.0) | 668 (6.9) | 38 (9.0) | |
≥ 20 | 1202 (12) | 1137 (12) | 65 (15) | |
Biomass exposure | 495 (4.9) | 466 (4.8) | 29 (6.9) | 0.063 |
Household cooking | 5896 (58) | 5599 (58) | 297 (71) | < 0.001 |
Occupational exposure | 771 (7.6) | 699 (7.2) | 72 (17) | < 0.001 |
Family history of asthma | 295 (2.9) | 256 (2.6) | 39 (9.3) | < 0.001 |
Geographic region | | | | < 0.001 |
North | 6627 (65) | 6283 (65) | 344 (82) | |
East | 1678 (17) | 1647 (17) | 31 (7.4) | |
Northeast | 768 (7.6) | 724 (7.4) | 44 (10) | |
Northwest | 1069 (11) | 1067 (11) | 2 (0.5) | |
Seasona | | | | < 0.001 |
Winter | 373 (3.7) | 369 (3.8) | 4 (1.0) | |
Spring | 1594 (16) | 1525 (16) | 69 (16) | |
Summer | 6325 (62) | 6095 (63) | 230 (55) | |
Autumn | 1850 (18) | 1732 (18) | 118 (28) | |
Table 2
Allergic symptoms prevalence in the PIFCOPD study population
Allergic nasal symptoms | |
Does not sometimes have an itchy, runny, or stuffy nose | 9576 (94) |
Does sometimes have an itchy, runny or stuffy nose | 566 (5.6) |
Allergic eye symptoms | |
Does not sometimes have itchy, watery, swollen or burning eyes | 9520 (94) |
Does sometimes have itchy, watery, swollen or burning eyes | 622 (6.1) |
Wheezing | |
Has not had wheezing symptoms | 10,022 (99) |
Has had wheezing symptoms | 120 (1.2) |
Worsening dyspnea caused by allergens | |
Has not had worsened shortness of breath due to exposure to dust, pollen, or pets | 9813 (97) |
Has had worsened shortness of breath due to exposure to dust, pollen or pets | 329 (3.2) |
≥ 2 allergic symptoms | 421 (4.2) |
The average residential PM
2.5 concentration was 35.19 (27.32) µg/m
3 (range 1–288 µg/m
3) on the day the allergic symptoms questionnaire was administered (lag0 day) and 34.73 (20.93) µg/m
3 (range 2.00–197.12 µg/m
3) for the moving average of 0-to-7 day (lag0–7 day). For long-term exposure, the 1-, 3- and 5-year annual average PM
2.5 concentrations were 50.56 (15.27) µg/m
3, 54.08 (16.92) µg/m
3, and 59.79 (17.88) µg/m
3, respectively (Table
3). The results of our study showed that during 2013–2020, the annual average concentrations of PM
2.5 among ten regions were 81.97 µg/m
3, 73.55 µg/m
3, 67.21 µg/m
3, 62.35 µg/m
3, 58.53 µg/m
3, 49 µg/m
3, 46.09 µg/m
3, and 44.92 µg/m
3, respectively. The annual average PM
2.5 concentration decreased by 45.2% from 2013 (81.97 µg/m
3) to 2020 (44.92 µg/m
3) among the ten regions (Additional file 1: Fig. S1). PM
2.5 concentrations exhibited seasonal variation, with the highest concentration in winter (90.08 µg/m
3, 5-year average), followed by autumn (56.26 µg/m
3), spring (55.33 µg/m
3), and summer (44.99 µg/m
3) (Additional file 1: Fig. S2).
