Association between Concentration of Air Pollutants and Prevalence of Inflammatory Sinonasal Diseases: A Nationwide Cross-sectional Study

Abstract

Background

Air pollution is a serious health concern and affects inflammatory sinonasal diseases such as allergic rhinitis (AR) and chronic rhinosinusitis (CRS). Clarifying the relationship between air pollutants and upper respiratory diseases could help the patients.

Objective

To evaluate the association between the concentration of air pollutants and the prevalence of AR and CRS among South Koreans.

Methods

In this cross-sectional study, nationwide data were reviewed for participants of the Korean National Health and Nutrition Examination Survey (KNHANES) 2008 to 2012. Participants were surveyed with health questionnaires, examined with endoscopies by otolaryngologists, and tested with serum immunoglobulin E levels. The concentrations of sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and particulate matter with aerodynamic diameters ≤10 µm (PM₁₀) were measured in 16 areas of South Korea. Air pollutant concentrations of geographic districts were matched to each participant's residence. Logistic regression analysis was performed.

Results

Among 27 863 eligible adults, 3359 and 1606 participants had AR or CRS, respectively. In multivariable logistic regression analysis for AR, PM₁₀ showed statistically significant results (odds ratio [OR] = 1.145, 95% confidence interval [CI] = 1.042–1.258). No air pollutants showed statistically significant differences in the prevalence of CRS. In AR, PM₁₀ (OR = 1.458, 95% CI = 1.201–1.770) was associated with endoscopic findings of watery rhinorrhea, whereas SO₂ (OR = 1.202, 95% CI = 1.100–1.313) was associated with pale mucosa.

Conclusion

The prevalence of AR was significantly associated with PM₁₀ concentration. In patients with AR, endoscopic findings of watery rhinorrhea were associated with PM₁₀. However, CRS was not associated with the air pollutant concentrations. Lower concentration of PM₁₀ might help managing the clinical symptoms in patients of AR.

Keywords

air pollution particulate matter allergic rhinitis sinusitis

Introduction

Air pollution is an important health concern, affecting approximately 1.6 million deaths annually.¹ It is becoming more serious as industrialization and urbanization accelerate worldwide. Harmful airborne substances like air pollutants come from natural sources, such as volcanic eruptions or forest fires, and anthropogenic sources such as emissions from traffic, fossil fuels, or home heating with coal or wood.² A variety of air pollutants, such as particulate matter (PM), sulfur dioxide (SO₂), or nitrogen dioxide (NO₂), are associated with the prevalence of cardiovascular, pulmonary, or infectious diseases.^3–6

Since air pollutants are airborne substances, epidemiological and experimental studies have tried to discover their association with respiratory diseases.^7,8 More specifically in the respiratory system, research has focused on inflammatory sinonasal diseases and their association with air pollutants.^9,10 That is because nasal cavities and paranasal sinuses are the first areas for an air pollutant to contact when inspired, and the air pollutants are known to cause excessive oxidative stress in the airways, leading to inflammatory response.^8,11

Considering the high prevalence and difficulties in efficiently controlling the inflammatory sinonasal diseases, finding possible aggravating factors, such as air pollutants, of the respiratory conditions would help patients avoid them and relieve the symptoms.^12–14 However, previous studies concerning air pollutants and inflammatory sinonasal diseases have been limited to analyzing subgroups of regional populations, and the results were inconsistent.^2,9,15,16

Because there is such a debate in this subject, in this population-based cross-sectional study we aimed to verify the association between the concentration of air pollutants and the prevalence of inflammatory sinonasal diseases such as allergic rhinitis (AR) and chronic rhinosinusitis (CRS), using the data of South Koreans on a nationwide scale. Additionally, we investigated whether immunoglobulin E (IgE) levels or rhinitis-related endoscopic findings of the nasal cavity were related to each air pollutant.

Methods

Study Population

The study data were obtained from the Korean National Health and Nutrition Examination Survey (KNHANES) conducted from 2008 to 2012. The KNHANES is a nationwide survey system that has assessed the current status and trends of health and nutrition of the South Korean population since 1998. The surveys are conducted by the Korea Centers for Disease Control and Prevention (KCDC). Every year, approximately 10 000 participants in 4000 households are selected from a panel based on the National Census Data, with the multistage clustered and stratified random sampling method, to represent the national population. The individuals are enrolled and surveyed all-year-round by trained interviewers, and thorough health interview, nutritional survey, and physical examination are performed. This cross-sectional nationwide survey provides information on the current national health status, individual health behavior, quality of life, socioeconomic status, and nutritional status. The results are updated every year on the KNHANES website and are used in administrative agencies and academic research.

