Seasonal effect of PM_2.5 exposure in patients with COPD: a multicentre panel study

Abstract

Background: Exposure to particulate matter <2.5 μm (PM_2.5) is linked to chronic obstructive pulmonary disease (COPD), but most studies lack individual PM_2.5 measurements. Seasonal variation and their impact on clinical outcomes remain understudied. Objective: This study investigated the impact of PM_2.5 concentrations on COPD-related clinical outcomes and their seasonal changes. Methods: A multicentre panel study enrolled 105 COPD patients (age range: 46–82) from July 2019 to August 2020. Their mean forced expiratory volume in 1 second after bronchodilation was 53.9%. Individual PM_2.5 levels were monitored continuously with indoor measurements at residences and outdoor data from the National Ambient Air Quality Monitoring Information System. Clinical parameters, including pulmonary function tests, symptom questionnaires (CAT and SGRQ-C), and impulse oscillometry (IOS), were assessed every three months over the course of one year. Statistical analysis was conducted using a linear mixed-effect model to account for repeated measurements and control for confounding variables, including age, sex, smoking status and socioeconomic status. Results: The mean indoor and outdoor PM_2.5 concentrations were 16.2 ± 8.4 μg m⁻³ and 17.2 ± 5.0 μg m⁻³, respectively. Winter had the highest PM_2.5 concentrations (indoor, 18.8 ± 11.7 μg m³; outdoor, 22.5 ± 5.0 μg m⁻³). Higher PM_2.5 concentrations significantly correlated with poorer St. George's Respiratory Questionnaire for COPD (SGRQ-C) scores and increased acute exacerbations, particularly in winter. Patients of lower socioeconomic status were more vulnerable. Increased PM_2.5 concentrations were also associated with amplified small airway resistance (R5–R20). Conclusions: PM_2.5 concentration changes are positively correlated with poorer SGRQ-C scores and increased acute exacerbations in COPD patients with significant seasonal variations, especially in winter.

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Environmental significance

This study underscores the critical impact of seasonal variations in PM_2.5 exposure on patients with chronic obstructive pulmonary disease (COPD) in South Korea, revealing significant correlations between increased PM_2.5 levels and worse clinical outcomes. By providing detailed indoor and outdoor PM_2.5 measurements and their associations with COPD severity, this research highlights the importance of air quality control measures. It further emphasizes the heightened vulnerability of lower socioeconomic groups, thereby advocating for targeted environmental and public health strategies to mitigate the adverse health impacts of air pollution on vulnerable populations. This comprehensive analysis provides essential insights to address seasonal and socioeconomic disparities in air pollution exposure.

Introduction

Chronic obstructive pulmonary disease (COPD) is a chronic progressive disease with significant morbidity and mortality.¹ Cigarette smoking is the most significant risk factor, but around 30% of patients with COPD are those who have never smoked.^2–4 Particulate matter (PM) is suggested as a principal risk factor for development of COPD.⁵ COPD exacerbations increase with PM exposure,^6–9 and air pollutants can also aggravate clinical symptoms, quality of life, and lung function in patients with COPD.^10,11 Indeed, PM enhances the risk of hospitalization, morbidity, mortality and exacerbation in COPD.¹²

Previous studies have focused on the association between inhalable PM with an aerodynamic diameter ≤10 μm and critical clinical parameters such as COPD hospitalizations or mortality.^13,14 Studies regarding PM with an aerodynamic diameter ≤2.5 μm (PM_2.5) and COPD are relatively rare and have described contradictory results. One study in Hong Kong demonstrated a significant positive association between PM_2.5 and hospitalization.¹⁵ However, a different study conducted in Rome revealed no association between the two parameters,¹⁶ whereas one from Birmingham in the United Kingdom presented opposing results.¹⁷ These contrasting studies were all population-based studies and did not evaluate precise ambient concentrations for each patient.

