Outdoor fine particulate air pollution (PM2.5) is a complex mixture of organic and inorganic components that contribute to the generation of reactive oxygen species and oxidative stress, which are thought to be important mechanisms underlying air pollution health effects.1,2 Although PM2.5 mass concentrations are recognized as an important contributor to the overall global burden of disease,3 it is not clear how coexposure to specific PM2.5 components may modify the long-term health effects of other common outdoor air pollutants such as oxidant gases (i.e., O3 and NO2). This is an important concept as populations are simultaneously exposed to both PM2.5 and oxidant gases, and we must understand how these air pollutants interact to affect public health to adequately inform future regulatory interventions.

Specifically, it seems possible that associations between long-term exposure to oxidant gases and mortality risk may be stronger in regions where PM2.5 composition has a greater capacity to cause oxidative stress (e.g., greater transition metal content and oxidative potential). This hypothesis is supported by existing evidence suggesting that outdoor PM2.5 with higher oxidative potential is more strongly associated with adverse health outcomes.1,2,4–8 However, this paradigm should not only apply to outdoor PM2.5, but all air pollutants capable of contributing to oxidative damage in human systems. Therefore, if oxidant gases are harmful to human health primarily through their ability to cause oxidative stress, the health impacts of oxidant gases may be greater in populations coexposed to PM2.5 that is more capable of causing oxidative stress because the combined oxidative impacts of PM2.5 and oxidant gases may be greater in these locations. To our knowledge, no cohort studies to date have examined this question, but a recent systematic review highlighted substantial heterogeneity in existing studies of long-term exposure to oxidant gases and mortality.9 One explanation for this heterogeneity may be that the long-term health impacts of oxidant gases depend in part on the composition/characteristics of outdoor PM2.5 that is also present in the environment.

In this study, our aim was to determine if associations between long-term exposure to oxidant gases (Ox, expressed as a redox-weighted average of O3 and NO2) and nonaccidental, cardiovascular, and respiratory mortality are stronger in locations where PM2.5 composition is more likely to contribute oxidative stress (i.e., with higher transition metal content and oxidative potential). To do this, we examined associations between long-term Ox exposures and mortality across strata defined by the content (i.e., mass proportions) of specific transition metals in outdoor PM2.5 as well as three different measures of PM2.5 oxidative potential using a database of ground-level measurements collected at 40 locations across Canada.

METHODS Study Population and Mortality Outcomes

Our cohort study population included members of multiple cycles (2001, 2006, 2011) of the Canadian Census Health and Environment Cohort (CanCHEC). Each CanCHEC is made up of adults (>25 years of age at enrollment) who completed the long-form census questionnaire capturing individual and household sociodemographic data, which were subsequently linked to vital statistics and tax records (for annual residential location).10 Ox-mortality analyses were limited to cohort members residing within a 10-km radius of a monitoring site for PM2.5 components/oxidative potential (described later). Individuals who were enumerated in more than one long-form census cycle were assigned to the cohort in which they first appeared. All participants were followed for mortality from census day (in either 2001, 2006, or 2011) until December 31, 2016. Our analyses focused on nonaccidental mortality (International Classification of Diseases, 10th revision codes (ICD-10): A00-R99) along with several specific causes of mortality including cardiovascular disease (ICD-10 codes: I10 to I69) and nonmalignant respiratory disease (ICD-10 codes: J00-J99). The CanCHEC database was created under the authority of the Statistics Act and approved by the Executive Management Board (reference number: 045-2015) at Statistics Canada. This is equivalent to standard research ethics board approval. Compared with the national CanCHEC cohorts, our analytical cohort had a slightly lower proportion (based on percentage of person-time) of younger subjects (14% of person-time among subjects 25–34 years of age compared with 18% in the full cohort) and a higher proportion of older subjects (26% of person-time among subjects 65–89 years of age compared with 19% in the full cohort) but was similar in terms of sex (52% female; 48% male), education (e.g., 18% with less than high school and 29% with a university degree), and employment status (63% employed).