Table 3
Distribution of the estimated ambient temperature and PM2.5 concentrations at residence
Short-term PM2.5, µg/m3 | | | |
Lag0 day | 35.19 (27.32) | 30.00 (25.00) | 1.00 to 288.00 |
Lag0–7 day | 34.73 (20.93) | 31.75 (22.00) | 2.00 to 197.12 |
Long-term PM2.5, µg/m3 | | | |
1-year | 50.56 (15.27) | 49.84 (20.47) | 10.93 to 86.50 |
3-year | 54.08 (16.92) | 53.09 (23.64) | 13.43 to 93.32 |
5-year | 59.79 (17.88) | 59.41 (23.71) | 15.45 to 100.35 |
Ambient temperaturea, ℃ | 20.81 (7.88) | 23.08 (7.61) | − 19.03 to 32.76 |
The results from the three models (Model 1, Model 2, and Model 3) all showed that allergic symptoms increased with higher short-term PM
2.5 exposure (Table
4). In adjusted Model 3, the ORs of allergic symptoms per 10 µg/m
3 increase in lag0 day average PM
2.5 concentration were 1.09 (95% CI 1.05, 1.12) for allergic nasal symptoms, 1.08 (95% CI 1.05, 1.11) for allergic eye symptoms, 1.06 (95% CI 1.02, 1.10) for worsening dyspnea caused by allergens, and 1.07 (95% CI 1.03, 1.11) for ≥ 2 allergic symptoms. The effect estimates were similar to those obtained using the PM
2.5 data of the lag0–7 day concentrations.
Table 4
Associations between short-term PM2.5 exposures and allergic symptoms in the PIFCOPD study population
Allergic nasal symptoms | | | | | | |
Lag0 day | 1.08 (1.06, 1.11) | < 0.001 | 1.09 (1.06, 1.12) | < 0.001 | 1.09 (1.05, 1.12) | < 0.001 |
Lag0–7 day | 1.1 (1.06, 1.14) | < 0.001 | 1.11 (1.06, 1.16) | < 0.001 | 1.11 (1.06, 1.16) | < 0.001 |
Allergic eye symptoms | | | | | | |
Lag0 day | 1.08 (1.05, 1.10) | < 0.001 | 1.08 (1.05, 1.11) | < 0.001 | 1.08 (1.05, 1.11) | < 0.001 |
Lag0–7 day | 1.13 (1.09, 1.16) | < 0.001 | 1.15 (1.11, 1.20) | < 0.001 | 1.16 (1.11, 1.20) | < 0.001 |
Worsening dyspnea caused by allergens | | | | | | |
Lag0 day | 1.07 (1.04, 1.10) | < 0.001 | 1.06 (1.02, 1.10) | 0.002 | 1.06 (1.02, 1.10) | 0.002 |
Lag0–7 day | 1.09 (1.04, 1.13) | < 0.001 | 1.06 (1.00, 1.11) | 0.049 | 1.06 (1.00, 1.12) | 0.06 |
≥ 2 allergic symptoms | | | | | | |
Lag0 day | 1.07 (1.04, 1.10) | < 0.001 | 1.07 (1.04, 1.10) | < 0.001 | 1.07 (1.03, 1.11) | < 0.001 |
Lag0–7 day | 1.09 (1.05, 1.13) | < 0.001 | 1.09 (1.03, 1.14) | 0.001 | 1.09 (1.03, 1.14) | 0.002 |
There were positive associations between long-term PM
2.5 concentrations (1-, 3- and 5-year average) and allergic symptoms (Table
5). Compared with the 3- and 5-year average PM
2.5 concentrations, the 1-year average PM
2.5 concentration had a stronger impact on allergic symptoms in the four long-term exposure models. In Model 3, each 10 µg/m
3 increase in the 1-year average PM
2.5 concentration was associated with higher odds of allergic nasal (1.23, 95% CI 1.14, 1.33) and eye symptoms (1.22, 95% CI 1.13, 1.31), worsening dyspnea caused by allergens (1.20, 95% CI 1.09, 1.32) and ≥ 2 allergic symptoms (1.21, 95% CI 1.11, 1.32); these effect estimates were similar to those after adjustment for short-term PM
2.5 concentration deviations in Model 4. Specifically, the OR of allergic eye symptoms for a 10 µg/m
3 increase in the 1-year average concentration was 1.22 (95% CI 1.13, 1.31) based on Model 3, compared with that of 1.22 (95% CI 1.14, 1.32) in Model 4 after adjustment for short-term PM
2.5 concentration deviations. Compared with long-term PM
2.5 concentration, the short-term PM
2.5 concentration deviations had a weaker impact on allergic symptoms in model 4. Each 10 µg/m
3 increase in the 1-year short-term PM
2.5 concentration deviation, the risk increased by 7% for allergic nasal symptoms (1.07, 95% CI 1.04, 1.10), 6% for allergic eye symptoms (1.06, 95% CI 1.03, 1.09), 5% for worsening dyspnea caused by allergens (1.05, 95% CI 1.01, 1.09), 6% for ≥ 2 allergic symptoms (1.06, 95% CI 1.02, 1.10), these effect estimates were similar to those in 3- and 5-year average PM
2.5 concentration logistic regression models (Additional file
1: Tables S3–S5).