Korea, because it is located between China and the Pacific Ocean, has four distinct seasons influenced by seasonal winds. Due to these geographic characteristics, Korea experiences dynamic weather changes and different air pollution concentrations as each season occurs. Daily PM_2.5 levels also have a wide range, from 10 to 80 μg m⁻³, with an annual mean of 29 μg m⁻³. Such variations provide ideal conditions to study the seasonal effects of PM_2.5. This study investigated the seasonal associations between PM_2.5 concentrations and clinical parameters by measurement of PM_2.5 concentrations and assessment of its individual exposure in patients with COPD.

Materials and methods

Study design and participants

This was a prospective panel study conducted at four hospitals located across South Korea.

A total of 105 patients with COPD were enrolled, and detailed inclusion and exclusion criteria are available in the ESI.† We evaluated indoor, outdoor, and individual patients' ambient PM_2.5 concentrations and their association with clinical parameters for one year. This study was approved by the Institutional Review Board of each study site: Asan Medical Center (2019-0476), Gangneung Asan Hospital (2019-06-049), Ulsan University Hospital (2019-07-049), and Gachon University Gil Medical Center (GBIRB 2019-290, Fig. S1†). All participants received comprehensive information about the study and provided written informed consent. This study is also registered at https://ClinicalTrials.gov (registration no. NCT04020237). The detailed study protocols were described previously.^18,19 After enrolment, each patient completed a questionnaire. Afterwards, the clinical outcomes were evaluated every three months for one year.

Clinical data collection

The patient questionnaire included past medical history, current medication(s), residential environment, daily activities, protective behaviour against particulate matter, use of inhalers, and socioeconomic status.¹⁹ The clinical outcomes were assessed at the enrolment and repeated every three months. These outcomes included the COPD assessment test (CAT), St. George's Respiratory Questionnaire Specific for COPD (SGRQ-C), and pulmonary function test (PFT). Additionally, a section of patients, who were attending the Asan Medical Center, underwent serial impulse oscillometry system (IOS) testing. The CAT is used for comprehensive assessment of clinical symptoms among patients with COPD.¹ The SGRQ-C is a shortened version of the SGRQ, and it measures the health status of patients with COPD.²⁰ Of note, PFTs are required to diagnose airflow limitation, and forced expiratory volume in one second (FEV₁) is an index of the severity of airflow limitation.¹ The IOS is an alternative test for pulmonary function utilizing sound waves to measure respiratory mechanics. The resistance measured at the frequency of 5 Hz (R5) represents the total airway resistance, and the resistance assessed at 20 Hz (R20) corresponds to the resistance of large airways. The difference between the two (R5–R20) signifies the resistance of small airways. The reactance at 5 Hz (X5) indicates elastic recoil of the small airways. Among patients with COPD, IOS results are significantly correlated with spirometry parameters. R5–R20 increases as airflow limitation worsens, and a decline in X5 has been associated with COPD symptoms.^21,22

Measurement of particulate matter concentrations

Indoor and outdoor PM_2.5 concentrations were monitored for individual patients continuously throughout the study period, as previously described.^18,19 A measurement device using a light-scattering sensor (CP-16-A5, Aircok, Seoul, Korea) was installed in the living room of each participant's residence, as this is generally where participants spend most of their time while at home. In a typical Korean house, the living room is in the centre connecting all the other rooms with all doorway openings towards it.²³ The Internet of Things system was used to transfer real-time data to a separate server throughout the study period. To quantify the outdoor ambient PM_2.5 levels, data from the national database, AirKorea network (https://www.airkorea.or.kr), during the study period were employed. The measurements from the Air Quality Monitoring Station (AQMS) nearest to each patient's residential address were recorded. Additionally, PM_2.5 exposure concentrations were assessed four times at three month intervals, with a minivolume air sampler (Model KMS-4100, KEMIK Corp., Seongnam, Korea) and two dust spectrometers (11-D, Grimm Technologies, Douglasville, GA, USA, and AM520, TSI, Shoreview, MN, USA). Moreover, every three months, participants were instructed to carry a portable PM measuring device with a Global Positioning System (GPS; Airbeam2 from HabitatMap, Brooklyn, NY, USA) to measure the actual ambient 24 hour PM_2.5 concentrations. During that particular day, the participants noted their whereabouts in a time-activity diary, and the GPS receiver also traced the patients' location for validation.²⁴ PM_2.5 ≥ 35 μg m⁻³ is defined as “severe” and PM_2.5 ≥ 75 μg m⁻³ as “very severe” by the Ministry of Environment in Korea.²⁵ Consequently, the duration of PM_2.5 concentrations above 35 μg m⁻³ and 75 μg m⁻³ was determined for each participant.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plan of this work.