The survey was conducted by trained members, including health interviewers, medical technicians, and physicians in a mobile examination center. In particular, from 2008 to 2012, 150 otorhinolaryngologists from 47 institutions participated in the KNHANES and conducted detailed medical interviews and endoscopic examinations. Therefore, in this study, we selected the KNHANES data from 2008 to 2012 to include validated information gathered by otorhinolaryngologists. Participants aged over 19 years were enrolled in the study.

Diagnosis of Inflammatory Sinonasal Diseases and Assessment of Related Conditions

In the KNHANES, specified questionnaires regarding inflammatory sinonasal diseases such as AR and CRS were included to be asked for each participant. They were investigated for the experience of being diagnosed with AR or CRS by a physician, and symptoms such as nasal obstruction, anterior or posterior nasal discharge, facial pain or pressure, and olfactory dysfunction, for three months or more were surveyed, in accordance with the European Position Paper on Rhinosinusitis and Nasal Polyps guidelines.^17,18

Moreover, endoscopic findings of the nasal cavity are considered as relevant findings in severity of AR, and pale mucosa and watery rhinorrhea are the major findings. Therefore, additional analyzes with the nasal endoscopic findings were performed. Nasal endoscopic examination was conducted by otorhinolaryngologists in the KNHANES from 2008 to 2012, and findings of pale mucosa or watery rhinorrhea were recorded if present.

Serum total IgE and specific IgE levels for three common indoor allergens (dog, cockroach, and house dust mite) were measured using ImmunoCAP in 10% of the participants in 2010. The cut-off values for the total IgE and specific IgE were defined as 100 kU/L and 0.35 kU/L, respectively. In the KNHANES, total IgE and specific IgE levels were measured in 2010 only.

Measurement of Air Pollutants

The Korean Ministry of Environment (MOE) has disclosed the air quality level of 16 areas near the World Cup Stadium on a real-time basis since April 2002. This was to meet the increasing public interest in air pollution and a clean environment so that the government and local authorities could use the data for regulating the environmental policies. South Korea is divided into 16 areas, including one capital metropolitan city (Seoul), six metropolitan cities (Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan), and nine provinces (Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju).

Using the existing infrastructure related to the nationwide air pollution monitoring network, the MOE collects air pollution data such as SO₂, O₃, NO₂, and PM₁₀ and provides information on air pollution levels by each region and quarter of every year. In this study, SO₂, NO₂, O₃, and PM₁₀ were included, and we calculated the annual average concentration of air pollutants in each region and matched it in conjunction with the residence information of the participants. This was in order to include the annual effect of air pollutants on each participant based on his or her residential area. As depicted in Figure 1, the mean concentrations of air pollutants in the 16 regions were used for the analysis. For additional information, average air pollutant concentrations in South Korea were lower than China or India but higher than the U.S.¹⁹

The method of measuring the air pollutants was the pulse ultraviolet fluorescence method for SO₂, the chemiluminescent method for NO₂, the ultraviolet photometric method for O₃, and the beta-ray absorption method for PM₁₀. The unit of measurement for air pollutants was μg/m³ for PM₁₀ and ppm for SO₂, NO₂, and O₃.

Covariates

Participant characteristics, including age, sex, residence, education level, household income, occupation, alcohol consumption, smoking status, body mass index (BMI), and asthma were assessed (Tables 1 and 2). Residence was classified into two categories, urban and rural, according to the administrative district of the participants. Education level was divided into four categories according to the level of graduation and household income was categorized into four groups according to the quartiles. Occupations were categorized into seven groups divided by the kind of labors. Alcohol consumption was classified into two groups according to the participants’ drinking experience; whether the participant has drunken alcohol at least once in their life or not. Never-smokers and ex-smokers were compared to current smokers. Participants with the BMI less than 18.5 were classified as low weight, BMI of 18.5 or more and less than 25 as normal weight, and BMI of 25 or more as obese. Diagnosis of asthma was evaluated and analyzed as a covariate.