Table 5
Associations between long-term PM2.5 exposures and allergic symptoms in the PIFCOPD study population
Allergic nasal symptoms | | | | | | | | |
1-Year | 1.25 (1.18, 1.33) | < 0.001 | 1.25 (1.17, 1.35) | < 0.001 | 1.23 (1.14, 1.33) | < 0.001 | 1.23 (1.14, 1.33) | < 0.001 |
3-Year | 1.14 (1.09, 1.20) | < 0.001 | 1.14 (1.07, 1.22) | < 0.001 | 1.13 (1.06, 1.21) | < 0.001 | 1.14 (1.07, 1.22) | < 0.001 |
5-Year | 1.14 (1.09, 1.20) | < 0.001 | 1.12 (1.05, 1.19) | < 0.001 | 1.11 (1.04, 1.19) | 0.001 | 1.13 (1.06, 1.20) | < 0.001 |
Allergic eye symptoms | | | | | | | | |
1-Year | 1.23 (1.16, 1.30) | < 0.001 | 1.24 (1.16, 1.33) | < 0.001 | 1.22 (1.13, 1.31) | < 0.001 | 1.22 (1.14, 1.32) | < 0.001 |
3-Year | 1.17 (1.11, 1.23) | < 0.001 | 1.2 (1.12, 1.27) | < 0.001 | 1.2 (1.12, 1.28) | < 0.001 | 1.21 (1.13, 1.29) | < 0.001 |
5-Year | 1.17 (1.12, 1.23) | < 0.001 | 1.17 (1.11, 1.24) | < 0.001 | 1.17 (1.10, 1.25) | < 0.001 | 1.19 (1.12, 1.26) | < 0.001 |
Worsening dyspnea caused by allergens | | | | | | | | |
1-Year | 1.26 (1.17, 1.37) | < 0.001 | 1.2 (1.10, 1.32) | < 0.001 | 1.2 (1.09, 1.32) | < 0.001 | 1.2 (1.09, 1.32) | < 0.001 |
3-Year | 1.17 (1.09, 1.25) | < 0.001 | 1.09 (1.01, 1.19) | 0.033 | 1.1 (1.01, 1.20) | 0.032 | 1.1 (1.01, 1.20) | 0.026 |
5-Year | 1.17 (1.10, 1.25) | < 0.001 | 1.08 (1.00, 1.17) | 0.057 | 1.08 (1.00, 1.17) | 0.062 | 1.09 (1.00, 1.18) | 0.041 |
≥ 2 allergic symptoms | | | | | | | | |
1-Year | 1.24 (1.16, 1.33) | < 0.001 | 1.22 (1.12, 1.33) | < 0.001 | 1.21 (1.11, 1.32) | < 0.001 | 1.21 (1.11, 1.32) | < 0.001 |
3-Year | 1.14 (1.08, 1.21) | < 0.001 | 1.11 (1.03, 1.20) | 0.006 | 1.11 (1.03, 1.21) | 0.006 | 1.12 (1.04, 1.21) | 0.004 |
5-Year | 1.15 (1.08, 1.21) | < 0.001 | 1.09 (1.02, 1.17) | 0.012 | 1.1 (1.02, 1.18) | 0.015 | 1.11 (1.03, 1.19) | 0.007 |
In addition to PM
2.5 exposure, the ORs for the other covariates were similar in short- and long-term PM
2.5 concentrations models (Additional file
1: Tables S1–S5). Specifically, multivariable logistic regression showed that ≥ 2 allergic symptoms was associated with older age (1.03, 95% CI 1.01, 1.04), higher education level (middle school (2.20, 95% CI 1.46, 3.41); high school (3.38, 95% CI 2.24, 5.27); college or higher (4.34, 95% CI 2.78, 6.96)), passive smoking (2.22, 95% CI 1.65, 2.97), household cooking (1.53, 95% CI 1.20, 1.96), occupational exposure (1.42, 95% CI 1.06, 1.88) and family history of asthma (2.95, 95% CI 2.02, 4.21) in the 1-year average PM
2.5 concentration logistic regression model 4 (Additional file
1: Table S3), which was similar to that of allergic nasal and eye symptoms, and worsening dyspnea caused by allergens.
Discussion
