Comments
Abstract
Rationale: Fine particulate air pollution (PM2.5; particulate matter 2.5 μm or less in diameter) is thought to contribute to acute respiratory morbidity in part through oxidative stress.
Objectives: To examine the association between PM2.5 oxidative burden and emergency room visits for respiratory illnesses.
Methods: We conducted a case-crossover study in Ontario, Canada between 2004 and 2011, including 127,836 cases of asthma, 298,751 cases of chronic obstructive pulmonary disease, and more than 1.1 million cases of all respiratory illnesses. Daily air pollution data were collected from ground monitors, and city-level PM2.5 oxidative potential was measured on the basis of a synthetic respiratory tract lining fluid containing the antioxidants glutathione and ascorbate. Conditional logistic regression was used to estimate associations between air pollution concentrations and emergency room visits, adjusting for time-varying covariates.
Measurements and Main Results: Three-day mean PM2.5 concentrations were consistently associated with emergency room visits for all respiratory illnesses. Among children (<9 yr), each interquartile change (5.92 μg/m3) in 3-day mean PM2.5 was associated with a 7.2% (95% confidence interval, 4.2–10) increased risk of emergency room visits for asthma. Glutathione-related oxidative potential modified the impact of PM2.5 on emergency room visits for respiratory illnesses (P = 0.001) but only at low concentrations (≤10 μg/m3). Between-city differences in ascorbate-related oxidative potential did not modify the impact of PM2.5 on respiratory outcomes.
Conclusions: Between-city differences in glutathione-related oxidative potential may modify the impact of PM2.5 on acute respiratory illnesses at low PM2.5 concentrations. This may explain in part how small changes in ambient PM2.5 mass concentrations can contribute to acute respiratory morbidity in low-pollution environments.
Oxidative stress plays an important role in the underlying biological mechanisms explaining air pollution health effects. However, it is not clear how between-city differences in the oxidative potential of fine particulate air pollution (PM2.5; particulate matter 2.5 μm or less in diameter) may modify the short-term respiratory health effects of this pollutant.
Our findings suggest that between-city differences in glutathione-related oxidative potential may modify the impact of PM2.5 on respiratory illnesses, particularly at low ambient concentrations. This may explain in part how small changes in ambient PM2.5 mass concentrations can contribute to acute respiratory morbidity in low-pollution environments. In the low-pollution environments typical of North America, short-term changes in PM2.5 mass concentrations may be of most concern in areas with high glutathione-related oxidative potential.
Ambient fine particulate air pollution (PM2.5; particulate matter 2.5 μm or less in diameter) is known to contribute to acute respiratory morbidity/mortality including increased risks of emergency room visits/hospital admissions for asthma and chronic obstructive pulmonary disease (COPD) (1–4). Whereas oxidative stress is known to play an important role in the health impacts of ambient PM2.5 (5–7), it is not clear how temporal (i.e., day-to-day) or regional (i.e., between-city) differences in oxidative potential may modify the acute respiratory health effects of this pollutant. This study focuses on the impact of between-city differences in PM2.5 oxidative potential as daily data are not yet available to support analyses of short-term temporal variations.
Glutathione and ascorbate are important antioxidants in the lung and act as a first line of defense against inhaled pollutants (7). Moreover, some evidence suggests that subjects with asthma have decreased ascorbate concentrations in lung lining fluid (8) and lower systemic antioxidant concentrations (including reduced glutathione) compared with healthy control subjects (9). Several studies have reported that the oxidative properties of particulate air pollution vary both between (10) and within regions (11–14). In particular, some studies indicate that regional differences in PM2.5 oxidative potential may be explained in part by differences in particle composition including transition metals, polycyclic aromatic hydrocarbons, and/or quinones (10, 11, 14, 15). In addition, studies have reported stronger associations between PM2.5 oxidative potential and airway inflammation, asthma incidence, and asthma prevalence among children compared with PM2.5 mass concentrations (16, 17). Nevertheless, few studies have examined how between-city differences in PM2.5 oxidative potential may impact overall health effects, and current guidelines treat all PM2.5 as equally toxic despite potential differences in particle composition or biological activity.
In this study, we examined the relationship between PM2.5 and the risk of emergency room visits for respiratory illnesses in the province of Ontario, Canada. The term oxidative potential is used to describe the ability of PM2.5 filter extracts (reflecting long-term city-level oxidative potential) to deplete the antioxidants glutathione and ascorbate in a simulated respiratory tract lining fluid. The term oxidative burden is used to describe the product of daily PM2.5 mass concentrations and long-term estimates of city-level oxidative potential (units: % Depletion/m3). To our knowledge this is the first study to examine how between-city differences in PM2.5 oxidative potential may modify associations between PM2.5 and respiratory illnesses at the population level.
The laboratory and statistical methods used in this study are the same as those applied in a related manuscript examining regional differences in PM2.5 oxidative potential and emergency room visits for myocardial infarction (18). In this study, short-term changes in ambient PM2.5 were associated with increased emergency room visits for myocardial infarction, with stronger associations observed in areas with higher glutathione-related oxidative potential (18).