Table 1.

Baseline Characteristics According to Diagnosis of AR.

	Study subjects		P-value
	Control (n = 24 421)	AR (n = 3359)	P-value
Age, years (mean ± SD)	51.1 ± 16.38	41.9 ± 14.3	<.0001
Sex, n (%)			<.0001
Male	10 551 (43.2)	1283 (38.2)
Female	13 870 (56.8)	2076 (61.8)
Residence, n (%)			<.0001
Urban	18 501 (75.76)	2868 (85.38)
Rural	5920 (24.24)	491 (14.62)
Education level, n (%)			<.0001
≤Elementary school	7169 (29.36)	342 (10.18)
Middle school	2819 (11.54)	260 (7.74)
High school	8074 (33.06)	1350 (40.19)
≥College or graduate school	6359 (26.04)	1407 (41.89)
Household income, n (%)			<.0001
Lower	5295 (21.68)	349 (10.39)
Lower middle	6188 (25.34)	835 (24.86)
Upper middle	6482 (26.54)	1085 (32.3)
Upper	6456 (26.44)	1090 (32.45)
Occupation, n (%)			<.0001
Managers, experts and related workers	2714 (11.11)	593 (17.65)
Office workers	1802 (7.38)	372 (11.07)
Service and Sales Personnel	3018 (12.36)	451 (13.43)
Agricultural, forestry and fishery skilled workers	2384 (9.76)	99 (2.95)
Technicians, equipment and machine operation and assembly workers	2289 (9.37)	280 (8.34)
Simple labor workers	2220 (9.09)	209 (6.22)
Unemployed (housewife, student, etc)	9994 (40.92)	1355 (40.34)
Drinking, n (%)			<.0001
Never	11 683 (47.84)	1480 (44.06)
Yes	12 738 (52.16)	1879 (55.94)
Smoking, n (%)			<.0001
None (Never-smoker or ex-smoker)	19 187 (78.57)	2770 (82.47)
Yes (Current smoker)	5234 (21.43)	589 (17.53)
Obesity, n (%)			<.0001
Low weight (BMI < 18.5)	1123 (4.6)	186 (5.54)
Normal (18.5 ≤ BMI < 25)	15 436 (63.21)	2237 (66.6)
Obese (25 ≤ BMI)	7862 (32.19)	936 (27.87)
Asthma, n (%)			<.0001
No	23 712 (97.1)	3166 (94.25)
Yes	709 (2.9)	193 (5.75)
Air pollutants (mean ± SD)
SO₂, ppm	5.2871 ± 0.895	5.3215 ± 0.906	.0370
NO₂, ppm	23.9544 ± 6.583	24.8505 ± 6.508	<.0001
O₃, ppm	23.7587 ± 3.394	23.3756 ± 3.359	<.0001
PM₁₀, μg/m³	50.1972 ± 5.033	50.727 ± 4.968	<.0001

Abbreviations: AR, allergic rhinitis; SD, standard deviation; ppm, parts per million; BMI, body mass index; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm. P-values with statistical significance are in bold.

Table 2.

Baseline Characteristics According to Diagnosis of CRS.

