Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jun 8.
Published in final edited form as: Hyg Environ Health Adv. 2023 Mar 31;6:100052. doi: 10.1016/j.heha.2023.100052

Personal and community-level exposure to air pollution and daily changes in respiratory symptoms and oxygen saturation among adults with COPD

Amro Aglan a, Andrew J Synn a, Lina Nurhussien a, Kelly Chen a, Charlotte Scheerens a,b, Petros Koutrakis c, Brent Coull c, Mary B Rice a,*
PMCID: PMC10249721  NIHMSID: NIHMS1902532  PMID: 37293389

Abstract

Background:

Air pollution exposure is associated with hospital admissions for Chronic Obstructive Pulmonary Disease (COPD). Few studies have investigated whether daily personal exposure to air pollutants affects respiratory symptoms and oxygenation among COPD patients.

Methodology:

We followed 30 former smokers with COPD for up to 4 non-consecutive 30-day periods in different seasons. Participants recorded worsening of respiratory symptoms (sub-categorized as breathing or bronchitis symptoms) by daily questionnaire, and oxygen saturation by pulse oximeter. Personal and community-level exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) were measured by portable air quality monitors and stationary monitors in the Boston area. We used generalized and multi-level linear mixed-effects models to estimate associations of the 24-hour average of each pollutant in the previous day with changes in respiratory symptoms and oxygen saturation.

Results:

Higher community-level exposure to air pollutants was associated with worsening respiratory symptoms. An interquartile range (IQR) higher community-level O3 was associated with a 1.35 (95%CI: 1.07–1.70) higher odds of worsening respiratory symptoms. The corresponding ORs for community-level PM2.5 and NO2 were 1.18 (95%CI: 1.02–1.37) and 1.06 (95%CI: 0.90–1.25), respectively. Community-level NO2 was associated with worsening bronchitis symptoms (OR=1.25, 95%CI: 1.00–1.56), but not breathing symptoms. Personal PM2.5 exposure was associated with lower odds of worsening respiratory symptoms (OR=0.91; 95%CI: 0.81–1.01). Personal exposure to NO2 was associated with 0.11% lower oxygen saturation (95%CI: −0.22, 0.00) per IQR.

Conclusions:

In this COPD population, there was a pattern of worsening respiratory symptoms associated with community-level exposure to O3 and PM2.5, and worsening oxygenation associated with personal exposure to NO2.

Keywords: Air pollution, Particulate matter, Nitrogen dioxide, Ozone, Chronic obstructive pulmonary disease, Symptoms

1. Introduction

Exposure to air pollution has been identified as one of the greatest public health concerns worldwide (Kelly and Fussell, 2015). A broad range of adverse respiratory health effects have been associated with exposure to ambient air pollution, including lower pulmonary function, higher risk of respiratory infections, and risk of exacerbations of chronic lung diseases, such as asthma and chronic obstructive pulmonary disease (COPD) (Thurston et al., 2017). COPD is characterized by an abnormal inflammatory response to noxious particles and gasses (Gómez and Rodriguez-Roisin, 2002), and adults with COPD appear to be at increased risk of experiencing adverse respiratory effects of ambient air pollution exposure, even at relatively low levels. (Duan et al., 2020)

While several studies have found that outdoor levels of particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) are associated with higher risk of COPD hospitalization (Gan et al., 2013; Moore et al., 2016; Song et al., 2014), few studies have evaluated if day-to-day differences in exposure to these pollutants affect daily symptoms or oxygenation among non-hospitalized COPD patients living at home. Most epidemiological studies investigating the effects of air pollution on COPD patients have relied only on outdoor pollution measurements from fixed central monitoring locations, which may differ from the personal exposure of the individual to pollution from both outdoor and indoor pollution sources (Butland et al., 2019a). Capturing personal exposure may be especially important in COPD patients, who spend more time at home compared to the general population (Leech and Smith-Doiron, 2006). Nonetheless, outdoor monitors provide relevant exposure information even for those who remain home, because pollution from outdoor sources such as traffic and industry enter the home and pollute the indoor environment. (Huang et al., 2018)

To address these knowledge gaps, we conducted the Study of Pollution and COPD Exacerbation (SPACE) to evaluate whether personal and community-level pollutant exposures are associated with daily worsening of respiratory symptoms and oxygen saturation among patients with COPD living in the Boston area, where pollution levels are generally in compliance with national ambient air quality standards. We hypothesized that both personal and community-level exposure to PM2.5, NO2, and O3 would be associated with worsened daily respiratory symptoms and oxygen saturation among COPD patients.

2. Material and methods

2.1. Study population

The study population consists of 30 former smokers with COPD who were recruited as part of the Study of Pollution and COPD Exacerbation (SPACE) at Beth Israel Deaconess Medical Center in Boston, Massachusetts (MA), United States of America. To be eligible, study participants were required to be former smokers with a ≥ 10 pack-year smoking history and a clinical diagnosis of COPD with at least moderate GOLD Stage II airflow obstruction, defined as forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio of <70% and FEV1 <80% predicted, using NHANES III prediction formula (HANKINSON et al., 1999). In order for the community-level pollution data to be applicable, all participants were required to have a home address within 50 km of the Harvard air pollution supersite in Boston. Participants with a history of lung cancer, interstitial lung disease, or bronchiectasis were ineligible to take part in the study.