A time-stratified case-crossover study design (19) was used to estimate associations between short-term changes in ambient air pollution concentrations and risks of emergency room visits for asthma (International Classification of Diseases-10th Revision [ICD]; code J45), chronic obstructive pulmonary disease (COPD) (ICD-10th revision, codes J40–J44), and all respiratory outcomes (ICD-10th revision, codes J00–J99) in 15 cities across Ontario, Canada: Barrie, Chatham, Cornwall, Hamilton, Kingston, Kitchener, Oakville, Oshawa, Petawawa, Peterborough, Port Stanley, Sarnia, St. Catharines, Thunder Bay, and Toronto (Figure 1). Cases occurring between April 1, 2004 and December 31, 2011 were extracted from the National Ambulatory Care Reporting System (NACRS) database maintained by the Canadian Institute for Health Information (CIHI). Demographic information (i.e., age and sex) was also recorded. All cases with three-digit residential postal codes corresponding to these cities at the time of admission were eligible to be included in the analyses. The NACRS database captures more than 97% of the emergency department visits in Ontario (20). Ethics approval for this study was granted through a data-sharing agreement between Health Canada and CIHI.
Figure 1. City-level estimates of (A) glutathione-related oxidative potential (OPGSH) and (B) ascorbate-related oxidative potential (OPAA) for (C) the locations studied across Ontario, Canada (2012–2013).
[More] [Minimize]Daily average concentrations of ambient PM2.5, NO2, and O3 were collected from fixed-site monitoring stations in Ontario, which are part of Canada’s National Air Pollution Surveillance (NAPS) network. Daily mean temperature and relative humidity data were also collected from these stations. Only 2 years of daily air pollution data were available for Barrie, Oshawa, and Hamilton West. The remaining sites had 5–8 years of daily air pollution data available. Exposures were assigned to case and control periods based on the monitoring station located in each subject’s city of residence; if air pollution/oxidative potential data were available for multiple monitors in a single city (i.e., Toronto and Hamilton), measurements were assigned to subjects based on the monitor closest to the population-weighted center of their three-digit postal code. The median (interquartile range [IQR]) distance between monitoring stations and the population-weighted centers of each subject’s three-digit postal code was 3.9 km (2.5–5.3 km).
Integrated regional (i.e., city-level) PM2.5 samples were collected between 2012 and 2013 from 19 sites located in 15 cities (Hamilton and Toronto each had three monitors) across Ontario. The province of Ontario uses Thermo TEOM 1400AB monitors (Thermo Fisher Scientific, Waltham, MA) with sample equilibration systems across its network at an operating temperature of 30°C; therefore, some volatile chemical species are expected to be lost from the filters. However, previous studies of nonvolatile PM components have shown stable results for oxidative potential (14). Only those cities with both long-term estimates of PM2.5 oxidative potential and daily air pollution/meteorological data were included in the analyses. City-level estimates of PM2.5 oxidative potential were based on a mean duration of 110 sampling days (IQR, 60–155); these estimates were generally based on multiple filter samples per site (range, 1–7). The duration of time (and seasons) reflected by each filter depended on the number of filters available, the start date of sample collection, and how often TEOM filters were changed by station managers throughout the year, typically every 6 weeks.
An in vitro assay based on a synthetic respiratory tract lining fluid was used to quantify glutathione-related oxidative potential (OPGSH) and ascorbate-related oxidative potential (OPAA) as previously described (14). A detailed description of the laboratory methods used to quantify oxidative potential is provided in the online supplement. In this study, the term oxidative potential is used to describe the ability of PM2.5 filter extracts (reflecting long-term city level oxidative potential) to deplete glutathione and ascorbate in the simulated respiratory tract lining fluid (units: % Depletion/μg PM2.5). The term oxidative burden is used to describe the product of daily PM2.5 mass concentrations and long-term estimates of city-level oxidative potential. Although the oxidative burden measure is essentially a reweighting of PM2.5 mass concentrations according to oxidative potential, it can also be viewed as a different exposure measured in units of % Depletion/m3 as opposed to μg/m3.
Conditional logistic regression models were used to examine the association between short-term changes in ambient air pollutants and the risk of emergency room visits for asthma, COPD, and all respiratory outcomes. All models were adjusted for 3-day mean ambient temperature and relative humidity using restricted cubic splines with three equally spaced knots and daily counts of emergency room visits for influenza. All analyses pooled cases across cities to evaluate potential effect modification by between-city differences in oxidative potential; a cluster variance estimator was used to account for potential within-city correlations.
Four different exposure lag periods were evaluated for ambient air pollutants (in separate models): lag 0 (the same day as the emergency room visit), lag 1 (the day before the visit), lag 2 (2 days before the visit), and the mean of lags 0–2 (i.e., 3-day mean). Because the case-crossover design compares cases with themselves at different points in time it adjusts for factors that do not vary within individuals over short time periods (e.g., age, smoking status, body mass index). In this study, matched sets consisted of the case period (the day of the emergency room visit) and control periods selected on the same day of the week in the same month and year as the case period (i.e., three or four referent periods per case). This time-stratified approach to referent selection has been shown to result in unbiased conditional logistic regression estimates in case-crossover studies (19).