	Study subjects
Group	Control (n = 25 829)	CRS (n = 1606)	P-value
Age, years (mean ± SD)	50 ± 16.39	51.7 ± 16.52	<.0001
Sex, n (%)			<.0001
Male	10 911 (42.24)	803 (50)
Female	14 918 (57.76)	803 (50)
Residence, n (%)			.0455
Urban	19 876 (76.95)	1201 (74.78)
Rural	5953 (23.05)	405 (25.22)
Education level, n (%)			<.0001
≤Elementary school	6934 (26.85)	507 (31.57)
Middle school	2858 (11.07)	193 (12.02)
High school	8760 (33.92)	518 (32.25)
≥College or graduate school	7277 (28.17)	388 (24.16)
Household income, n (%)			.0086
Lower	5202 (20.14)	377 (23.47)
Lower middle	6521 (25.25)	403 (25.09)
Upper middle	7061 (27.34)	425 (26.46)
Upper	7045 (27.28)	401 (24.97)
Occupation, n (%)			.0072
Managers, experts and related workers	3087 (11.95)	175 (10.9)
Office workers	2015 (7.8)	121 (7.53)
Service and Sales Personnel	3235 (12.52)	194 (12.08)
Agricultural, forestry and fishery skilled workers	2328 (9.01)	148 (9.22)
Technicians, equipment and machine operation and assembly workers	2348 (9.09)	187 (11.64)
Simple labor workers	2243 (8.68)	161 (10.02)
Unemployed (housewife, student, etc)	10 573 (40.93)	620 (38.61)
Drinking, n (%)			.9279
Never	12 209 (47.27)	761 (47.38)
Yes	13 620 (52.73)	845 (52.62)
Smoking, n (%)			.0003
None (Never-smoker or ex-smoker)	20 479 (79.29)	1213 (75.53)
Yes (Current smoker)	5350 (20.71)	393 (24.47)
Obesity, n (%)			.0357
Low weight (BMI < 18.5)	1213 (4.7)	74 (4.61)
Normal (18.5 ≤ BMI < 25)	16 469 (63.76)	976 (60.77)
Obese (25 ≤ BMI)	8147 (31.54)	556 (34.62)
Asthma, n (%)			<.0001
No	25 054 (97)	1495 (93.09)
Yes	775 (3)	111 (6.91)
Air pollutants (mean ± SD)
SO₂, ppm	5.288 ± 0.896	5.3072 ± 0.907	.4055
NO₂, ppm	24.0189 ± 6.576	24.2153 ± 6.694	.2462
O₃, ppm	23.7297 ± 3.389	23.6726 ± 3.496	.5135
PM₁₀, μg/m³	50.2213 ± 5.029	50.4644 ± 4.935	.0599

Abbreviations: CRS, chronic rhinosinusitis; SD, standard deviation; ppm, parts per million; BMI, body mass index; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm. P-values with statistical significance are in bold.

Statistical Analyzes

Statistical analysis was performed using the Statistical Analysis System version 9.4 (SAS Institute, Inc., Cary, NC, USA). The baseline characteristics of the participants were analyzed to determine the differences with respect to “number of patients (percentages)” for categorical variables and “mean ± standard deviation (SD)” for continuous variables using chi-square tests and one-way analysis of variance, respectively.

Logistic regression analyzes were performed to determine the association between the variables, including air pollutants and inflammatory sinonasal diseases. The odds ratio (OR) and 95% confidence interval (CI) were determined, and the statistical significance level was set at P < .05. In multivariable logistic regression analysis for evaluation of AR, confounding variables which showed significant effects in univariable logistic regression analyzes (P < .25) were adjusted (age, sex, residence, education level, household income, occupation, drinking, smoking, obesity, and asthma) in order not to omit potentially relevant variables. Similarly, in multivariable logistic regression analysis for evaluation of CRS, age, sex, residence, education level, household income, occupation, smoking, obesity, and asthma were adjusted as confounding variables. The ORs were calculated by every 1 ppm increase for SO₂ and 10 ppm (or μg/m³) increase for NO₂, O₃, and PM₁₀ because the concentration variation range for SO₂ was in one-digit scale and the other pollutants in ten-digit scale.

Ethical Consideration

The KNHANES has been reviewed and approved by the Research Ethics Review Committee of the KCDC annually (IRB No. 2008-04EXP-01-C, 2009-01CON-03-2C, 2010-02CON-21-C, 2011-02CON-06-C, and 2012-01EXP-01-2C).

Results

Prevalence of Inflammatory Sinonasal Diseases

Among total of 45 811 participants from the KNHANES 2008–2012, those aged under 19 years, with missing values for the variables, or without nasal endoscopic findings were excluded (Figure 2). Finally, 27 863 participants were eligible for the study and were evaluated according to whether each participant was diagnosed with AR or CRS.

Among total participants, 3350 (approximately 12%) were participants diagnosed with AR. In AR group, 38.2% were men, 85.38% lived in urban areas, and 17.53% were current smokers (Table 1). Other baseline characteristics such as education level, household income, occupation, drinking, obesity, and asthma showed statistically significant differences between the AR and control groups.

In evaluation of CRS, 1606 participants (approximately 5.85% of total subjects) had history of diagnosis with CRS. Baseline characteristics including age, sex, residence, education level, household income, occupation, smoking, obesity, and asthma showed statistically significant differences between the CRS and control groups (Table 2).