Outdoor Oxidant Gas and PM2.5 Concentrations

We did not examine models for O3 or NO2 separately as our focus was on the combined oxidant capacity of these gases (i.e., Ox) and how associations for Ox may be influenced by coexposure to outdoor PM2.5 with varying abilities to cause oxidative stress. This is particularly important for NO2 which often serves as a marker for a broad mix of combustion pollutants (e.g., traffic-related air pollution), which could have much different health impacts than NO2 the molecule. Since we were interested in the combined oxidant capacity of the NO2 and O3 molecules themselves, we combined these exposures into a single redox-weighted average (i.e., a weighted average based on their redox potential) based on the following equation: Ox = ((1.07 × NO2) + (2.075 × O3))/ 3.145.11,12

Ozone data reflected the daily maximum of 8-hour average concentrations based on chemical transport modeling of surface observations in the warm season between 2002 and 2015 (i.e., the average of maximum values within the same 8-hour period each day during the warm season).13 Hourly O3 model output was fused with ground monitor data14,15; the spatial resolution of the O3 model was 21 km2 before 2009 and 10 km2 in subsequent years of follow-up. NO2 data were obtained from a national land use regression model for the year 2006, using 10 km2 gridded remote sensing-derived NO2 estimates and highly resolved land use data to produce a model with a resolution of 100 m2.16 We applied spatiotemporal adjustments to annual estimates of O3 and NO2 by first developing an annual time-series of both pollutants in Canada’s 24 largest cities, based on ground monitoring data from 1981 to 2016. We then estimated yearly adjustment factors equal to the ratio of the observed concentration in the desired year to the average concentration in the reference year(s) (i.e., 2006 for NO2 and the mean of 2002–2015 for O3). We scaled the concentration estimates over the follow-up period using the annual adjustment factors based on the nearest city to that postal code location. Annual average outdoor NO2 and O3 concentrations were first assigned to the centroids of residential 6-digit postal codes for each cohort member and annual average Ox concentrations were then calculated using the formula above.

Annual average outdoor PM2.5 mass concentrations (µg/m3) were estimated using a model combining satellite retrievals of aerosol optical depth (AOD) at 1 km2 resolution with simulations of the AOD-to-PM2.5 relationship using GEOS-Chem (a chemical transport model).17

Ground-level Measurements of PM2.5 Transition Metals and Oxidative Potential

Integrated PM2.5 samples were collected on Teflon filters (using cascade impactors at a flow rate of 5 L/min) for 2 weeks each month at 40 monitoring sites across Canada between 2016 and 2018.7 Monthly samples were analyzed for OP and components and results were averaged over the 2-year monitoring period to estimate long-term average values for each site. The median number of monthly samples pooled within each site to estimate long-term average values for OP and transition metals was 27 (range: 15–41). Monitoring sites included the following locations across Canada (eFigure S1; https://links.lww.com/EDE/B958): Alberta: Anzac, Athabasca Valley, Calgary, Edmonton (3 sites), Fort Mackay, Fort McMurray, Red Deer, and St Albert; British Columbia: Courtenay, Duncan, Kamloops, Kelowna, Nanaimo, Prince George, Quesnel, Trail, and Victoria; Manitoba: Brandon, Flin Flon, and Winnipeg; New Brunswick: Fredericton and Saint John; Newfoundland and Labrador: Mt. Pearl and St. John’s; Nova Scotia: Halifax; North West Territories: Yellowknife; Ontario: Hamilton, London, Ottawa (2 sites), and Windsor; Quebec: Montreal and Quebec City; Saskatchewan: Prince Albert, Regina, Saskatoon, and Swift Current; Yukon: Whitehorse. In most cities, monthly PM2.5 samplers were located at provincial monitoring sites except for Ottawa and Montreal where monitors were located outside private residences.