PM2.5 oxidative burden metrics were generated for glutathione (PM2.5 × OPGSH) and ascorbate (PM2.5 × OPAA) by multiplying daily mean ambient PM2.5 mass concentrations (μg/m3) by city-level estimates of oxidative potential. These parameters reflect a reweighting of PM2.5 mass concentrations according to city-level oxidative potential and were treated as separate exposure variables in the analysis. Potential effect modification by age and sex were evaluated through stratified analyses and by including the appropriate interaction terms in statistical models (age was treated as a continuous variable for interaction terms). The shapes of concentration–response curves were examined for PM2.5 across quartiles of city-level oxidative potential using restricted cubic splines with three equally placed knots. The P values for interaction terms between PM2.5 and categorical variables for quartiles of oxidative potential were used to evaluate the statistical significance of effect modification by between-city differences in oxidative potential. Concentration–response plots for PM2.5 are shown over a range of 0–10 μg/m3 as this change represented the 95th percentile of the observed difference in 3-day mean PM2.5 concentrations between case and control periods. In addition, approximately 80% of all respiratory cases (including asthma and COPD) occurred on days with ambient PM2.5 concentrations less than or equal to 10 μg/m3. Models with four equally spaced knots were also evaluated but did not improve model fit based on the Akaike information criterion.
For sensitivity analysis we examined the impact of adding ambient NO2 or O3 to single pollutant models for PM2.5 and PM2.5 oxidative burden. The lag time with the strongest association was used for gaseous pollutants in multipollutant models. In addition, we examined the combined oxidant capacity (Ox) of NO2 and O3 calculated as their sum (21) as well as their redox-weighted oxidant capacity () calculated as a weighted average using redox potentials (22) as the weights (i.e., = [(1.07 V × NO2) + (2.075 V × O3)]/3.145 V). The redox-weighted measure accounts for the fact that O3 is a stronger oxidant than NO2. All risk estimates reflect IQR changes in ambient air pollution concentrations. Interquartile ranges were similar across exposure lag periods (within 1 μg/m3 for PM2.5 and within 1 ppb for NO2 and O3) and thus lag-0 IQR values were used as the exposure increment for all statistical analyses. All statistical analyses were conducted with Stata version 13 (StataCorp, College Station, TX).
In total, 128,731 cases of asthma, 298,751 cases of COPD, and more than 1.1 million cases of emergency room visits for all respiratory illnesses were included in the analyses (Table 1). Asthma patients tended to be younger than patients with COPD or all respiratory outcomes combined, but both sexes were present in approximately equal proportions. Air pollution concentrations were generally low (Table 2) and correlations between daily mean PM2.5 and gaseous pollutants (spatiotemporal correlations considering data from all sites pooled) were weak (r < 0.42). NO2 was inversely correlated with O3 (r = –0.35) and daily PM2.5 concentrations were not correlated with city-level oxidative potential measures (r < 0.10) (i.e., cities with higher oxidative potential did not tend to have higher mass concentrations). Glutathione- and ascorbate-related oxidative burden were weakly correlated with gaseous pollutants (r < 0.38) and were moderately correlated with each other (r = 0.67). City-level estimates of PM2.5 oxidative potential varied substantially between cities (Figure 1), and cities with high glutathione-related oxidative potential tended to have lower ascorbate-related oxidative potential (see Figure E1 in the online supplement). To evaluate possible changes in air pollution sources over time, we also examined temporal trends in the correlation between daily mean PM2.5 and NO2 within cities. In general, the correlation between PM2.5 and NO2 did not change over time (suggesting stable sources) and only one site (downtown Toronto) had a significant relationship between time and the correlation between these two pollutants (slope, –0.022; 95% confidence interval, –0.037 to –0.0072).
| Outcome | No. of Cases | Male (%) | Mean Age (SD) (yr) |
|---|---|---|---|
| Asthma | 128,731 | 48.8 | 27.5 (21.4) |
| COPD | 298,751 | 47.6 | 44.6 (26.0) |
| All respiratory | 1,181,961 | 47.8 | 33.9 (25.8) |
| Air Pollutant | Mean (SD) | Median | IQR | Range |
|---|---|---|---|---|
| PM2.5, μg/m3 | 7.10 (6.25) | 5.21 | 5.92 | <1 to 56.8 |
| PM2.5 × OPGSH | 0.992 (1.10) | 0.63 | 0.89 | <1 to 13.5 |
| PM2.5 × OPAA | 1.62 (1.50) | 1.17 | 1.37 | <1 to 20.4 |
| NO2, ppb | 12.9 (7.79) | 11.5 | 10.6 | <1 to 65.0 |
| O3, ppb | 25.2 (10.4) | 24.5 | 14.5 | <1 to 74.7 |
| Ox, ppb | 38.1 (10.5) | 37.2 | 13.8 | 8.35 to 88.0 |
| , ppb | 21.0 (6.39) | 20.4 | 8.84 | 3.12 to 52.2 |
| Temperature, °C | 8.20 (9.97) | 8.90 | 15.2 | −29.9 to 32.1 |
| Relative humidity, % | 74.4 (11.8) | 75.0 | 16.2 | 25.5 to 100 |
Short-term changes in ambient PM2.5 mass concentrations and PM2.5 oxidative burden were consistently associated with increased risks of emergency room visits for asthma, COPD, and all respiratory outcomes combined with the strongest associations observed with 3-day mean concentrations (Table 3). Moreover, these associations were robust to adjustment for gaseous pollutants in multipollutant models (see Tables E1–E3). Ozone, Ox, and were also associated with emergency room visits for asthma, COPD, and all respiratory outcomes in single-pollutant models, with the strongest associations observed with 3-day mean concentrations (Table 4). Ambient NO2 was associated with emergency room visits for asthma in single-pollutant models but was inversely associated with all three outcomes after adjusting for 3-day mean PM2.5 (see Tables E1–E3). Risk estimates for O3, Ox, and also decreased when PM2.5 was included in the models (see Tables E1–E3); however, associations for O3 remained significantly elevated for all three outcomes when PM2.5 mass concentrations or PM2.5 oxidative burden were included in the models.