Statistical analysis

Data were analysed using Student's t-test, the χ² test, or analysis of variance, as appropriate. The Shapiro–Wilk test was used to test for normality. To evaluate seasonal changes in correlation between the PM_2.5 concentrations and clinical outcomes, Spearman's rank correlation coefficient was applied. Linear regression models were used to investigate the relationship between PM_2.5 concentrations exceeding the threshold of 35 μg m⁻³ or 75 μg m⁻³ and clinical outcomes. Repeatedly measured data (i.e., PM_2.5 concentrations and IOS) were analysed with a linear mixed-effect model. The model evaluating factors associated with PM_2.5 concentrations treated individual patients and investigation sites as random effects, with age, sex, smoking status, socioeconomic status and season as fixed effects. The model assessing the relationship between PM_2.5 and IOS measurements included participant-level random effects and fixed effect covariates of age, sex, smoking status and socioeconomic status. The specific PM_2.5 exposure type (indoor, outdoor or measurements from portable devices) used in each analysis depended on the focus of the analysis. A value of P < 0.05 was considered statistically significant (two-tailed). All statistical analyses were conducted using statistical software R (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria) and GraphPad Prism (version 9; GraphPad Software, San Diego, CA, USA).

Results

Baseline characteristics

Of the initial 126 participants, 105 patients completed the study (Fig. S2†). The mean age was 68.2 years, and 92.4% were male. Current or past smokers comprised 85.7% of patients. Utilization of inhalers was widespread across the cohort (99.0%). The most common treatment regimen was triple therapy (39.0%), which consisted of a long-acting β2-adrenergic agonist (LABA), a long-acting muscarinic antagonist (LAMA) and an inhaled corticosteroid (ICS). The mean post-bronchodilator FEV₁ was 53.9% of the predicted value. The baseline CAT and SGRQ-C scores were 16.7 and 37.5. More than half of the patients (55.2%) reported an MMRC scale of 2 or higher. A history of acute exacerbation was present in 39.0% of patients (Table 1).

Table 1 Baseline characteristics of the study subjects^a

Baseline characteristics	Total (n = 105)
a Data are presented as mean ± standard deviation or number (%). Abbreviations: FEV1, forced expiratory volume in one second; FVC, forced vital capacity; LABA, long-acting β2-adrenergic agonist; LAMA, long-acting muscarinic antagonist; ICS, inhaled corticosteroid; SGRQ-C St George's Respiratory Questionnaire Specific for COPD; CAT, COPD assessment test; MMRC, Modified Medical Research Council.
Age, years	68.2 ± 7.2
Male sex	97 (92.4)
Smoking status
Current smoker	23 (21.9)
Former smoker	67 (63.8)
Never smoker	14 (13.3)
Smoking, pack-year	34.7 ± 23.0
Lung function
Before bronchodilator
FEV1, litres	1.6 ± 0.5
FEV1, % of predicted value	52.7 ± 16.2
FVC, litres	3.3 ± 0.8
FVC, % of predicted value	80.1 ± 14.6
After bronchodilator
FEV1, litres	1.6 ± 0.6
FEV1, % of predicted value	53.9 ± 16.5
FVC, litres	3.4 ± 0.8
FVC, % of predicted value	80.6 ± 14.8
Inhaled medication
None	1 (1.0)
LABA or LAMA	14 (13.3)
LABA + LAMA	23 (21.9)
LABA + ICS or LAMA + ICS	25 (23.8)
LABA + LAMA + ICS	42 (40.0)
History of acute exacerbation	41 (39.0)
SGRQ-C total score	37.5 ± 21.8
CAT score	16.7 ± 8.2
MMRC dyspnoea scale
Grade 0	4 (3.8)
Grade 1	43 (41.0)
Grade 2	25 (23.8)
Grade 3	25 (23.8)
Grade 4	8 (7.6)