In the PIFCOPD study, short- and long-term ambient PM2.5 exposure levels were significantly associated with allergic symptoms, apart from other individual risk factors, including older age, higher education level, passive smoking, household cooking, occupational exposure, and family history of asthma. The associations between PM2.5 exposure and allergic symptoms, including allergic nasal and eye symptoms, worsening dyspnea caused by allergens, and ≥ 2 allergic symptoms, were stronger for the 1-, 3- and 5-year concentrations than the lag0 day, and lag0–7 day concentrations. Adjusting for short-term PM2.5 concentration deviations had little effect on the estimated associations with long-term exposures. Our study provides new evidence of the health effects of ambient PM2.5 exposure in relation to the prevalence of allergic symptoms in the middle-aged and elderly Chinese population.
China is experiencing a transition period from high to low air pollution levels. The PM
2.5 concentration trends in our study were consistent with previous reports [
27,
28]. As the focus of the APPCAP, a remarkable decline in the annual average PM
2.5 concentration was achieved [
28], with yearly concentrations decreasing by 45.2% (from 2013 to 2020) in our study. However, the short- and long-term exposure concentrations were considerably higher than the current Chinese grade I ambient air-quality standard [
13] or the WHO interim target 4 [
12] in this study. Winter is the most severe season for ambient PM
2.5 air pollution [
27]. A total of 8464 (83.5%) participants lived in northern, northeastern, and northwestern geographic regions, where coal burning is needed for heating in winter, emitting massive anthropogenic air pollutants.
Consistent with our study, previous studies showed that the prevalence of allergic symptoms and diseases increased when ambient air pollutants such as PM
2.5 were high [
21,
29,
30]. Experimental exposure to PM
2.5 could directly impair the barrier function of epithelial cells and induce oxidative stress and inflammatory responses [
31], thereby leading to allergic symptoms. Moreover, particulate matter contains many potential allergen carriers, such as pollutants, aerosols, pollens, bacteria, and fungi [
32,
33], and interacts with allergens to enhance their immunogenicity [
34,
35]. In addition, particulate matter also has an adjuvant effect on the production of immunoglobulin E (IgE) to common environmental allergens [
36]. The synergistic biological effects induced by allergens and ambient PM
2.5 could be one of the potential mechanisms illustrating the increasing risk of allergic diseases and symptoms at high PM
2.5 exposure levels.