| Outcome | Lag | Exposure | ||
|---|---|---|---|---|
| PM2.5 | PM2.5 × OPGSH | PM2.5 × OPAA | ||
| [% Change (95% CI)] | [% Change (95% CI)] | [% Change (95% CI)] | ||
| Asthma | 0 | 1.1 (0.79–1.4) | 0.87 (0.48–1.3) | 1.0 (0.67–1.4) |
| 1 | 2.1 (1.5–2.7) | 1.6 (1.0–2.2) | 1.9 (1.1–2.7) | |
| 2 | 2.4 (1.6–3.3) | 1.9 (1.2–2.7) | 2.2 (1.3–3.2) | |
| 3-d mean | 3.5 (2.7–4.3) | 2.6 (1.8–3.5) | 3.1 (2.0–4.2) | |
| COPD | 0 | 1.0 (0.61–1.4) | 0.85 (0.45–1.3) | 0.91 (0.43–1.4) |
| 1 | 1.3 (0.76–1.8) | 0.93 (0.54–1.3) | 1.1 (0.48–1.8) | |
| 2 | 1.2 (0.74–1.6) | 0.91 (0.52–1.3) | 1.1 (0.55–1.6) | |
| 3-d mean | 2.2 (1.4–2.9) | 1.6 (0.98–2.2) | 1.9 (0.2–2.8) | |
| All respiratory | 0 | 0.97 (0.70–1.2) | 0.85 (0.63–1.1) | 0.83 (0.55–1.1) |
| 1 | 1.2 (0.66–1.7) | 0.91 (0.39–1.4) | 1.0 (0.52–1.5) | |
| 2 | 0.99 (0.58–1.4) | 0.83 (0.42–1.3) | 0.92 (0.56–1.3) | |
| 3-d mean | 1.9 (1.3–2.5) | 1.5 (0.93–2.1) | 1.6 (1.1–2.2) | |
| Outcome | Lag | Exposure | |||
|---|---|---|---|---|---|
| NO2 [% Change (95% CI)] | O3 [% Change (95% CI)] | Ox [% Change (95% CI)] | [% Change (95% CI)] | ||
| Asthma | 0 | 0.73 (0.015 to 1.5) | −0.30 (–1.4 to 0.83) | 0.32 (–1.0 to 1.6) | 0.049 (–1.3 to 1.4) |
| 1 | 0.26 (–0.40 to 0.94) | 2.9 (2.1 to 3.8) | 2.8 (2.1 to 3.5) | 3.3 (2.4 to 4.2) | |
| 2 | 2.0 (0.78 to 3.3) | 3.3 (1.0 to 5.6) | 4.5 (3.0 to 6.1) | 4.5 (2.4 to 6.6) | |
| 3-d mean | 1.7 (0.25 to 3.2) | 3.9 (1.4 to 6.6) | 4.5 (2.6 to 6.4) | 5.0 (2.5 to 7.6) | |
| COPD | 0 | 0.69 (–0.1 to 1.5) | 0.15 (–1.0 to 1.3) | 0.68 (–0.30 to 1.6) | 0.49 (–0.70 to 1.6) |
| 1 | −0.10 (–1.0 to 0.71) | 2.2 (1.1 to 3.3) | 1.9 (0.99 to 2.8) | 2.3 (1.3 to 3.4) | |
| 2 | 0.26 (–0.80 to 1.4) | 2.7 (1.3 to 4.0) | 2.6 (1.7 to 3.6) | 3.0 (1.8 to 4.2) | |
| 3-d mean | 0.52 (–0.80 to 1.9) | 3.3 (1.3 to 5.4) | 3.1 (1.7 to 4.5) | 3.7 (1.9 to 5.6) | |
| All respiratory | 0 | −0.10 (–0.80 to 0.60) | 0.97 (0.37 to 1.6) | 0.82 (0.36 to 1.3) | 1.0 (0.51 to 1.5) |
| 1 | −0.50 (–1.2 to 0.24) | 2.3 (1.7 to 3.0) | 1.8 (0.93 to 2.6) | 2.3 (1.6 to 3.1) | |
| 2 | −0.30 (–0.90 to 0.41) | 2.0 (1.3 to 2.8) | 1.7 (1.1 to 2.3) | 2.1 (1.4 to 2.8) | |
| 3-d mean | −0.50 (–1.5 to 0.60) | 3.7 (2.6 to 4.7) | 2.6 (1.5 to 3.6) | 3.6 (2.5 to 4.6) | |
We did not observe significant interactions between air pollution exposures and age or sex for any of the exposures/outcomes examined (interaction P values > 0.05). However, relationships between 3-day mean PM2.5 (and 3-d PM2.5 oxidative burden) and emergency room visits for asthma tended to be strongest among children (see Table E4). Specifically, each IQR increase in 3-day mean PM2.5 was associated with a 7.2% (95% confidence interval, 4.2–10) increased risk of emergency room visits for asthma among children (<9 yr of age) whereas increases of 1.9–2.2% were observed for other age strata. Concentration–response relationships between 3-day mean PM2.5 and emergency room visits for asthma are shown in Figure 2. A similar pattern was observed for the relationship between 3-day mean PM2.5 and emergency room visits for COPD (Figure 3).