Association between Inflammatory Sinonasal Diseases and Air Pollutants

In the univariable logistic regression analysis, SO₂, NO₂, O₃, and PM₁₀ showed significant differences between the AR and control groups (Table 3). In the analysis, confounding variables such as age, sex, residence, education level, household income, occupation, drinking, smoking, obesity, and asthma showed significant differences (P < .25). Thus, these variables were adjusted in the multivariable logistic regression analysis. The results showed that as the PM₁₀ concentration increased, the odds ratio of the prevalence of AR increased (1.145, 95% CI = 1.042–1.258, per 10 μg/m³ increase).

Table 3.

Univariable and Multivariable Logistic Regression Analysis of the Association Between the Concentration of Air Pollutants and the Prevalence of AR.

	AR
	Univariable		Multivariable
	OR (95% CI)	P-value	OR (95% CI)	P-value
SO₂	1.044 (1.003-1.086)	.0370	1.003 (0.956-1.051)	.9171
NO₂	1.229 (1.164-1.299)	<.0001	1.006 (0.898-1.129)	.9118
O₃	0.701 (0.625-0.785)	<.0001	1.092 (0.865-1.378)	.4580
PM₁₀	1.230 (1.146-1.321)	<.0001	1.145 (1.042-1.258)	.0048

In multivariable logistic regression analysis, confounding variables with P < .25 in univariable logistic regression analysis were adjusted (age, sex, residence, education level, household income, occupation, drinking, smoking, obesity, and asthma).

Abbreviations: AR, allergic rhinitis; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm; OR, odds ratio; CI, confidence interval. P-values with statistical significance are in bold.

In the univariable logistic regression analysis for evaluation of CRS, NO₂ and PM₁₀ showed significant effects on the prevalence of CRS (Table 4). However, after adjusting for variables which showed significant differences in univariable logistic regression analysis, including age, sex, residence, education level, household income, occupation, smoking, obesity, and asthma, NO₂ and PM₁₀ had no statistically significant impact on CRS prevalence.

Table 4.

Univariable and Multivariable Logistic Regression Analysis of the Association Between the Concentration of Air Pollutants and the Prevalence of CRS.

	CRS
	Univariable		Multivariable
	OR (95% CI)	P-value	OR (95% CI)	P-value
SO₂	1.024 (0.968-1.083)	.4055
NO₂	1.046 (0.969-1.129)	.2462	1.006 (0.898-1.129)	.1038
O₃	0.951 (0.819-1.105)	.5135
PM₁₀	1.100 (0.996-1.216)	.0600	1.145(1.042-1.258)	.2063

Abbreviations: CRS, chronic rhinosinusitis; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm; OR, odds ratio; CI, confidence interval. P-values with statistical significance are in bold.

Association between Endoscopic Findings and Air Pollutants

In the AR group of 3359 eligible participants, data of endoscopic findings such as pale mucosa and watery rhinorrhea were reviewed. Additional analyzes were performed to find if there is any correlation between the air pollutant concentrations and the endoscopic findings. For pale mucosa, SO₂, NO₂, and PM₁₀ concentrations showed significant effects in the univariable logistic regression analysis (Table 5). In the multivariable logistic regression analysis, after adjusting for confounding variables in addition to other air pollutants, the SO₂ concentration showed a statistically significant effect on endoscopic findings of pale mucosa. As the SO₂ concentration increased, the odds ratio for pale mucosa was 1.202 (95% CI = 1.100–1.313, per 1 ppm increase, P < .0001).

Table 5.

Univariable and Multivariable Logistic Regression Analysis of the Association Between Endoscopic Findings and Air Pollutants in the AR Group.

	Univariable		Multivariable
	OR (95% CI)	P-value	OR (95% CI)	P-value
Pale mucosa in AR
SO₂	1.207 (1.112-1.309)	<.0001	1.202 (1.100-1.313)	<.0001
NO₂	1.110 (0.991-1.244)	.0710	1.133 (0.981-1.308)	.0888
O₃	0.965 (0.774-1.203)	.7486
PM₁₀	1.138 (0.981-1.319)	.0882	1.148 (0.945-1.396)	.1655
Watery rhinorrhea in AR
SO₂	1.091 (1.006-1.183)	.0361	1.089 (0.996-1.189)	.0601
NO₂	1.038 (0.926-1.163)	.5245
O₃	1.095 (0.880-1.361)	.4156
PM₁₀	1.163 (1.003-1.349)	.0460	1.458 (1.201-1.770)	<.0001

In multivariable logistic regression analysis, confounding variables with P < .25 in univariable logistic regression analysis were adjusted (age, occupation, drinking, and obesity for pale mucosa, and age and sex for watery rhinorrhea).