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PM_2.5 concentrations

During the study period, the mean indoor and outdoor PM_2.5 concentrations were 16.2 μg m⁻³ and 17.2 μg m⁻³, respectively. Among the four seasons, the concentrations were the highest in winter (18.8 μg m⁻³ [indoor] vs. 22.6 μg m⁻³ [outdoor], P < 0.001) and the lowest in fall (14.5 μg m⁻³ [indoor] vs. 13.7 μg m⁻³ [outdoor], P = 0.068, Fig. 1A). The indoor/outdoor (I/O) ratios were 0.918 in spring, 1.112 in summer, 1.059 in fall and 0.837 in winter (Table S1†). The mean duration of PM_2.5 concentrations above 35 μg m⁻³ was the longest in winter for both indoors (247.7 hours per month) and outdoors (382.8 hours per month, Fig. 1B). Winter displayed the longest duration of PM_2.5 levels above 75 μg m⁻³ (52.2 hours per month indoor and 15.5 hours per month outdoor, P = 0.009, Fig. 1C).

Fig. 1 PM_2.5 concentrations in each season of the year. (A) Mean concentration of PM_2.5. (B) Duration of PM_2.5 concentration over 35 μg m⁻¹. (C) Duration of PM_2.5 concentration over 75 μg m⁻³. Asterisk represents statistical significance of P < 0.05. Abbreviations: PM_2.5, particulate matter with aerodynamic size ≤2.5 μm.

In the linear mixed-effect model analysis, season was the only fixed effect significantly associated with variations in both indoor and outdoor PM_2.5 concentrations over a 90 day period, compared to other factors such as age, sex, smoking status and socioeconomic status. Relative to spring, concentrations of PM_2.5 were lower across all other seasons. The decline in PM_2.5 amounts was the largest during the fall season, during which the observed reduction in indoor PM_2.5 concentrations showed a slope of −3.739, with 95% confidence interval (CI) ranging from −4.887 to −2.594 (P < 0.001). A similar trend was observed for outdoor PM_2.5 concentrations, which decreased with a slope of −6.862 and a 95% CI of −7.719 to −6.003 (P < 0.001, Tables S2 and S3†).

COPD outcomes

The clinical outcomes, including CAT, SGRQ-C, PFT and acute exacerbations, did not exhibit any significant difference among seasons (ESI Fig. S3†). The correlations between PM_2.5 and clinical outcomes were assessed by Spearman's rank correlation test for each season. The significant relationship of PM_2.5 with acute exacerbations was present in winter. The correlation coefficient was the highest in outdoor mean concentration during the 90 days before evaluation (ρ = 0.267), followed by the outdoor duration above 75 μg m⁻³ within 35 days (ρ = 0.266) and outdoor mean concentration within 90 days (ρ = 0.262). PM_2.5 concentrations were also more highly correlated with SGRQ-C during winter. The outdoor concentration and average actual exposure within 90 days were all positively statistically correlated with the correlation coefficient ranging from 0.197 to 0.319. The CAT had higher correlations with indoor PM_2.5 concentrations during winter and spring. The FEV₁ had fewer correlations with PM_2.5 levels in comparison to other clinical outcomes (Fig. 2).

Fig. 2 Correlation between the clinical outcomes and concentrations of PM_2.5 during the days before the evaluation. Darker colours represent a larger Spearman's correlation coefficient, ρ. (A) Mean CAT scores. (B) FEV₁, % predicted. (C) SGRQ-C scores. (D) Number of acute exacerbations. Asterisk represents statistical significance of P < 0.05. Total exposure indicates correlations between participants' exposure and PM_2.5 concentration, as estimated by portable monitors worn for 24 hour periods once every three months. Abbreviations: PM_2.5, particulate matter with aerodynamic size ≤2.5 μm; CAT, COPD assessment test; FEV₁, forced expiratory volume in one second; SGRQ-C, Saint George's Respiratory Questionnaire for COPD.