Figure 2. Concentration–response curves for 3-day mean ambient PM2.5 (particulate matter 2.5 μm or less in diameter) and risk of emergency room visits for (A) asthma among children (<9 yr of age) and for all cases (B) in Ontario, Canada (2004–2011). Solid lines reflect point estimates, and dashed lines reflect 95% confidence intervals (CIs). Models are adjusted for 3-day mean ambient temperature and relative humidity (restricted cubic splines) and daily counts of emergency room visits for influenza.
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Figure 3. Concentration–response curve for 3-day mean ambient PM2.5 (particulate matter 2.5 μm or less in diameter) and risk of emergency room visits for chronic obstructive pulmonary disease in Ontario, Canada (2004–2011). Solid lines reflect point estimates, and dashed lines reflect 95% confidence intervals (CIs). The model is adjusted for 3-day mean ambient temperature and relative humidity (restricted cubic splines) and daily counts of emergency room visits for influenza.
[More] [Minimize]Significant effect modification by city-level differences in oxidative potential was not observed over the full range of PM2.5 exposures (interaction P values > 0.05). However, analysis of concentration–response plots suggested that city-level differences in glutathione-related oxidative potential modified the impact of PM2.5 on the risk of emergency room visits for asthma and all respiratory outcomes at low ambient concentrations, with point estimates and 95% confidence intervals overlapping only at higher levels of exposure (see Figures E2–E4). To test this hypothesis, we reexamined possible effect modification by glutathione-related oxidative potential on days with 3-day mean ambient PM2.5 mass concentrations less than or equal to 10 μg/m3 (more than 80% of cases occurred on days with ambient PM2.5 concentrations less than or equal to 10 μg/m3). In these analyses, city-level differences in glutathione-related oxidative potential modified the impact of PM2.5 on the risk of emergency room visits for asthma (interaction P value = 0.007) (Figure 4) and all respiratory outcomes combined (interaction P value = 0.001) (Figure 5); however, significant effect modification by glutathione-related oxidative potential was not observed for COPD (interaction P value = 0.09) (Table 5).
Figure 4. Concentration–response curves for 3-day mean ambient PM2.5 (particulate matter 2.5 μm or less in diameter) and risk of emergency room visits for asthma below the 25th percentile of glutathione-related oxidative potential (OPGSH) (black lines) and above the 75th percentile of OPGSH (blue lines) in Ontario, Canada (2004–2011) at low PM2.5 concentrations (≤10 μg/m3). Solid lines reflect point estimates, and dashed lines reflect 95% confidence intervals (CIs). Models are adjusted for 3-day mean ambient temperature and relative humidity (restricted cubic splines) and daily counts of emergency room visits for influenza.
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Figure 5. Concentration–response curves for 3-day mean ambient PM2.5 (particulate matter 2.5 μm or less in diameter) and risk of emergency room visits for all respiratory outcomes below the 25th percentile of glutathione-related oxidative potential (OPGSH) (black lines) and above the 75th percentile of OPGSH (blue lines) in Ontario, Canada (2004–2011) at low PM2.5 concentrations (≤10 μg/m3). Solid lines reflect point estimates and dashed lines reflect 95% confidence intervals (95% CIs). Models are adjusted for 3-day mean ambient temperature and relative humidity (restricted cubic splines) and daily counts of emergency room visits for influenza.
[More] [Minimize]| Outcome | Percentile of OPGSH | Interaction P Value | |||
|---|---|---|---|---|---|
| <25th | 25th–50th | >50th–75th | >75th | ||
| [% Change (95% CI)] | [% Change (95% CI)] | [% Change (95% CI)] | [% Change (95% CI)] | ||
| Asthma | 0.92 (–0.35 to 2.2) | 3.6 (0.0 to 7.5) | 4.6 (–1.5 to 11) | 5.9 (3.0 to 8.9) | 0.007 |
| COPD | 0.76 (–2.4 to 4.1) | 0.82 (–3.7 to 5.5) | 2.8 (–0.10 to 5.7) | 3.5 (2.1 to 4.9) | 0.09 |
| All respiratory | 0.47 (–0.40 to 1.4) | 2.2 (1.7 to 2.8) | 2.7 (0.096 to 5.5) | 2.8 (1.5 to 2.0) | 0.001 |
Between-city differences in ascorbate-related oxidative potential did not have a meaningful impact on the shapes of concentration–response relationships and 95% confidence intervals overlapped for analyses limited to the top and bottom quartiles of ascorbate-related oxidative potential (see Figures E5–E7). For all outcomes, associations with PM2.5 mass concentrations were stronger in the lowest quartile of ascorbate-related oxidative potential compared with the highest quartile; however, this may be explained in part by the inverse relationship between glutathione- and ascorbate-related oxidative potential as areas with low ascorbate-related oxidative potential tended to have higher glutathione-related oxidative potential
To our knowledge, this is the first epidemiological study to examine how between-city differences in PM2.5 oxidative potential may modify associations between short-term changes in ambient PM2.5 and emergency room visits for respiratory illnesses. As in previous studies, our findings suggest that short-term changes in ambient PM2.5 and O3 contribute to increased emergency room visits for respiratory illnesses including asthma and COPD (1–4, 23, 24). More importantly, our results also indicate that between-city differences in glutathione-related oxidative potential may modify the impact of PM2.5 mass concentrations on respiratory morbidity, particularly at low ambient concentrations.