Abbreviations: AR, allergic rhinitis; SO₂, sulfur dioxide; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm; NO₂, nitrogen dioxide; O₃, ozone; OR, odds ratio; CI, confidence interval. P-values with statistical significance are in bold.

Meanwhile, for the endoscopic findings of watery rhinorrhea, SO₂ and PM₁₀ concentrations seemed to have a significant effect in the univariable logistic regression analysis. In the multivariable logistic regression analysis, PM₁₀ concentrations had statistically significant effects on watery rhinorrhea. As PM₁₀ increased, the odds ratio for watery rhinorrhea was 1.458 (95% CI = 1.201-1.770, per 10 ppm increase, P < .0001).

Association between Serum IgE Levels and Air Pollutants

In the KNHANES 2010, total 699 participants in the AR group were assessed with serum IgE levels. Among them, there was considerable correlation between serum total IgE levels and SO₂, and between specific IgE levels and NO₂ in the univariable logistic regression analysis (Table 6). In the multivariable logistic regression analysis, confounding factors with P < .25 were adjusted. Consequently, there were no statistically significant differences in the odds ratio of both serum total IgE and specific IgE levels according to any of the air pollutant concentrations.

Table 6.

Logistic Regression Analysis of the Association Between Serum IgE Levels and Air Pollutants in the AR Group.

	Univariable		Multivariable
	OR (95% CI)	P-value	OR (95% CI)	P-value
Total IgE (+) in AR
SO₂	0.947 (0.876-1.022)	.1623	0.948 (0.878-1.024)	.1746
NO₂	1.052 (0.948-1.168)	.3398
O₃	0.978 (0. 798-1.198)	.8314
PM₁₀	1.002 (0.873-1.148)	.9827
Any specific IgE (+) in AR
SO₂	1.021 (0.945-1.103)	.5957
NO₂	1.097 (0.988-1.219)	.0834	1.080 (0.967-1.207)	.1723
O₃	0.932 (0.757-1.146)	.5037
PM₁₀	1.054 (0.918-1.209)	.4562

In multivariable logistic regression analysis, confounding variables with P < .25 in univariable logistic regression analysis were adjusted (age, sex, education level, occupation, drinking, smoking, and obesity for total IgE and age, sex, education level, household income, occupation, drinking, smoking, and obesity for any specific IgE).

Abbreviations: IgE, immunoglobulin E; AR, allergic rhinitis; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; PM₁₀, particulate matter with aerodynamic diameters ≤10 µm; OR, odds ratio; CI, confidence interval. P-values with statistical significance are in bold.

Discussion

In this study, among the 27 863 participants in the KNHANES from 2008 to 2012, the PM₁₀ concentration in the residential area of each participant was significantly associated with higher odds ratio for the prevalence of AR in the multivariable logistic regression analysis. Further, the concentration of PM₁₀ was positively associated with the endoscopic findings of watery rhinorrhea, which had been examined and investigated by the otolaryngologists. However, serum total and specific IgE levels to common aeroallergens were not associated with the concentration of air pollutants in participants with AR. There was no significant association between air pollutants and the prevalence of CRS either. To our knowledge, this is the first study to be conducted based on a nationwide-scale sample to investigate whether there is an association between the prevalence of inflammatory sinonasal diseases, such as AR and CRS, and the concentration of air pollutants.

There has been a discrepancy in the results regarding the association between air pollutants and AR. Studies have shown that the PM₁₀ concentration is associated with the diagnosis of AR.⁹ A nationwide study which evaluated the health insurance data reported that air pollutants such as NO₂, SO₂, and O₃ as well as PM₁₀ are related to the increased prevalence of seasonal AR.²⁰ Conversely, other studies showed the results that PM₁₀ was not associated with the AR prevalence¹⁵ or even reported that increased PM₁₀ was associated with a reduced risk of AR prevalence.¹⁶ However, the study populations in these studies were limited to participants living in a designated metropolitan city. These diversities in the scale of the study population might have led to the discrepancies in the results. In that sense, our result that AR is significantly associated with the PM₁₀ concentration has a strength in methodology compared to the previous studies by analyzing the big data representing the national population.