Air pollution contributes to the onset and aggravation of allergic symptoms or diseases [
21,
35,
37]. Most previous studies were conducted on children and young adults [
7,
19,
30]. However, few studies have examined the associations between ambient PM
2.5 exposure and allergic nasal or eye diseases among middle-aged and elderly populations in large well-documented cohorts. Our study revealed that short- and long-term ambient PM
2.5 exposure is positively associated with allergic nasal and eye symptoms among middle-aged and elderly populations. Consistent with our results, one study from Fukuoka showed that an interquartile range (IQR) increase in PM
2.5 at lag0 day was associated with allergic nasal (1.08, 95% CI 1.03, 1.13) and eye (1.10, 95% CI 1.04, 1.16) symptoms among 2317 schoolchildren [
7]. Ambient PM
2.5 exposure might alter tear film stability, causing the film to break up and thin by disrupting the tear lipid layer, thus causing eye irritation and discomfort [
38]. According to a hospital-based population study in Japan, the ambient PM
2.5 concentration was associated with the number of outpatient visits for allergic conjunctivitis during the nonpollen season [
4]. The China, Children, Homes and Health (CCHH) project revealed that each 10 µg/m
3 increase in the annual PM
2.5 concentration was associated with a 20% increase in the prevalence of allergic rhinitis among preschool children in six Chinese cities [
30]. Short-term PM
2.5 exposure levels were correlated with nasal lavage fluid eosinophils and exudation mediators in children with asthma in Paris [
19]. PM
2.5 can increase inflammatory cytokines, induce pathological damage to the nasal mucosa and conjunctival epithelium, and worsen nasal and eye symptoms [
39,
40]. Improved air quality might reduce the inflammatory response and reduce allergic diseases and symptoms [
41].
Exposure to high levels of ambient PM
2.5 is a potential aggravating factor for allergic respiratory symptoms or diseases [
1,
42]. Wheezing and worsening dyspnea caused by allergens are common allergic respiratory symptoms [
43,
44]. The prevalence of allergic respiratory symptoms was 9–20.4% in a previous study [
42,
44,
45], compared to that of 1.2% for wheezing and 3.2% for worsening dyspnea caused by allergens in this study. The discrepancy might be attributed to the exclusion of individuals with chronic respiratory diseases such as COPD, emphysema, asthma, and other conditions from this study. Consistent with previous studies [
5,
42,
46], our study found a positive association between PM
2.5 exposure and worsening dyspnea caused by allergens. A combined analysis of cross-sectional data from Lifelines and UK Biobank cohorts, each 5 µg/m
3 increase in the PM
2.5 concentration was associated with an increase of 16% for wheezing and 61% for shortness of breath [
42]. PM
2.5 exposure, either alone or in combination with allergic sensitization, can induce oxidative stress, signal transduction interference, enzyme inhibition, epigenetic dysregulation, airway hyperresponsiveness and remodeling [
47]. Allergic respiratory symptoms can decrease with air quality improvement [
48] and smoking cessation [
49].
Our study found long-term PM2.5 exposure was more strongly associated with allergic symptoms than short-term PM2.5 exposure. The findings in our study could be attributable in part to differences between long- and short-term ambient PM2.5 exposure levels. Ambient PM2.5 exhibited the highest concentration in winter (90.08 µg/m3, 5-year average) and the lowest concentration in summer (44.99 µg/m3, 5-year average). Among the participants, 6325 (62%) were enrolled in the study in the summer, and only 373 (3.7%) were enrolled in the winter. The short-term (lag0 and lag0–7 day concentrations) ambient PM2.5 exposure levels were lower than the long-term (1-, 3- and 5-year average concentrations) in this study. Meanwhile, the questionnaire collected nonperiod-specified rather than recent allergic symptoms, which may be another important reason for the stronger associations for long-term than short-term PM2.5 exposure.