Monthly PM2.5 filters were analyzed for sulfur and transition metal content using x-ray fluorescence (United States Environmental Protection Agency Method IO-3.3) and oxidative potential as described below. The following transition metals were selected a priori for inclusion in our analyses based on previous evidence suggesting an association with particle OP: Cu, Fe, Zn, Ni, Mn.1,2,18,19 Sulfur was included in our PM2.5 component analyses as we recently reported that the acute cardiovascular health impacts of PM2.5 mass concentrations were stronger in men when both S and transition metals were elevated (S makes the particles more acidic and is thought to make the metals more biologically available).8,20 Therefore, we wanted to examine how combinations of these components may modify the health impacts of Ox.

For oxidative potential analyses, PM2.5 samples were first extracted into HPLC grade methanol by vortexing at 1,800 revolutions per minute for 20 minutes and sonicating for 10 minutes. Decanted methanol was evaporated under a gentle flow of nitrogen. PM samples were resuspended in ultrapure water containing 5% HPLC methanol to a final storage concentration of 200 μg PM/mL. Resuspended PM2.5 samples were analyzed in triplicate using three OP metrics: the ascorbate (AA), glutathione (GSH), and dithiothreitol (DTT) assays. Ascorbate and glutathione oxidative potential (OPAA and OPGSH) were assessed using the acellular respiratory tract lining fluid (RTLF) OP assay.21 Briefly, PM2.5 samples were incubated at a concentration of 75 μg/mL in a 96 well plate for 4-hours at 37°C with synthetic respiratory tract lining fluid (RTLF) containing 200 μM of each AA, GSH, and uric acid in an ultraviolet—visible plate reader (Molecular Devices, Spectra Max 190) alongside positive controls (0.5 μM Cu(NO3)2, 0.02% H2O2) and blanks. Ascorbate depletion was calculated over the 4-hour incubation period, and GSH depletion was measured using the glutathione-reductase enzyme recycling assay.22–25

Dithiothreitol oxidative potential (OPDTT) was assessed using an adapted version of the DDT assay. Briefly, resuspended PM2.5 samples were incubated with 100 μM DTT in a 96 well plate alongside positive controls (0.5 μM Cu(NO3)2), blanks, and DTT standards (containing 0−100 μM DTT) for 35 minutes at 37°C, with constant shaking. After 5, 15, 25, and 35 minutes, the remaining DTT was measured by adding 1.0 mM 5,5′-dithiobis(2-nitrobenzoic acid) (DTNB) to each well and measuring absorbance at 412 nm. Samples were initially analyzed at a concentration of 50 μg/mL; however, if DTT depletion exceeded 25% after 35 minutes, the sample was reanalyzed at a lower concentration. All OP values are expressed in units of pmol/min/μg; values below detection were replaced with half the limit of detection.

Statistical Analysis

Cox proportional hazard models were used to estimate associations between outdoor Ox concentrations and mortality outcomes across strata defined by ground-level measurements of oxidative potential (OPGSH, OPAA, and OPDTT) (pmol/min/μg) and mass proportions of transition metals (i.e., Cu, Fe, Zn, Ni, Mn) and sulfur (S) in PM2.5. Strata for OP, metals, and sulfur were based on median values observed across the 40 monitoring sites (eFigure S1; https://links.lww.com/EDE/B958). Median values were used to define strata of OP/components for three reasons. First, a smaller number of strata were needed to ensure sufficient power to detect possible effect modification across categories of OP/components. Second, since OP/component data were collected at the end of the follow-up period, we thought that error in classifying locations as above/below median values would be less than for classifications across 3 or more categories. Finally, random error across levels of a dichotomous effect modifier will tend to diminish the observed effect modification26; thus, we felt this decision was conservative in that we would not overestimate possible differences across strata (assuming misclassification was independent of Ox). Models were first examined across strata of each component/characteristic separately, followed by models examining strata with both high sulfur and high metals (i.e., both above the median) or both low sulfur and low metals (i.e., both below the median). Intermediate categories (e.g., high S/low metals) were not examined as far fewer cases (~80% fewer cases) were available for these strata.