The study data were obtained from the KNHANES, a population-based, cross-sectional, and nationally representative survey of questionnaires investigated annually by the KCDC. The participants, approximately 10 000 individuals annually, were selected via a stratified, clustered, and systematic sampling design, which is a key strength of using this nationwide population. Detailed methods have been described in the literature.^21,22

To analyze the association between AR or CRS and air pollutants, the participants of the KNHANES were statistically matched according to their residence and then to the area's annual mean air pollutant concentration. Several studies covered respiratory diseases and their association with air pollution in nationally representative samples.^23,24 The ambient PM₁₀ in 2010 was matched to each individual's residence data, and lower respiratory tract diseases such as asthma and chronic obstructive pulmonary disease were covered, but not upper respiratory tract diseases. Other studies regarding the association between inflammatory sinonasal diseases and air pollutant concentrations covered the prevalence of AR in adults or children, symptoms of rhinitis, the number of outpatient clinic visits due to AR, and pathologic findings of CRS. The data included were limited to a specific region, rather than covering a whole-nation population^{9,10,15,25,26} or were limited to the spring and fall seasons.²⁰

AR is a common upper airway inflammatory disease, caused by IgE-mediated reaction after allergen exposure, along with the inflammation of the nasal mucosa by type 2 helper T (Th2) cells.²⁷ AR affects more than 400 million people worldwide, and the high prevalence leads to a significant economic cost, which is approximately $2-5 billion in the United States alone.^28–30 AR is a risk factor for acute exacerbation of asthma²⁸ and deteriorates individual social function in school, work, and sleep.^14,31 According to the treatment guidelines, AR is managed with patient education (avoidance of allergens), medical therapy, and immunotherapy³²; however, since there is no complete cure for AR, up to 62% of adults with AR are reported to exhibit residual symptoms when treated with orally consumed and intranasally sprayed medicine.¹³ Therefore, in addition to conservative medical therapies, the discovery of aggravating factors of AR would help patients avoid them and relieve the symptoms. Possible risk factors for AR include urbanization, western lifestyle, and a family history of allergy,³³ and the results of our study provide additional evidence supporting the view that air pollution is an important environmental factor of AR.

As mentioned above, the pathogenesis of AR is the inflammation of the nasal mucosa mediated by allergen-specific IgE. Thus, it has been speculated that a possible aggravating factor of AR, such as PM₁₀, could cause or exacerbate AR by modulating the immunologic response of allergen-specific IgE sensitization.³⁴ Although limited to pollen allergens, several experimental studies have demonstrated that inhalation of traffic-related pollutants upregulates respiratory allergic responses, possibly by binding to airborne pollens.^35–37 However, according to our findings, serum allergen-specific IgE levels were not associated with any air pollutant concentration, including PM₁₀. This is in accordance with a recent European birth cohorts study of 6163 children and adolescents; when followed from birth up to 16 years of age, there was no consistent association between air pollution exposure and IgE sensitization to common inhalant allergens.^38–40 This may be due to the effect of PM₁₀ to AR in terms of “nonspecific nasal hyperreactivity” rather than allergic sensitization; it has been proposed that PM₁₀ could exacerbate the inflammatory response of the nasal epithelial cells by increasing the oxidate distress in patients with AR when exposed to an aeroallergen.^11,41 The more specific mechanism of action by which air pollutants have effects on inflammatory sinonasal diseases including AR needs further investigation and research.

As the prevalence of AR was associated with the PM₁₀ concentration, we also found that among the rhinitis-related endoscopic findings of the participants with AR, watery rhinorrhea was associated with PM₁₀ concentration. A possible explanation for this result could be that PM₁₀ deteriorates the normal function of the submucosal glands and the nasal cavity mucosa. According to the recent experimental studies which investigated histopathological findings of the nasal mucosa in AR animal model, glandular swelling was observed in the nasal mucosa and the airway mucosal barrier function was disrupted after PM exposure in the animal model of AR.^8,42 Previous studies assessed the association between PM and respiratory symptoms,^43,44 but not any investigation regarding PM₁₀ and rhinitis symptoms in patients with AR has been reported. Further experimental and epidemiologic studies could determine whether such an association exists.