2.2. Data collection

Participants entered the study between 02/24/2017 and 01/17/2019. At study entry, demographic information, height, weight, past medical history, medication history, baseline lung function, and oxygen saturation were obtained. In addition, participants were instructed on how to use a personal air quality monitor (PAM), portable oximeter and were shown how to complete a daily electronic questionnaire (full questionnaire available in online supplement, Appendix A).

Study participants were then observed for up to four non-consecutive months in different seasons in a twelve-month period. During these observation periods, participants recorded daily changes in respiratory symptoms and oxygen saturation using a daily questionnaire, accessed electronically through Research Electronic Data Capture (REDCap) (Harris et al., 2009). This questionnaire was developed to identify self-reported changes in respiratory symptoms (Aaron et al., 2012; Seemungal et al., 1998; Wilkinson et al., 2004). The questionnaire asks about the occurrence of any of the following within the previous 24 h compared to usual state of health: increased breathlessness; increased sputum color; increased sputum amount; cold-like symptoms (such as a runny or blocked nose); increased wheeze or chest tightness; sore throat; increased cough; fever. Furthermore, as in previous work (Scheerens et al., 2022), we categorized respiratory symptoms into breathing symptoms (breathlessness, chest tightness, and wheeze)(“Global Initiative for Chronic Obstructive Lung Disease,” 2019) and bronchitis symptoms (cough, sputum color, and sputum amount) (Miravitlles et al., 2012). In addition, participants measured their daily oxygen saturation (SpO2) using Nonin GO2 LED Achieve 9571 Finger Pulse Oximeter (Plymouth, Minnesota). The Nonin GO2 device uses the same PureSAT® SoO2 technology as the medical grade oximeters and has a declared oxygen saturation accuracy range of 70%–100% SpO2 + 2 digits. As an estimate of physical activity, we measured personal 24-hour step count using the portable Fitbit® Charge 2 worn by participants each day for all hours, except during bathing (during which the participants were instructed to charge the device). We also recorded inhaler use during the participants’ entry visit into the study, which included inhaled corticosteroids, short-acting beta agonists, long-acting beta agonists, short-acting muscarinic antagonist, long-acting muscarinic antagonist.

2.3. Exposure assessment

We measured personal pollutant exposures using PAMs, and community-level pollutant exposures using an average of Boston-area stationary monitors. For community-level PM2.5 and NO2, we averaged daily concentrations from state-owned monitors in Boston. For O3, we used the state monitor in the Roxbury neighborhood of Boston, which measures O3, as previously described (Nurhussien et al., 2021). The PAMs, developed by Atmospheric Sensors Ltd (model 520) in collaboration with investigators in the U.K., were equipped with sensors for temperature and relative humidity, Alphasense® electrochemical cells for NO2 and O3, and an Alphasense® optical particle counter for fine PM2.5 (Liu et al., 2019). All personal 24-hour exposure measures for PM2.5, NO2, O3, temperature, and relative humidity collected during our study were calibrated against reference monitors prior to data analysis, as previously described (Nurhussien et al., 2021).

2.4. Statistical analyses

We calculated mean 24-hour exposure to pollutants from the PAMs (personal exposure) and outdoor state-owned stationary monitors in the Boston area (community-level exposure). We assessed associations between 24-hour exposure to personal and community-level pollutants and odds of worsening of any respiratory symptoms, and also examined associations with odds of worsening breathing symptoms (defined as breathlessness, chest tightness, wheeze), and bronchitis symptoms (defined as cough, sputum color, and sputum amount) separately. We constructed generalized logistic mixed-effects models using the PROC GLIMMIX function in SAS with a participant-level random effect to account for intra-individual correlations between repeated measurements among the same person, and a participant-observation month-specific random effect to allow daily observations from a single observation month within the same person to be more highly correlated than observations from a different observation month on the same person. To examine associations between 24-hour exposures and oxygen saturation (a continuous outcome), we constructed similar multi-level linear mixed-effects (PROC MIXED) models with a participant-specific random effect, a participant-observation month specific random effect, and normally distributed errors assumed to follow an autoregressive [AR(1)] process to model the serial correlation among the daily time series within the same person’s observation month, as previously described (Nurhussien et al., 2021). All models were adjusted for previous-day temperature, humidity, season, age, sex, race, height, weight, total pack-year smoking history, and education. Season was categorized as winter, spring, summer, and fall based on the calendar start dates of each observation month. Moreover, we conducted a secondary analysis to examine if physical activity and inhaler use could affect associations of pollutant exposures with symptoms and oxygenation by additionally adjusting for 24-hour step count and inhaler use in our models. All statistical analyses were performed using SAS 9.4 (Cary, North Carolina, United States of America).