Although effect modification by glutathione-related oxidative potential was limited to days with low ambient concentrations this does not diminish the importance of this finding. Indeed, many regions of North America have ambient PM2.5 concentrations similar to those observed in this study, and more than 80% of all emergency room visits for respiratory illnesses in Ontario between 2004 and 2011 occurred on days with low ambient PM2.5 levels (≤10 μg/m3). Therefore, our findings suggest that regional differences in glutathione-related oxidative potential may play an important role in explaining between-city differences in the respiratory health effects of ambient PM2.5. Moreover, this finding may also explain in part how small changes in PM2.5 mass concentrations in relatively low-pollution environments can contribute to the exacerbation of respiratory illnesses. In particular, our findings suggest that at low PM2.5 mass concentrations, short-term changes in ambient PM2.5 are of relevance to respiratory health predominantly in areas with high glutathione-related oxidative potential, with little increased risk observed in regions with low oxidative potential. Bulk particle mass concentrations appear to become more important only as pollution levels increase, with concentration–response curves for PM2.5 in high and low oxidative potential areas overlapping only at higher concentrations. Local sources most likely have greater influence on PM2.5 composition, and hence oxidative potential, when PM2.5 concentrations are low. In contrast, higher levels of PM2.5 typically arise in Ontario because of regional influences whereby the composition of PM2.5 becomes more similar across the region. Therefore, the stronger effect modification at low ambient concentrations may reflect spatial heterogeneity in the influence of local sources.
Oxidative stress plays an important role in the development of air pollution health effects (3–5), and the physiological function of glutathione supports the biological plausibility of our findings. In particular, glutathione acts to control oxidative stress in the lungs, and the ratio of reduced to oxidized glutathione (GSH/GSSG) is an important parameter in the regulation of airway inflammation (25–28). Moreover, some evidence suggests that polymorphisms in glutathione S-transferase genes that decrease antioxidant capacity may increase the risk of COPD (29). Similarly, others have reported stronger associations between exposure to environmental tobacco smoke and asthma among children with glutathione S-transferase gene polymorphisms (30). Ascorbate is also an important antioxidant (31), and some studies have noted decreased ascorbate concentrations in the lung lining fluid of subjects with asthma (8). However, between-city differences in ascorbate-related oxidative potential did not modify the relationship between PM2.5 and emergency room visits for respiratory illnesses in this study.
Other studies have not examined the association between ascorbate-related oxidative potential and emergency room visits for respiratory outcomes; however, a cross-sectional study of plasma antioxidant concentrations among patients with COPD did not observe a significant association between plasma ascorbate concentrations and COPD phenotypes whereas lower plasma glutathione levels were associated with a history of COPD exacerbations (32). Similarly, a comparison of ascorbate concentrations in bronchoalveolar lavage fluid from children with asthma and children without asthma did not report a significant difference (33), whereas a panel study reported decreased plasma glutathione concentrations among adults with asthma compared with healthy control subjects (7). In general, however, it is not clear why ascorbate-related oxidative potential would not have an impact similar to that of glutathione-related oxidative potential on the relationship between PM2.5 and acute respiratory morbidity. One possibility may be that dietary intake of ascorbate counteracts any minor depletion caused by inhaled pollutants. Alternatively, ascorbate depletion in the lung may have little impact on plasma ascorbate concentrations, which may ultimately be more important for health. Studies of source contributions to ascorbate depletion suggest that traffic-related components of PM2.5 are important determinants (e.g., elemental carbon, iron, copper, and polycyclic aromatic hydrocarbons), with OPAA values at continuous traffic sites exceeding values for “stop and go” locations (11). On the other hand, the brake and tire wear portion of traffic-related air pollution has been shown to be an important determinant of glutathione-related oxidative potential (13). Therefore, these two assays may provide important information with respect to the relative toxicities of different aspects of traffic-related air pollution (i.e., tail pipe emissions vs. brake and tire wear). If this is the case, our findings suggest that the contribution of break and tire wear from traffic may modify the impact of PM2.5 on respiratory health. However, more work is needed with respect to specific source contributions to the oxidative potential of PM2.5, and future studies should address this question.
In addition to PM2.5 oxidative potential, we also examined the combined oxidant capacity of ozone and nitrogen dioxide based on evidence suggesting that this measure may be a better predictor of daily nonaccidental mortality than either pollutant on its own (16). In single-pollutant models, the combined oxidant capacity of ozone and nitrogen dioxide was more strongly associated with emergency room visits for asthma than either pollutant on its own; however, ozone was more strongly associated with all three outcomes when PM2.5 was included in the models. In general, the combined oxidant capacity measure did not offer any distinct advantages over O3 in characterizing the acute respiratory health effects of gaseous pollutants in this study.