The limitation of this study is its cross-sectional design, making it difficult to interpret and clarify the causal relationship of our findings. Second, PM_2.5 was not included as an air pollutant variable in the analyzes. In recent decades, as the impact of air pollution on health has drawn attention to studies, the range of air pollutants with academic interest has diversified. Among the different air pollutants, PM_2.5, has been recognized as an important factor in inducing the acute exacerbation of asthma or aggravating cardiovascular disease.^45,46 However, since the equipment necessary for measuring PM_2.5 concentration was not completely functional in the MOE's air pollution monitoring sites until 2010, we could not obtain the PM_2.5 data for our evaluation.

Furthermore, the otorhinolaryngologists participated and performed endoscopic examinations in the KNHANES only from 2008 to 2012; due to administrative issues, they were unable to join the survey from 2013. Therefore, we enrolled participants from 2008 to 2012 to include the data collected consistently by the otorhinolaryngologists. Also, in analyzing the association between air pollutants and serum IgE levels in participants with AR, the association was estimated using 5% of standard error, 80% of power, 95% of confidence interval, and OR of at least 0.948 to be detected for logistic regression analysis. Power curves calculated by the G*Power 3.1.3 (Franz Paul, Universitat Kiel, Kiel, Germany) indicated that the minimum sample size needed at a range of power levels given was 3200. In our analysis of serum IgE levels, relatively small number of 699 participants were enrolled. This is due to the characteristics of the data; ImmunoCAP was measured only in 2010 by the K-CDC. Therefore, taking these limitations into account, in future research with similar topic to our study, PM_2.5 may also be included as an air pollutant and a larger population from recent years may be investigated.

Conclusions

In this study, multivariable logistic regression analyzes of the nationwide population of 27 863 participants of the KNHANES from 2008 to 2012 demonstrated a statistically significant association between the prevalence of AR and the concentration of PM₁₀. In participants with AR, endoscopic findings of watery rhinorrhea were associated with PM₁₀ concentrations. However, there was no statistically significant association between CRS and the air pollutant concentrations. These findings suggest that PM₁₀ might have clinically significant effect on aggravating the symptoms in patients with AR. Clinicians and administrators could closely monitor and refer to the PM concentration and facilitate better management with AR.

Footnotes

Acknowledgments

This research was supported by the Statistical Support Project for Writing Medical Papers using the KNHANES at the Korea University Anam Hospital. We thank Editage () for English language editing.

Author Contributions

Conceptualization, M.H., K.L., and T.H.K.; methodology, S.J.C., Y.J., K.L., and T.H.K.; validation, S.J.C., Y.J., and T.H.L.; investigation, M.H. and K.L.; resources, Y.J., K.L., S.H.L., and T.H.K.; data curation, M.H., S.J.C., Y.J., and K.L.; writing-original draft preparation, M.H., S.J.C., and T.H.K.; writing-review and editing, M.H. and T.H.K.; visualization, M.H., S.J.C. and T.H.K.; supervision, S.H.L. and T.H.K.; project administration, Y.J., T.H.L. and T.H.K.; funding acquisition, K.L. and T.H.K.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research was supported by the Basic Science Research Program, National Research Foundation of Korea, funded by the Ministry of Science and Technology and the Ministry of Science, ICT & Future Planning (2017R1A2B2003575, NRF-2020R1A2C1006398), the Ministry of Science and ICT, Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01819) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), the Korea Health Technology R&D Project (HI17C0387), Korea Health Industry Development Institute (KHIDI), and the Ministry of Health & Welfare. This research was also supported by a Korea University grant and a grant from Korea University Medical Center and Anam Hospital, Seoul, Republic of Korea.

Trial Registration

Not applicable, because this article does not contain any clinical trials.

Data Availability

The data of the KNHANES are open to the public; therefore, any researcher can obtain the raw data by requesting it on the website ().

Disclosures

The research was utilized in the thesis for master's degree of the author (S.J.C.).

ORCID iDs

Munsoo Han

Kijeong Lee

Sang Hag Lee

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