3. Results

3.1. Characteristics of study participants

Baseline measures of study participants were collected at the time of their respective study entry visit and are summarized in Table 1. We collected 3619 daily observations with exposure and outcome data among 30 participants with a mean age of 71.1 ± 8.4 years and were 80% white and 20% black. Household income and education status were generally low. A large majority of participants had a household income of less than $50,000, and fewer than one third of participants had attained a bachelor’s degree of education or higher. Participants were former heavy smokers with more than 50 pack-years of smoking on average. As we have previously noted, the overall frequency of worse-than-baseline respiratory symptoms was low (Sun et al., 2020). Out of the 3619 daily observations, participants reported any worsening of baseline respiratory symptoms on 490 days (13.5%), including 227 days (6.3%) with worsening breathing symptoms and 330 days (9.1%) with worsening bronchitis symptoms.

Table 1.

Baseline characteristics of participants (N = 30).

Mean (SD) or N (%)
Demographics
 Age, years 71.1 (8.4)
 Height, cm 165.1 (10.2)
 Weight, kg 85.3 (17.7)
 Sex
  Male 14 (46.7)
  Female 16 (53.3)
 Race
  White 24 (80.0)
  Black or African American 6 (20.0)
 Income
  <$25,000 10 (33.3)
  $25,000-$49,999 10 (33.3)
  > $50,000 9 (30.0)
  Other/Missing 1 (3.3)
 Education
  Up to grades 12 or GED 8 (26.7)
  Some college – Associate’s degree 13 (43.3)
  Bachelor’s degree and above 9 (30.0)
 Total Pack-Year Smoking History 54.4 (30.7)
Baseline Clinical Measures
 FEV1% predicted 54.3 (14.5)
 FVC% predicted 79.9 (15.7)
 FEV1/FVC 51.1 (11.5)
Number of observations
 Total number of daily observations 3619 (100)
 Days with worsening of any respiratory symptom 490 (13.5)
 Days with worsening of breathing symptoms 227 (6.3)
 Days with worsening of bronchitis symptoms 330 (9.1)
Open in a new tab

Data are presented as mean (SD, standard deviation) or as n (%, column percentage), as indicated. FEV1: forced expiratory volume in 1 second; FVC: forced vital capacity.

3.2. Concentration levels of air pollutants

Table 2 displays the distribution of personal and community-level exposure to air pollutants. The median previous-day 24-hour personal exposure to PM2.5 by personal (8.8 μg/m3) and community-level monitor (6.0 μg/m3) were within the EPA 24-hour PM2.5 standard of 35 μg/m3 and the annual standard of 12 μg/m3. Median previous-day 24-hour personal and community-level NO2 concentrations were similar (approximately 7 ppb), while median personal exposure to O3 was very low (9.6 ppb) compared to the community-level O3 concentration (26.2 ppb), which was well within the 8-hour ambient air quality standard for O3 (70 ppb) (Cox et al., 2015; US EPA, 2013). As previously published by our group (Nurhussien et al., 2021), community-level exposures of these pollutants were positively associated with personal exposures measured by the PAMs, however, the magnitude of these associations was modest. For example, for each 10 μg/m3 increase in community-level PM2.5, there was 2 (0.9–3.2) μg/m3 increase in personal exposure to PM2.5.

Table 2.

Previous-day community-level and personal exposure to air pollutants.

Exposure N Median IQR
PM2.5, μg/m3
  Community-Level 3619 6.0 3.6
  Personal 2943 8.8 3.8
NO2, ppb
  Community-Level 3619 7.2 4.7
  Personal 2943 6.7 5.3
O3, ppb
  Community-Level 3601 26.2 11.8
  Personal 2943 9.6 5.2
Open in a new tab

Abbreviations: PM2.5, fine particulate matter less than 2.5 μm in diameter; NO2, nitrogen dioxide; O3, ozone; IQR, interquartile range.

3.3. Pollution exposure and respiratory symptoms

The associations between previous-day exposure to air pollutants and worsening respiratory symptoms are shown in Fig. 1. In fully adjusted models, we observed a pattern of positive (adverse) associations between higher community-level exposure to air pollutants and worsening of respiratory symptoms. Every IQR higher community-level exposure to O3 was associated with a 1.35 (95%CI: 1.07–1.70) higher odds of worsening of any respiratory symptom. The corresponding odds ratios (ORs) for community-level PM2.5 and NO2 were 1.18 (95% CI: 1.02–1.37) and 1.06 (95% CI: 0.90–1.25), respectively. The association for community-level O3 was greater for breathing symptoms (OR=1.52, 95% CI: 1.14–2.02), than bronchitis symptoms (OR=1.11, 95% CI: 0.81–1.52), while community-level NO2 was associated with worsening bronchitis symptoms (OR=1.25, 95% CI: 1.00–1.56) but not breathing symptoms[OR=1.04, 95% CI: 0.86–1.26]. Community-level PM2.5 had similar associations with both breathing and bronchitis symptoms with ORs of 1.17 (95% CI: 0.98–1.40) and 1.23 (95% CI: 1.00–1.51), respectively. Every IQR higher personal exposure to PM2.5 was associated with lower odds of worsening of any respiratory symptoms (OR=0.91; 95% CI: 0.81–1.01) including breathing symptoms (OR=0.91; 95%: 0.81–1.03). Personal exposure to NO2 and O3 were not associated with worsening respiratory symptoms (Fig. 1). After adjusting for physical activity and inhaler use in our secondary analyses, we found no significant changes in our primary associations. For example, the association between personal PM2.5 and worsening of any respiratory symptoms was unchanged [OR 0.91 (CI 0.81, 1.01)], as was the association between community-level O3 and worsening of any respiratory symptoms [OR 1.35 (CI 1.07, 1.70) vs. 1.33 (CI 1.04, 1.69)] in models adjusting for prior-day step count and inhaler use. Full results available in supplementary material (Appendix B).