The effects of PM_2.5 concentrations on SGRQ-C and acute exacerbations were further evaluated with logistic regression (Fig. 3). In winter, SGRQ-C and acute exacerbation increased as the duration of PM_2.5 concentrations above 35 μg m⁻³ or 75 μg m⁻³ increased. Significant relationships with SGRQ-C were mainly demonstrated with durations above 35 μg m⁻³. Meanwhile, acute exacerbation was correlated with both indoor and outdoor durations above 35 μg m⁻³ and 75 μg m⁻³.

Fig. 3 Changes in SGRQ-C and acute exacerbations according to PM_2.5 concentrations during winter. The black circles represent the correlation between the SGRQ-C score or number of acute exacerbations and PM_2.5 concentrations. The x-axis illustrates the linear regression coefficients. The y-axis displays the PM_2.5 concentrations of each day or sum of PM_2.5 concentrations during the days before clinical evaluation. (A) PM_2.5 and SGRQ-C. (B) PM_2.5 and the number of acute exacerbations in three months. Asterisk represents statistical significance of P < 0.05. Abbreviations: PM_2.5, particulate matter with aerodynamic size ≤2.5 μm; SGRQ-C, Saint George's Respiratory Questionnaire for COPD.

Relationship between PM_2.5 and IOS

The 44 patients enrolled in the Asan Medical Center underwent IOS at each visit to the clinic. Changes in R5–R20 were positively associated with patients' ambient PM_2.5 concentrations over previous 90 days. Statistically significant associations were found for PM_2.5 concentrations measured using portable devices (ΔR5–R20: 0.003 kPa L⁻¹ s⁻¹, P = 0.040), for the duration of outdoor PM_2.5 concentrations above 35 μg m⁻³ (ΔR5–R20: 0.00006 kPa L⁻¹ s⁻¹, P = 0.046), and for the duration of PM_2.5 concentrations above 75 μg m⁻³ (ΔR5–R20: 0.0002 kPa L⁻¹ s⁻¹, P = 0.015, Fig. 4).

Fig. 4 Changes in the measurement of impulse oscillometry according to PM_2.5 concentrations. (A) Changes in reactance at 5 Hz. (B) Changes in resistance at 5 Hz. (C) Changes in the difference between airway resistance at 5 Hz and 20 Hz. Asterisk represents statistical significance of P < 0.05. Abbreviations: PM_2.5, particulate matter with aerodynamic size ≤2.5 μm; X5, pulmonary reactance at 5 Hz; R5 airway resistance at 5 Hz; R20 airway resistance at 20 Hz.

Socioeconomic factors and PM_2.5

The associations among socioeconomic factors, monthly household income, economic status, education level, and PM_2.5 concentrations were analysed. When the patients were allocated into higher and lower socioeconomic status groups, in terms of monthly income (ESI Fig. S7†), current economic status (ESI Fig. S10†) and educational status (ESI Fig. S13†), the higher groups invariably had significantly lower indoor PM_2.5 concentrations than outdoor concentrations, whereas the lower socioeconomic groups did not. In addition, changes in SGRQ-C and acute exacerbations were more often associated with PM_2.5 exposure levels in groups belonging to the lower socioeconomic status categories of monthly household income, economic status, and education level (ESI Fig. S8, S9, S11, S12, S14 and S15†).

Discussion

Our study offers compelling evidence for the significant impact of PM_2.5 exposure on patients with COPD, underscoring a clear seasonal variability and its correlation with worsened clinical outcomes. By prospectively monitoring 105 COPD patients over a year, we have elucidated the nuanced interplay between ambient PM_2.5 levels and worsened COPD outcomes, particularly accentuated during winter and spring, the seasons with the highest PM_2.5 concentrations.