Short-term ambient PM
2.5 concentration deviations might not significantly affect on long-term PM
2.5 exposure models. Consistent with our results, a study of the LuftiBus cohort adjusting for short-term variations in nitrogen dioxide (NO
2) and PM
2.5 concentrations had little effect on the estimated associations between air pollution exposure and lung function parameters in long-term exposure models [
50]. Other studies adjusted for previous single-day or moving average concentrations instead of short-term deviations and revealed that the conclusions of associations between lung function parameters and long-term air pollution exposure were not altered [
51,
52]. Therefore, it might not be necessary to adjust for short-term air pollution concentrations, including short-term deviations, previous single-day or moving average concentrations, when estimating the effect of long-term ambient air pollution exposure.
Allergic diseases, which involve complex interactions of genetic, ethnic, environmental, and socioeconomic status or lifestyle risk factors, are primarily attributed to environmental factors such as indoor and outdoor air pollution, tobacco smoke exposure, and exposure to other pollutants [
21,
37,
44,
53]. In addition to ambient PM
2.5 exposure, we also found that allergic symptoms were positively associated with older age, higher education level, passive smoking, household cooking, occupational exposure and family history of asthma. Household cooking and tobacco smoke are major sources of indoor air pollution. Chinese cooking emits more PM
2.5 than Western cooking [
54]. For risk factors for allergic symptoms, previous studies have mainly focused on cooking fuel but not cooking itself [
55]. In this study, a total of 58% (5896/10142) of the participants cooked frequently at home, only 4.9% (495/10142) had biomass exposure, and 84% (4969/5896) had kitchen ventilation. Household cooking itself, not cooking fuel, is a major risk factor for allergic symptoms. Despite smoking bans in public places in China, 9.6% of the participants in our study were still exposed to environmental tobacco smoke at home or in the workplace. We found that passive smoking was associated with increased allergic symptoms. Consistent with our results, a study based on the Respiratory Health in Northern Europe (RHINE) cohort revealed that passive smoking increased the risk of wheezing (1.26, 95% CI 1.02, 1.57) [
56]. Family history of asthma is a well-known risk factor for asthma [
53]. This study found that a history of asthma in close relatives is also a risk factor for nasal symptoms, eye symptoms, worsening dyspnea caused by allergens, and ≥ 2 allergic symptoms.
Several limitations of this study should be addressed. First, given the study design, it is challenging to provide causal inferences about the associations between PM2.5 exposure and allergic symptoms. Further intervention and prospective studies are needed to verify the causality of the association in this study. Second, allergic symptoms were assessed by self-report questionnaires, making the study prone to recall bias. Third, as an issue commonly reflected in other studies, PM2.5 exposure concentrations were only estimated at the residence due to a need for more information about work addresses or time-activity patterns. This might result in misclassification. Limited by data availability, information about other ambient pollutants was not available, and we could not distinguish between associations due to PM2.5 specifically or other correlated pollutants.
Acknowledgements
We thank the participants of Predictive Value of Inflammatory Biomarkers and FEV1 for COPD. For continuous support, assistance, and cooperation, we thank Yunxia Wang, Zhu Tian, Meng Wu, Xiaoyu Ma, Chunbo Zhang, Meng Zhang, Peining Zhou, Jingya Sun, Yishan Nie (Peking University First Hospital), Yi Wang, Lina Zhang (Shichahai Community Health Service Center), Xiaofei Wang (Xitian'gezhuang Community Health Service Center), Wen Han, Tingting Li, Qin Peng (Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University), Hongmin Yu (The First Hospital of Qinhuangdao), Yanzhi Liu (Hongguang Livable Community Health Service Center), Jiao Zhang (Linheli Second Community Health Service Station), Suyan Liu (Tianjin Medical University General Hospital), Zhaojun Fan (Ciming Health Examination Management Group Tianjin Co., Ltd), Jing Chen (Heping District Xinxing Street Community Health Service Center), Xuhong Wang, Yonggang Li (General Hospital of Taiyuan Iron & Steel (Group) Co., LTD), Yanzhi Fan, Meina Zheng (Jinyuan Community Health Service Center), Faming Liu, Kangning Tang, Ling Zhu (The Second Hospital of Jilin University).