Fig. 1.

Fig. 1.

Open in a new tab

Associations of personal and community-level exposure to air pollutants with odds of worsening respiratory symptoms

Legend: Mixed-model analyses showing odds of worsening respiratory symptoms per interquartile range (IQR) difference in previous-day pollutant exposure. All models are adjusted for previous-day temperature, humidity, season, age, sex, race, height, weight, total pack-year smoking history, and education. Black: results for any respiratory symptom, red: results for breathing symptoms (breathlessness, wheeze, tightness of the chest), blue: results for bronchitis symptoms (cough, sputum color, sputum amount). Abbreviations: OR, odds ratio, PM2.5, fine particulate matter less than 2.5 μm in diameter; NO2, nitrogen dioxide; O3, ozone.

3.4. Pollution exposure and oxygenation

Fig. 2 shows the association between previous-day exposure to air pollutants and daily change in oxygen saturation. Per IQR of previous-day personal NO2 exposure, oxygen saturation was on average 0.10 (95% CI: −0.21, 0.01) percentage points lower. Personal and community-level exposure to PM2.5 and O3 were not associated with daily oxygen saturation. Full results available in supplementary material (Appendix C).

Fig. 2.

Fig. 2.

Open in a new tab

Associations of personal and community-level exposure to air pollutants with daily oxygen saturation

Legend: Linear mixed-model analysis showing percentage point difference in oxygen saturation per interquartile range (IQR) difference in previous-day pollutant exposure. All models are adjusted for previous-day temperature, humidity, season, age, sex, race, height, weight, total pack-year smoking history, and education. Abbreviations: PM2.5, fine particulate matter less than 2.5 μm in diameter; NO2, nitrogen dioxide; O3, ozone; SpO2, blood oxygen saturation.

4. Discussion

In our study population of 30 former smokers with moderate-to-severe COPD living in the Greater Boston area, we found positive associations between community-level air pollutants, including PM2.5, O3 and NO2 concentrations and daily worsening of respiratory symptoms. Community-level O3 and PM2.5 were associated with worsening of any respiratory symptoms, while community-level NO2 was only associated with worsening bronchitis symptoms. On the other hand, personal exposure to PM2.5 was paradoxically associated with a lower odds of worsening symptoms. Personal exposure to NO2, but not PM2.5 or O3, was associated with lower daily oxygen saturation.

Our findings are consistent with a growing number of studies reporting adverse effects of community-level exposure to air pollution on respiratory symptoms among COPD patients. For example, Peacock et al. investigated the effects of outdoor air pollution, measured by local monitors in London, among 94 COPD subjects who recorded their symptoms by filling daily diaries (Peacock et al., 2011). The authors reported that an IQR increase in PM10 (19 μg/m3) was significantly associated with dyspnea (OR=1.13, 95% CI: 1.04–1.23, p = 0.008) and symptomatic fall in peak expiratory flow (OR=1.12, 95% CI: 1.02–1.25, p = 0.029). No significant association was observed between outdoor O3 and symptoms in the Peacock et al. (2011) study, in contrast to our study. On the other hand, a multi-center panel study of patients with COPD and asthma in 4 European cities found that a 10 μg/m3 increase in outdoor O3 for the same day, previous-day and previous 2 days were positively associated with cough (OR=1.06, 95% CI: 1.01–1.11 for same day and similar for previous 1–2 days), while associations of PM2.5 and NO2 with respiratory symptoms were inconsistent, suggesting that O3 gas is a more powerful respiratory irritant at ambient concentrations (Karakatsani et al., 2012). In another three-month prospective study among 40 COPD patients living in New Zealand, an IQR increase in outdoor PM10 (35.04 μg/m3) was associated with nighttime chest symptoms (RR=1.38, 95% CI: 1.07–1.78) and an IQR increase in outdoor NO2 (9.74 μg/m3) was associated with increased daily rescue medication use (RR=1.42, 95% CI: 1.13–1.79) (Harre et al., 1997). Taken together, these findings support the notion that outdoor gaseous and particulate pollutants can exacerbate daily respiratory symptoms in COPD patients. Differences in the findings of studies on the respiratory effects of ambient PM2.5, NO2 and O3 may be partially explained by different pollution levels between regions and over time, and different pollutant mixtures that comprise the ambient PM2.5 and gaseous pollution.