Consistent with the previous observations from Switzerland and the United States, the ambient PM_2.5 seasonal concentration was the highest in winter, followed by spring, summer, and fall in this study. A study from four regions of Switzerland reported that the PM_2.5 concentration was the highest in winter.²⁶ Likewise, in a report from the United States, winter was also the season that witnessed the highest PM_2.5 concentration.²⁷ These seasonal characteristics of winter can be explained by atmospheric stagnation²⁸ combined with greater biomass and fuel combustion.^28–30 Furthermore, indoor PM_2.5 concentrations were notably higher in winter and spring, with greater proportion of measurements exceeding 35 and 75 μg m⁻³. This trend may be partly due to seasonal lifestyle differences, such as reduced ventilation and less frequent window opening in colder months.³¹

Notably, our study advances the discourse by directly correlating these elevated levels with specific clinical outcomes in COPD correlated with seasonal variations. The significant aggravation of SGRQ-C was noted as an ambient PM_2.5 level increase in spring, fall, and winter. Indoor concentrations were more frequently correlated with SGRQ-C, compared to outdoor ones, suggesting the importance of indoor PM_2.5 control. Correlations between PM_2.5 and acute exacerbations were only present in winter. As winter had the highest concentration of PM_2.5, the correlation between PM_2.5 and the clinical outcome may be the most prominent. A Chinese group reviewed every day PM_2.5 concentrations and hospital records in China, and stronger short-term effects of PM_2.5 on cardiorespiratory hospital admission were found during the cold season.³² Similarly, a study from the United States investigated the effects of PM_2.5 on hospitalizations due to respiratory problems from 1999 to 2005 and established the effect to be the strongest during winter.³³

SGRQ-C and acute exacerbations were the clinical parameters associated with PM_2.5 concentrations. Recently, Hansel et al. (2021) explored the effects of air cleaners on patients with COPD. In the air cleaners' group, levels of PM_2.5, PM₁₀ and NO₂ were lower than those in the placebo group, with subsequent better outcomes in SGRQ and moderate exacerbations.³⁴ Similarly, a randomized controlled trial showed behavioural intervention to avoid PM exposure improved the quality of life, as assessed by SGRQ-C and COPD assessment tests.³⁵ These studies support our findings about the significant associations of PM_2.5 with quality of life and exacerbations.

We discovered a significant positive association between the changes in SGRQ-C and acute exacerbations and the duration of PM_2.5 ≥ 35 μg m⁻³ and 75 μg m⁻³. Our results suggest that an exposure to PM_2.5 above a certain concentration can provoke worsening of clinical outcomes in COPD. Furthermore, both short-term and relatively long-term effects, up to 90 days, were statistically significant. Although long-term exposure to high levels of PM_2.5 has been associated with the development and progression of COPD,^36,37 most studies investigating the relationship between clinical outcomes of COPD and exposure to PM_2.5 have focused on short-term exposures.^32,33 This study displays a possibility that the PM_2.5 concentration can have a gradual impact on COPD for at least up to three months.

The IOS results in this study provided clues on the pathophysiology of PM_2.5 effects on COPD. We found significant positive relationships between changes in R5–R20 and PM_2.5 concentrations, in the absence of associations between FEV₁ and PM_2.5. IOS is more sensitive than FEV₁ in detection of small airway changes,^9,38 and our results imply that PM_2.5 mainly have an impact on small airway diseases. Many previous studies supported the underlying mechanism of this clinical finding. Smaller particles can reach small airways more deeply, and they can remain without exhalation.³⁹ When human bronchial epithelial cells are exposed to PM_2.5, genes associated with the inflammatory response and extracellular IL-6 are up-regulated,⁴⁰ which can be related with the development of asthma and COPD.⁴¹ In a study that exposed mice to PM_2.5 for 48 weeks demonstrated an increase in IL-6 in the bronchoalveolar lavage fluid and airway wall remodelling on microscopic examination.⁴² These findings support the potential impact of PM_2.5 on small airway diseases.