All Cox proportional hazards models were stratified by age (5-year groups), sex, immigrant status, and census cycle, and included covariates for 3-year moving average outdoor PM2.5 mass concentration with a 1-year lag (i.e., the same as for Ox), individual-level income, educational attainment, marital status, Indigenous identity, employment status, occupational class, and visible minority status. In addition, we included neighborhood-level variables for four dimension of the Canadian Marginalization Index (CAN-Marg) which describes inequalities in terms of material deprivation, residential instability, dependency, and ethnic concentration.27 Individual-level data on smoking and body mass index (BMI) are not available in CanCHEC; however, we did evaluate correlations between outdoor Ox concentrations and smoking (6 levels: never, former-occasional, former-daily, occasional, occasional-former daily, daily) and BMI (continuous) in the Canadian Community Health Survey (CCHS) cohort population over the same time-period (CCHS is an ancillary population-based cohort with individual-level data on smoking and BMI). In the CCHS cohort, weak inverse correlations were observed between outdoor Ox concentrations and both smoking (r = −0.045) and BMI (r = −0.038); thus, suggesting that any residual confounding by these factors in the CanCHEC cohort would tend to underestimate the magnitude of associations for Ox.

All hazard ratios for outdoor Ox are expressed per interquartile change in outdoor concentration (6.27 ppb); interaction p-values were estimated by including an interaction term between Ox and indicator variables for strata. Ox exposures were assigned as 3-year moving average with a 1-year lag (to ensure that exposure preceded the outcome); exposures were updated for residential mobility and subjects were censored if they moved to a location more than 10-km from one of our monitoring sites. We did not examine PM2.5-mortality associations across strata of components/OP as long-term average outdoor PM2.5 mass concentrations did not vary meaningfully across our study sites (IQR: 6.37–9.77 µg/m3).

RESULTS

In total, our study population included 2 million adults with 153,800 nonaccidental deaths, 44,200 cardiovascular disease deaths, and 13,700 respiratory disease deaths occurring during the follow-up period (Table 1). The median Ox concentration was 28.75 ppb (5th = 20.41 ppb; 95th = 36.58 ppb) and Ox distributions were similar within strata of PM2.5 OP and components (eTable S1; https://links.lww.com/EDE/B958). Long-term estimates of mass proportions of transition metals and sulfur in PM2.5 varied substantially across study locations (eTable S2; https://links.lww.com/EDE/B958). Spearman correlations between PM2.5 OP and PM2.5 components are shown in eFigure S2; https://links.lww.com/EDE/B958. OPDTT was weakly correlated with transition metals in PM2.5 (−0.15 < r < 0.10) as well as OPAA and OPGSH (−0.02 < r < 0.17). OPGSH and OPAA were moderately correlated with each other (r = 0.67) and with Cu (0.68 < r < 0.72). Outdoor Ox concentrations were moderately correlated with outdoor PM2.5 mass concentrations (r = 0.62). Correlations between outdoor Ox concentrations and long-term estimates of PM2.5 OP and mass proportions of PM2.5 components across study sites were as follows: OPGSH (r = 0.28), OPAA (r = 0.43), OPDTT (r = 0.17), Cu (r = 0.26), S (r = 0.60), Fe (r = 0.35), Zn (r = 0.13), Mn (r = 0.24), Ni(r = −0.13).