Although this study had a number of important advantages, including a large number of cases and detailed city-level oxidative potential data over a broad geographic area, it is important to note several limitations. First, because oxidative potential data were collected after the case ascertainment period, exposure measurement error likely impacted our assessment of between-city differences in PM2.5 oxidative potential. Similarly, the fact that city-level oxidative potential data were based on a relatively short time period (∼100 d) likely also contributed to this uncertainty. This measurement error likely increased uncertainty in effect estimates for oxidative burden analyses and contained elements of both classical (i.e., imprecise estimates of long-term oxidative potential) and Berkson type error (i.e., true daily oxidative potential values distributed around the measured long-term estimates of city-level oxidative potential). The overall impact of this error was likely a bias of effect estimates for oxidative burden toward the null. A second limitation is related to the fact that city-level estimates of PM2.5 oxidative potential were based on measurements collected from fixed-site monitors and thus our analyses do not account for spatial differences in oxidative potential within cities. Indeed, evidence suggests that spatial variations in PM2.5 oxidative potential are greater than for PM2.5 mass concentrations (34), and thus differences in measurement error may hinder direct comparisons of effect estimates for PM2.5 and PM2.5 oxidative burden. Moreover, we were not able to account for temporal (i.e., day-to-day) changes in PM2.5 oxidative potential, and future studies should aim to address these limitations as both spatial and temporal differences in PM2.5 oxidative potential may influence the acute respiratory health effects of fine particulate air pollution. Finally, it is important to note that our estimates of city-level oxidative potential likely underestimate overall oxidative potential owing to the loss of volatile components from TEOM filters. This likely also contributed to measurement error in assessing city-level oxidative potential, particularly in regions with a higher proportion of volatile components in PM2.5. In general, as multiple assays are available to characterize the oxidative potential of particulate air pollution (15), the use of any single method may not adequately describe the total oxidative potential of PM2.5 in a given region. Future studies should explore multiple assays in an effort to identify specific particle components and sources of oxidative stress most relevant to public health.
In summary, our findings suggest that between-city differences in glutathione-related oxidative potential may modify the impact of PM2.5 on emergency room visits for respiratory illnesses in low-pollution environments typical of much of North America. In general, short-term changes in PM2.5 mass concentrations may be most relevant to respiratory health in areas with high glutathione-related oxidative potential. This finding requires further replication as this study is the first to report such an association.
The authors thank Farhan Mansoor for conducting the oxidative burden analyses; Ronald Garson for producing the map in Figure 1; and Christie McMann, Tim Shin, and Hongyu You for help in compiling oxidative burden data. The authors thank Luc White (Environment Canada) and the Ontario Ministry of the Environment and Climate Change for help in collecting PM2.5 filters for oxidative burden analyses. The authors thank Barry Jessiman for helpful comments during manuscript preparation. This study was funded by Health Canada.
| 1. | Delfino RJ, Wu J, Tjoa T, Gullesserian SK, Nickerson B, Gillen DL. Asthma morbidity and ambient air pollution: effect modification by residential traffic–related air pollution. Epidemiology 2014;25:48–57. |
| 2. | Iskandar A, Andersen ZJ, Bønnelykke K, Ellermann T, Andersen KK, Bisgaard H. Coarse and fine particles but not ultrafine particles in urban air trigger hospital admission for asthma in children. Thorax 2012;67:252–257. |
| 3. | Atkison RW, Kang S, Anderson HR, Mills IC, Walton HA. Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and metaanalysis. Thorax 2014;69:660–665. |
| 4. | Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL, Samet JM. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 2006;295:1127–1134. |
| 5. | Weichenthal SA, Godri-Pollitt K, Villeneuve PJ. PM2.5, oxidant defence and cardiorespiratory health: a review. Environ Health 2013;12:40. |
| 6. | Li N, Hao M, Phalen RF, Hinds WC, Nel AE. Particulate air pollutants and asthma: a paradigm for the role of oxidative stress in PM-induced adverse health effects. Clin Immunol 2003;109:250–265. |
| 7. | Kelly FJ. Oxidative stress: its role in air pollution and adverse health effects. Occup Environ Med 2003;60:612–616. |
| 8. | Kelly FJ, Mudway I, Blomberg A, Frew A, Sandström T. Altered lung antioxidant status in patients with mild asthma. Lancet 1999;354:482–483. |
| 9. | Ben Anes A, Ben Nasr H, Fetoui H, Bchir S, Chahdoura H, Yacoub S, Garrouch A, Benzarti M, Tabka Z, Chahed K. Alteration in systemic markers of oxidative and antioxidative status in Tunisian patients with asthma: relationships with clinical severity and airflow limitation. J Asthma 2016;53:227–237. |
| 10. | Künzli N, Mudway IS, Götschi T, Shi T, Kelly FJ, Cook S, Burney P, Forsberg B, Gauderman JW, Hazenkamp ME, et al. Comparison of oxidative properties, light absorbance, total and elemental mass concentration of ambient PM2.5 collected at 20 European sites. Environ Health Perspect 2006;114:684–690. |