In our study, relatively better management of indoor PM_2.5 exposure was noted in higher socioeconomic groups. Participants in the lower socioeconomic groups did not to reduce the indoor PM_2.5 concentrations from outdoor concentrations. The PM_2.5 levels were also more frequently associated with SGRQ-C or acute exacerbations in the lower socioeconomic groups. Socioeconomic disparity in environment is an important public issue to be addressed. A study based on area-level census data in Australia stated that the population in areas with greater socioeconomic disadvantages was more heavily exposed to PM_2.5.⁴³ Comparably, an analysis on the annual mean concentration of PM_2.5 of one million residential postcodes in England reported that PM_2.5 concentrations were higher in areas of socioeconomic deprivation.⁴⁴

Our study has several limitations. First, the number of participants was small. Meanwhile, we monitored PM_2.5 data continuously with the Internet of Things system and collected detailed clinical parameters every three months, which included about 400 measurement points in the analysis. Based on this individualized patient monitoring, we probed into the diverse aspects of the relationship between PM_2.5 and COPD. Second, the study period encapsulated the COVID-19 pandemic, which kept patients more at home, and their use of protective masks when outdoors would have decreased direct inhalation or contact with PM_2.5. These behavioural changes may have affected the clinical outcomes. Third, the attitude and behaviour towards PM_2.5 may be diverse depending on the country and culture, and the relationship and impact of PM_2.5 may have some detailed differences according to the region. Despite this, the results herein agree with previous reports of other countries. Our earlier report on the impact of lifestyle on indoor PM_2.5 concentrations also presented consistent findings with previous studies from Western countries.⁴⁵ Additionally, we could not directly control for temperature, a potential confounder for clinical outcomes, due to lack of data. However, by analysing seasonal differences in PM_2.5 effects, we indirectly account for temperature-related effects, as temperature differs markedly across seasons.³¹ Lastly, outdoor PM_2.5 concentrations were monitored through the nearest AQMS. Although the median distance between the participants' homes and the nearest AQMS was 2.2 km, we observed a moderate correlation between outdoor concentrations measured at several homes using the same sensor-based light scattering device (CP-16-A5; Aircok Inc., Seoul, Korea) and AQMS values (e.g., R²: 68%; RMSE: 5.6 μg m⁻³), and local variations in air pollutant concentrations may still exist.¹⁹

Conclusions

In conclusion, our study not only confirms the significant impact of seasonal PM_2.5 exposure on COPD clinical outcomes but also highlights the exacerbating role of socioeconomic disparities. These insights call for an integrated approach to air quality management, encompassing environmental, public health, and socioeconomic strategies to mitigate the adverse effects of PM_2.5 on vulnerable populations. Future research should explore the long-term clinical impacts of sustained PM_2.5 reduction and the development of personalized COPD management plans that consider environmental exposures.

Data availability

The data analysis scripts of this article are available in the interactive notebook [Google Colab] at [https://colab.research.google.com/drive/1oB5ibkO93-rQ4FU481lNRHcaSjTiQG0w?usp=sharing].

Author contributions

Jin-Young Huh: data curation, formal analysis, methodology, software, visualization, writing – original draft, writing – review & editing; Hajeong: formal analysis, investigation; Seung Won Ra: resources, writing – review & editing; Sung-Yoon Kang: resources; Bock Hyun Jung: resources; Mihye Kim: resources; Sang Min Lee: resources; Sang Pyo Lee: resources; Dirga Kumar Lamichhane: data curation, formal analysis; Young-Jun Park: funding acquisition; Seon-Jin Lee: funding acquisition; Jae Seung Lee: resources; Yeon-Mok Oh: resources, supervision; Hwan-Cheol Kim: conceptualization, data curation, formal analysis, investigation, methodology, project administration; Sei Won Lee: conceptualization, funding acquisition, investigation, resources, project administration, validation, visualization, writing – original draft, writing – review & editing. All authors read and approved the final manuscript.

Conflicts of interest

The authors declare that they have no competing or financial interests.

Acknowledgements

This study was supported by grants from the Research of Korea Centers for Disease Control and Prevention [No. 2019ER671100 and 01, and 2021ER120900 and 01] grants from the Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program (KGM5322422), the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2023R1A2C2006688 and RS-2023-00222687, SWL), the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2022M3A9G8017220).

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4em00376d

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