TABLE 1. - Descriptive Cohort Data (2001, 2006, 2011 CanCHEC cohorts) for Participants Living Within 10-km of a Ground-level Monitoring Location for PM2.5 Oxidative Potential and Components Characteristic Person-years Participants Mortality Outcomes Nonaccidental Cardiovascular Respiratory All 15,807,300 2,001,600 153,800 44,200 13,700 Sex  Male 8,382,400 1,047,500 71,300 19,200 6,300  Female 7,424,900 954,200 82,500 25,000 7,400 Age  25–29 504,500 196,000 100 NA NA  30–39 2,614,700 585,300 1,200 200 NA  40–49 3,511,300 734,700 4,700 1,100 200  50–59 3,608,000 730,800 14,000 3,200 700  60–69 2,677,100 556,300 26,100 6,000 1,700  70–79 1,822,000 380,500 44,400 12,000 4,200  80–89 1,069,700 235,600 63,400 21,600 6,800 Education  Not completed high school 2,955,000 365,600 64,300 19,400 6,600  High school with/without trades certificate 4,538,600 555,300 45,300 12,900 3,800  Postsecondary nonuniversity 3,947,600 506,500 25,900 7,000 2,100  University degree 4,366,100 574,200 18,300 4,800 1,100 Employment status  Employed 10,263,500 1,271,700 31,500 7,500 1,600  Unemployed 587,300 79,000 2,700 700 200  Not in labor force 4,956,500 651,000 119,600 36,000 11,900 Income deciles  1st decile—highest 1,574,900 442,900 15,800 4,500 1,600  2nd decile 1,550,500 526,700 25,700 7,700 2,700  3rd decile 1,472,700 570,300 18,400 5,400 1,800  4th decile 1,430,100 588,300 14,800 4,200 1,400  5th decile 1,387,600 593,300 12,000 3,400 1,100  6th decile 1,348,800 583,500 9,700 2,700 700  7th decile 1,318,500 561,200 7,800 2,100 600  8th decile 1,307,100 523,100 6,400 1,700 400  9th decile 1,311,400 461,400 5,300 1,400 300  10th decile—lowest 1,442,300 353,700 5,300 1,300 300  Not applicable 1,663,500 128,300 32,600 9,800 2,800 Occupational level  Management 1,281,900 156,800 4,100 1,000 200  Professional 2,541,100 306,200 6,300 1,400 300  Skilled, technical, and supervisory 3,332,900 422,300 11,200 2,700 600  Semiskilled 3,119,700 387,100 11,600 2,800 600  Unskilled 1,134,100 148,100 5,200 1,400 400  No occupation 4,397,500 581,200 115,500 35,000 11,600 Visible minority  Not defined as visible minority 13,369,200 1,678,300 142,400 40,900 12,800  Visible minority 2,438,100 323,400 11,400 3,200 900 Immigration status  Nonimmigrant 12,035,100 1,531,700 119,000 33,600 11,000  Immigrant 3,772,200 470,000 34,800 10,600 2,600 Marital status  Single, never married 2,716,700 364,900 16,400 4,800 1,300  Common-law 1,647,400 223,200 6,700 1,600 500  Married 8,965,400 1,080,100 82,200 22,700 6,800  Separated 451,500 59,100 4,200 1,100 400  Divorced 1,200,700 154,200 14,700 4,100 1,400  Widowed 825,600 120,100 29,600 9,700 3,200 Can-Marg: Residential instability  Q1—lowest 1,487,900 231,200 9,200 2,600 800  Q2 2,596,800 415,800 18,900 5,300 1,600  Q3 3,230,400 522,100 29,900 8,600 2,500  Q4 3,835,700 621,500 41,100 11,900 3,700  Q5—highest 4,641,200 756,300 54,700 15,700 5,000 Can-Marg: Dependency  Q1—lowest 3,641,500 596,100 24,200 6,800 2,100  Q2 2,909,900 503,400 24,800 7,100 2,200  Q3 2,134,700 399,300 20,800 6,000 1,900  Q4 3,199,800 550,000 33,600 9,600 3,000  Q5—highest 3,906,000 625,300 50,300 14,600 4,400 Can-Marg: Material deprivation  Q1—lowest 3,702,900 569,000 26,400 7,400 2,200  Q2 2,515,400 442,300 23,500 6,900 2,000  Q3 2,757,200 485,200 28,600 8,300 2,500  Q4 3,029,300 532,200 32,400 9,500 3,000  Q5—high