Despite the accumulating evidence that exposure to outdoor air pollutants affects COPD symptoms, few studies have focused on indoor exposures. In a Baltimore-based study, Hansel et al. (2013) found that each 10 μg/m3 increase in indoor PM2.5 in the main living area was associated with worse dyspnea (β=0.11, 95% CI 0.01–0.24, p = 0.06), more wheezing (β=0.27, 95% CI 0.11–0.43, p = 0.001), and higher risk of exacerbations (OR=1.50, 95% CI 1.04–2.18, p = 0.03) among former smokers with COPD (Hansel et al., 2013). Their results showed that exposure to indoor air pollutants, despite overall relatively low concentrations, was associated with worsening respiratory symptoms and increased risk of COPD exacerbations. Nevertheless, in our study population, despite previously finding negative associations between personal exposure to pollutants (especially NO2) and lower lung function (Nurhussien et al., 2021), we did not observe any adverse effects of personal exposure to air pollution measured by PAMs and respiratory symptoms.

Studies on effects of exposure to air pollutants on oxygen saturation are less common and have shown variable results (Jansen et al., 2005; Pope et al., 1999). A repeated-measures study on elderly subjects living in Steubenville, OH, found that oxygen saturation decreased by 0.18% (95% CI −0.31, −0.06) per 13.4 μg/m3 increase in PM2.5 in the previous day (Luttmann-Gibson et al., 2014). A chamber study involving controlled exposure of participants to different concentrations of air pollutants found that exposure to concentrated ambient particles, mainly PM2.5 at a concentration of 200 μg/m3 over 2 h, was associated with a statistically significant decrease in oxygen saturation estimated as 0.7% for healthy subjects (n = 6) and 0.3% for COPD patients (n = 18). In that study, co-exposure to NO2 did not significantly change the responses to particles, which led the authors to conclude that the observed decreases in oxygen saturation were driven primarily by PM2.5 (Gong et al., 2005). In contrast, our study found that personal exposure to NO2, not PM2.5, was associated with lower oxygen saturation.

Most studies of air pollution health effects have relied on fixed monitoring site data, which may not accurately measure personal exposure to air pollution (Butland et al., 2019b). Stationary, state-owned monitors are the gold standard used for monitoring of ambient air quality and for regulation of air quality standards. Studies in Boston and elsewhere have shown that outdoor air quality is a major determinant of indoor air quality (Brown et al., 2009; Freijer and Bloemen, 2011; Sarnat et al., 2002; Tang et al., 2017; Zou et al., 2009). Estimates from the state-owned monitors provide an unbiased estimate of community-level daily exposure that is not influenced by person-level factors. However, personal exposure to air pollution depends on several factors, including individual activity patterns, housing quality, indoor pollution (e.g. from cooking), and nearby outdoor pollution sources. Recent advances in measurement technologies, such as the portable PAMs used in our study, present an opportunity to assess the associations between personal exposure to air pollution and health outcomes (Mead et al., 2013). These personal monitors have the advantage of also measuring indoor air quality, where people actually spend most of their time, and may be able to offer a more individualized exposure assessment by capturing differences in exposure due to behaviors and housing characteristics. However, while the personal monitors have been calibrated to stationary monitors, the measurements are more variable and sensitive to environmental and technical factors (Chatzidiakou et al., 2019; Liu et al., 2019; Nurhussien et al., 2021).

A recent study conducted in London with a similar design to the present Boston study, including deployment of PAMs, found that personal exposure to NO2, but not PM2.5, was associated with a higher odds of COPD exacerbations (Evangelopoulos et al., 2021). Our study found that personal exposure to NO2 was associated with lower daily oxygen saturation, however, no association was found with symptoms, possibly due to smaller sample size and the low frequency of worsening respiratory symptoms in our study. Results of both studies suggest that personal exposure to NO2 might be more consistently harmful than PM2.5 to the daily respiratory health of COPD patients.

The negative association between personal exposure to PM2.5 and COPD symptoms observed in our study was also reported in the London study (Evangelopoulos et al., 2021). A potential reason could be that the total personal exposure to PM2.5 measured by the PAMs does not distinguish between indoor- and outdoor-generated particles. These particles come from different sources and therefore have different composition, characteristics, and effects on respiratory health (Evangelopoulos et al., 2020). Additionally, this unexpected finding could be explained by the “cloud-dust effect” phenomenon. Indoor activities on less symptomatic days may be associated with resuspension of the house dust, resulting in increased PM2.5 captured by PAMs (Ferro et al., 2004). More studies are needed in the future to identify impacts of particles from different sources on the respiratory health of COPD patients.

Our study has several limitations. Since our study population consists of former smokers with COPD living in an urban environment, our findings may not be generalizable to current smokers or those living in other settings. While we collected a large amount of longitudinal data (up to 120 observation-days per participant), our study only included 30 individuals with moderate-to-severe COPD who did not have a high frequency of symptom exacerbation, which also limits generalizability. We did not ascertain whether a participant ever left home without the PAM, which likely resulted in some misclassification of the personal exposure estimates. We used stationary monitors to estimate community-level exposure to the pollutants. Community-level, state-owned monitors are the gold standard used for monitoring and regulation of air quality standards. While this captures day to day variability in outdoor exposure, it may not capture contributions from more local sources including traffic. This is especially relevant for NO2 exposure among participants living nearer to major roadways. Lastly, our results could have been affected by unmeasured pollutants, including constituents of PM2.5 such as black carbon and ultrafine particles.