| 11. | Janssen NAH, Yang A, Strak M, Steenhof M, Hellack B, Gerlofs-Nijland ME, Kuhlbusch T, Kelly F, Harrison R, Brunekreef B, et al. Oxidative potential of particulate matter collected at sites with different source characteristics. Sci Total Environ 2014;472:572–581. |
| 12. | Yang A, Wang M, Eeftens M, Beelen R, Dons E, Leseman DL, Brunekreef B, Cassee FR, Janssen NA, Hoek G. Spatial variations and land use regression modeling of the oxidative potential of fine particles. Environ Health Perspect 2015;123:1187–1192. |
| 13. | Yanosky JD, Tonne CC, Beevers SD, Wilkinson P, Kelly FJ. Modeling exposures to the oxidative potential of PM10. Environ Sci Technol 2012;46:7612–7620. |
| 14. | Godri KJ, Duggan ST, Fuller GW, Baker T, Green D, Kelly FJ, Mudway IS. Particulate matter oxidative potential from waste transfer station activity. Environ Health Perspect 2010;118:493–498. |
| 15. | Ayres JG, Borm P, Cassee FR, Castranova V, Donaldson K, Ghio A, Harrison RM, Hider R, Kelly F, Kooter IM, et al. Evaluating the toxicity of airborne particulate matter and nanoparticles by measuring oxidative stress potential—a workshop report and consensus statement. Inhal Toxicol 2008;20:75–99. |
| 16. | Delfino RJ, Staimer N, Tjoa T, Gillen DL, Schauer JJ, Shafer MM. Airway inflammation and oxidative potential of air pollutant particles in a pediatric asthma panel. J Expo Sci Environ Epidemiol 2013;23:466–473. |
| 17. | Yang A, Janssen NAH, Brunekreef B, Cassee FR, Hoek G, Gehring U. Children’s respiratory health and oxidative potential of PM2.5: the PIAMA birth cohort study. Occup Environ Med 2016;73:154–160. |
| 18. | Weichenthal S, Lavigne E, Evans G, Pollitt K, Burnett RT. Ambient PM2.5 and risk of emergency room visits for myocardial infarction: impact of regional PM2.5 oxidative potential: a case-crossover study. Environ Health 2016;15:46. |
| 19. | Janes H, Sheppard L, Lumley T. Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias. Epidemiology 2005;16:717–726. |
| 20. | Gibson D, Richards H, Chapman A. The national ambulatory care reporting system: factors that affect the quality of its emergency data. Int J Information Quality 2008;2:97–114. |
| 21. | Williams ML, Atkinson RW, Anderson HR, Kelly FJ. Associations between daily mortality in London and combined oxidant capacity, ozone and nitrogen dioxide. Air Qual Atmos Health 2014;7:407–414. |
| 22. | Bratsch SG. Standard electrode potentials and temperature coefficients in water at 298.15 K. J Phys Chem Ref Data 1989;18:1–21. Available from: http://www.nist.gov/data/PDFfiles/jpcrd355.pdf |
| 23. | Zheng XY, Ding H, Jiang LN, Chen SW, Zheng JP, Qiu M, Zhou YX, Chen Q, Guan WJ. Association between air pollutants and asthma emergency room visits and hospital admissions in time series studies: a systematic review and meta-analysis. PLoS One 2015;10:e0138146. |
| 24. | Halonen JI, Lanki T, Tiittanen P, Niemi JV, Loh M, Pekkanen J. Ozone and cause-specific cardiorespiratory morbidity and mortality. J Epidemiol Community Health 2010;64:814–820. |
| 25. | Pastore A, Federici G, Bertini E, Piemonte F. Analysis of glutathione: implication in redox and detoxification. Clin Chim Acta 2003;333:19–39. |
| 26. | Rahman I, MacNee W. Lung glutathione and oxidative stress: implications in cigarette smoke–induced airway disease. Am J Physiol 1999;277:L1067–L1088. |
| 27. | Ballatori N, Krance SM, Notenboom S, Shi S, Tieu K, Hammond CL. Glutathione dysregulation and the etiology and progression of human diseases. Biol Chem 2009;390:191–214. |
| 28. | Rahman I, MacNee W. Oxidative stress and regulation of glutathione in lung inflammation. Eur Respir J 2000;16:534–554. |
| 29. | Bentley AR, Emrani P, Cassano PA. Genetic variation and gene expression in antioxidant related enzymes and risk of COPD: a systematic review. Thorax 2008;63:956–961. |
| 30. | Kabesch M, Hoefler C, Carr D, Leupold W, Weiland SK, von Mutius E. Glutathione S transferase deficiency and passive smoking increase childhood asthma. Thorax 2004;59:569–573. |
| 31. | Valko M, Leibfritz D, Moncol J, Cronin MTD, Mazur M, Telser J. Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol 2007;39:44–84. |
| 32. | Nicks ME, O’Brien MM, Bowler RP. Plasma antioxidants are associated with impaired lung function and COPD exacerbations in smokers. COPD 2011;8:264–269. |
| 33. | Schock BC, Young IS, Brown V, Fitch PS, Shields MD, Ennis M. Antioxidants and oxidative stress in BAL fluid of atopic asthmatic children. Pediatr Res 2003;53:375–381. |
| 34. | Yang A, Hellack B, Leseman D, Brunekreef B, Kuhlbusch TAJ, Cassee FR, Hoek G, Janssen NAH. Temporal and spatial variation of the metal-related oxidative potential of PM2.5 and its relation to PM2.5 mass and elemental composition. Atmos Environ 2015;102:62–69. |
Supported by Health Canada.
Author Contributions: S.A.W., E.L., and R.T.B. designed the study, conducted/guided statistical analyses, and contributed to writing the manuscript. G.J.E. and K.J.G.P. conducted/supervised laboratory analyses related to PM2.5 oxidative potential and contributed to writing the manuscript. All authors provided final approval of the version submitted for publication.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org
Originally Published in Press as DOI: 10.1164/rccm.201512-2434OC on March 10, 2016
Author disclosures are available with the text of this article at www.atsjournals.org.