Our study also has several strengths. Our longitudinal study design with daily personal measures of exposure, respiratory symptoms, and oxygen saturation allowed us to evaluate how day-to-day variability in individual-level exposures relate to daily respiratory health in a vulnerable population while adjusting for a robust list of potential individual-level and seasonal confounders. We used lightweight, portable exposure monitors, calibrated to gold standard stationary monitors, to measure exposure at the individual level for a prolonged period (4 months) spanning all four seasons. Most studies have relied on community-level monitors or brief sampling periods to estimate exposure to pollution, which may not capture personal exposure to pollutants, especially among COPD patients who spend most of their time at home.

5. Conclusions

In this study of former smokers with COPD living in an urban environment, there was a pattern of worsening respiratory symptoms associated with previous-day community-level exposure to O3 and PM2.5, and worsening oxygenation associated with personal exposure to NO2. However, personal exposure to PM2.5 was associated with lower odds of worsening symptoms, possibly due to differences in the composition of indoor PM2.5. Future work is needed to determine the effects of personal exposure to air pollution on the respiratory health of COPD patients.

Supplementary Material

1
2
3

Acknowledgments

We would like to thank the SPACE study participants for volunteering their time and effort, and the research staff who assisted in this study, especially Wendy Sun, whose hard work and patience was integral to the success of this study. We also thank Drs. Ben Barratt, Lia Chatzidiakou, Rod Jones, and Mike Kellaway for their guidance on using the PAMs for respiratory health research. Lastly, we thank the staff of the Massachusetts Department of Environmental Protection for their assistance with the monitor calibrations.

Declaration of Competing Interest

Dr. Rice reports grants from the NIH and fees from the Conservation Law Foundation. Drs. Coull, Koutrakis and Synn report grants from the NIH. Dr. Scheerens reports financial support from Belgian American Educational Foundation and Strategic International Partnership Ghent University-Harvard University.

Funding information

This work was supported by the National Institutes of Environmental Health Sciences (K23ES026204, R01ES031252, P30ES000002).

Abbreviations:

COPD

chronic obstructive pulmonary disease

PM2.5

fine particulate matter less than 2.5 μm in aerodynamic diameter

NO2

nitrogen dioxide

O3

ozone

FEV1

forced expiratory volume in 1 second

FVC

forced vital capacity

PAM

personal air quality monitor

SpO2

oxygen saturation

SD

standard deviation

OR

odds ratio

CI

confidence interval

IQR

interquartile range

Footnotes

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.heha.2023.100052.

References

  1. Aaron SD, Donaldson GC, Whitmore GA, Hurst JR, Ramsay T, Wedzicha JA, 2012. Time course and pattern of COPD exacerbation onset. Thorax 67 (3). doi: 10.1136/thoraxjnl-2011-200768. [DOI] [PubMed] [Google Scholar]
  2. Brown KW, Sarnat JA, Suh HH, Coull BA, Koutrakis P, 2009. Factors influencing relationships between personal and ambient concentrations of gaseous and particulate pollutants. Sci. Total Environ 407 (12), 3754–3765. doi: 10.1016/J.SCIT0TENV.2009.02.016. [DOI] [PubMed] [Google Scholar]
  3. Butland BK, Samoli E, Atkinson RW, Barratt B, Katsouyanni K, 2019a. Measurement error in a multi-level analysis of air pollution and health: a simulation study. Environ. Health 18 (1). doi: 10.1186/s12940-018-0432-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Butland BK, Samoli E, Atkinson RW, Barratt B, Katsouyanni K, 2019b. Measurement error in a multi-level analysis of air pollution and health: a simulation study. Environ. Health 18 (1). doi: 10.1186/s12940-018-0432-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chatzidiakou L, Krause A, Popoola O, di Antonio A, Kellaway M, Han Y, Squires F, Wang T, Zhang H, Wang Q, Fan Y, Chen S, Hu M, Quint J, Barratt B, Kelly F, Zhu T, Jones R, 2019. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments. Atmos. Meas. Tech 12 (8), 4643–4657. doi: 10.5194/AMT-12-4643-2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cox, et al. , 2015. Cox Jr, Louis Anthony Tony. “National ambient air quality standards for ozone. Fed. Regist 75233 (2015), 75411. [Google Scholar]
  7. Duan R-R, Hao K, Yang T, 2020. Air pollution and chronic obstructive pulmonary disease. Chron. Dis. Translat. Med 6 (4). doi: 10.1016/j.cdtm.2020.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Evangelopoulos D, Chatzidiakou L, Walton H, Katsouyanni K, Kelly FJ, Quint JK, Jones RL, Barratt B, 2021. Personal exposure to air pollution and respiratory health of COPD patients in London. Eur. Respirat. J 58 (1). doi: 10.1183/13993003.03432-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Evangelopoulos D, Katsouyanni K, Keogh RH, Samoli E, Schwartz J, Barratt B, Zhang H, Walton H, 2020. PM2.5 and NO2 exposure errors using proxy measures, including derived personal exposure from outdoor sources: a systematic review and meta-analysis. Environ. Int 137. doi: 10.1016/j.envint.2020.105500. [DOI] [PubMed] [Google Scholar]
  10. Ferro AR, Kopperud RJ, Hildemann LM, 2004. Elevated personal exposure to particulate matter from human activities in a residence. J. Expo. Sci. Environ. Epidemiol 14 (S1). doi: 10.1038/sj.jea.7500356. [DOI] [PubMed] [Google Scholar]
  11. Freijer JI, & Bloemen HJT (2011). Modeling Relationships between Indoor and Outdoor Air Quality. 50(2), 292–300. doi: 10.1080/10473289.2000.10464007. [DOI] [PubMed] [Google Scholar]
  12. Gan WQ, FitzGerald JM, Carlsten C, Sadatsafavi M, Brauer M, 2013. Associations of ambient air pollution with chronic obstructive pulmonary disease hospitalization and mortality. Am. J. Respir. Crit. Care Med 187 (7). doi: 10.1164/rccm.201211-2004OC. [DOI] [PubMed] [Google Scholar]
  13. Global Initiative for Chronic Obstructive Lung Disease. (2019). In www.goldcopd.org.
  14. Gómez FP, Rodriguez-Roisin R, 2002. Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines for chronic obstructive pulmonary disease. Curr. Opin. Pulm. Med 8 (2). doi: 10.1097/00063198-200203000-00001. [DOI] [PubMed] [Google Scholar]
  15. Gong H Jr., Linn WS, Clark KW, Anderson KR, Geller MD, Sioutas C, 2005. Respiratory responses to exposures with fine particulates and nitrogen dioxide in the elderly with and without COPD. Inhal. Toxicol 17 (3). doi: 10.1080/08958370590904481. [DOI] [PubMed] [Google Scholar]
  16. Hankinson JL, Odencrantz JR, Fedan KB, 1999. Spirometric reference values from a sample of the general U.S. population. Am. J. Respir. Crit. Care Med 159 (1). doi: 10.1164/ajrccm.159.1.9712108. [DOI] [PubMed] [Google Scholar]
  17. Hansel NN, McCormack MC, Belli AJ, Matsui EC, Peng RD, Aloe C, Paulin L, Williams DL, Diette GB, Breysse PN, 2013. In-home air pollution is linked to respiratory morbidity in former smokers with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med 187 (10). doi: 10.1164/rccm.201211-1987OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Harre ES, Price PD, Ayrey RB, Toop LJ, Martin IR, Town GI, 1997. Respiratory effects of air pollution in chronic obstructive pulmonary disease: a three month prospective study. Thorax 52 (12). doi: 10.1136/thx.52.12.1040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG, 2009. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform 42 (2). doi: 10.1016/j.jbi.2008.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Huang S, Lawrence J, Kang C-M, Li J, Martins M, Vokonas P, Gold DR, Schwartz J, Coull BA, Koutrakis P, 2018. Road proximity influences indoor exposures to ambient fine particle mass and components. Environ. Pollut. (Barking, Essex : 1987) 243 (Pt B). doi: 10.1016/j.envpol.2018.09.046. [DOI] [PubMed] [Google Scholar]
  21. Jansen KL, Larson T.v., Koenig JQ, Mar TF, Fields C, Stewart J, Lippmann M, 2005. Associations between health effects and particulate matter and black carbon in subjects with respiratory disease. Environ. Health Perspect 113 (12). doi: 10.1289/ehp.8153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Karakatsani A, Analitis A, Perifanou D, Ayres JG, Harrison RM, Kotronarou A, Kavouras IG, Pekkanen J, Hämeri K, Kos GP, de Hartog JJ, Hoek G, Katsouyanni K, 2012. Particulate matter air pollution and respiratory symptoms in individuals having either asthma or chronic obstructive pulmonary disease: a European multicentre panel study. Environ. Health : A Glob. Access Sci. Sour 11. doi: 10.1186/1476-069X-11-75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kelly FJ, Fussell JC, 2015. Air pollution and public health: emerging hazards and improved understanding of risk. Environ. Geochem. Health 37 (4). doi: 10.1007/s10653-015-9720-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Leech JA, Smith-Doiron M, 2006. Exposure time and place: do COPD patients differ from the general population? J. Expo. Sci. Environ. Epidemiol 16 (3). doi: 10.1038/sj.jea.7500452. [DOI] [PubMed] [Google Scholar]
  25. Liu M, Lin J, Boersma KF, Pinardi G, Wang Y, Chimot J, Wagner T, Xie P, Eskes H, van Roozendael M, Hendrick F, Wang P, Wang T, Yan Y, Chen L, Ni R, 2019a. Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile. Atmos. Meas. Tech 12 (1). doi: 10.5194/amt-12-1-2019. [DOI] [Google Scholar]
  26. Liu M, Lin J, Folkert Boersma K, Pinardi G, Wang Y, Chimot J, Wagner T, Xie P, Eskes H, van Roozendael M, Hendrick F, Wang P, Wang T, Yan Y, Chen L, Ni R, 2019b. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments. Atmos. Meas. Tech 12 (8), 1–21. doi: 10.5194/AMT-12-1-2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Luttmann-Gibson H, Sarnat SE, Suh HH, Coull BA, Schwartz J, Zanobetti A, Gold DR, 2014. Short-term effects of air pollution on oxygen saturation in a cohort of senior adults in Steubenville, Ohio. J. Occupat. Environ. Med 56 (2). doi: 10.1097/JOM.0000000000000089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M, Hayes M, Baldovi JJ, McLeod MW, Hodgson TF, Dicks J, Lewis A, Cohen J, Baron R, Saffell JR, Jones RL, 2013. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ 70. doi: 10.1016/j.atmosenv.2012.11.060. [DOI] [Google Scholar]
  29. Miravitlles M, Kruesmann F, Haverstock D, Perroncel R, Choudhri SH, Arvis P, 2012. Sputum colour and bacteria in chronic bronchitis exacerbations: a pooled analysis. Eur. Respir. J 39 (6). doi: 10.1183/09031936.00042111. [DOI] [PubMed] [Google Scholar]
  30. Moore E, Chatzidiakou L, Kuku M-O, Jones RL, Smeeth L, Beevers S, Kelly FJ, Barratt B, Quint JK, 2016. Global associations between air pollutants and chronic obstructive pulmonary disease hospitalizations. a systematic review. Ann. Am. Thorac. Soc 13 (10). doi: 10.1513/AnnalsATS.201601-064OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nurhussien L, Kang C-M, Koutrakis P, Coull BA, Rice MB, 2021. Air pollution exposure and daily lung function in COPD: effect modification by eosinophilia. Ann. Am. Thorac. Soc doi: 10.1513/AnnalsATS.202107-846OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Peacock JL, Anderson HR, Bremner SA, Marston L, Seemungal TA, Strachan DP, Wedzicha JA, 2011. Outdoor air pollution and respiratory health in patients with COPD. Thorax 66 (7). doi: 10.1136/thx.2010.155358. [DOI] [PubMed] [Google Scholar]
  33. Pope CA 3rd, Dockery DW, Kanner RE, Villegas GM, Schwartz J, 1999. Oxygen saturation, pulse rate, and particulate air pollution: a daily time-series panel study. Am. J. Respir. Crit. Care Med 159 (2). doi: 10.1164/ajrccm.159.2.9702103. [DOI] [PubMed] [Google Scholar]
  34. Sarnat JA, Long CM, Koutrakis P, Coull BA, Schwartz J, Suh HH, 2002. Using sulfur as a tracer of outdoor fine particulate matter. Environ. Sci. Technol 36 (24), 5305–5314. doi: 10.1021/ES025796B. [DOI] [PubMed] [Google Scholar]
  35. Scheerens C, Nurhussien L, Aglan A, Synn AJ, Coull BA, Koutrakis P, Rice MB, 2022. The impact of personal and outdoor temperature exposure during cold and warm seasons on lung function and respiratory symptoms in COPD. ERJ Open Res. 8 (1). doi: 10.1183/23120541.00574-2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Seemungal TA, Donaldson GC, Paul EA, Bestall JC, Jeffries DJ, Wedzicha JA, 1998. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med 157 (5 Pt 1). doi: 10.1164/ajrccm.157.5.9709032. [DOI] [PubMed] [Google Scholar]
  37. Song Q, Christiani DC, Xiaorong Wang, Ren J, 2014. The global contribution of outdoor air pollution to the incidence, prevalence, mortality and hospital admission for chronic obstructive pulmonary disease: a systematic review and meta-analysis. Int. J. Environ. Res. Public Health 11 (11). doi: 10.3390/ijerph111111822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sun WY, Zhang C, Synn AJ, Nurhussien L, Coull BA, Rice MB, 2020. Change in inhaler use, lung function, and oxygenation in association with symptoms in COPD. Chronic Obstr. Pulm. Dis. (Miami) 7 (4). doi: 10.15326/jcopdf.7.4.2020.0138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tang CH, Garshick E, Grady S, Coull B, Schwartz J, Koutrakis P, 2017. Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households. J. Expo. Sci. Environ. Epidemiol 28 (2), 125–130. doi: 10.1038/jes.2017.11, 2018 28:2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, de Matteis S, Forastiere F, Forsberg B, Frampton MW, Grigg J, Heederik D, Kelly FJ, Kuenzli N, Laumbach R, Peters A, Rajagopalan ST, Rich D, Ritz B, … Brunekreef B, 2017. A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework. Eur. Respir. J 49 (1). doi: 10.1183/13993003.00419-2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. US EPA, 2013. US EPA (US Environmental Protection Agency). “National ambient air quality standards for particulate matter. Final rule. Fed. Reg 78 (2013), 3086–3287 10. [Google Scholar]
  42. Wilkinson TMA, Donaldson GC, Hurst JR, Seemungal TAR, Wedzicha JA, 2004. Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med 169 (12). doi: 10.1164/rccm.200310-1443OC. [DOI] [PubMed] [Google Scholar]
  43. Zou B, Wilson JG, Zhan FB, Zeng Y, 2009. Air pollution exposure assessment methods utilized in epidemiological studies. J. Environ. Monitor 11 (3), 475–490. doi: 10.1039/B813889C. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1902532-supplement-1.docx (14.8KB, docx)
2
NIHMS1902532-supplement-2.docx (15.7KB, docx)
3
NIHMS1902532-supplement-3.docx (14.2KB, docx)

RESOURCES