Multi-pollutant surface objective analyses and mapping of air quality health index over North America

Does ambient air pollution influence biochemical markers of liver injury? Findings of a cross-sectional population-based survey

BACKGROUND

There is limited evidence supporting an adverse effect of ambient air pollution on the liver.

OBJECTIVES

To test the association between exposure to residential air pollution and serum biochemical indicators of liver injury.

METHODS

We used a nationally representative sample of 32,989 participants aged 3-79 years old who participated in the Canadian Health Measures Survey between 2007 and 2019. Cross-sectional associations were assessed by generalized linear mixed models incorporating survey-specific sampling weights.

RESULTS

The joint effect of an interquartile range (IQR) increase in nitrogen dioxide (NO2), ozone (O3) and fine particulate matter (PM2.5) was positively and significantly associated with all measures of liver injury adjusting for age, sex, education, income, smoking, alcohol consumption, body mass index (BMI), total cholesterol, diabetes, hypertension, and physical activity. The ranking of effect sizes from largest to smallest percent increases were 8.72% (95% confidence interval [CI] 7.56, 9.88) for alanine aminotransferase (ALT), 5.54% (95%CI 3.31, 7.77) for gamma-glutamyl transferase (GGT), 4.81% (95%CI 3.87, 5.74) for aspartate aminotransferase (AST), 2.46% (95%CI 0.26, 4.65) for total bilirubin (TBIL) and 1.18% (95%CI 0.62, 1.75) for alkaline phosphatase (ALP). Findings were not significantly different when stratified by age (≤16, >16 yr), sex, smoking (current, other), cholesterol (≤6.18, >6.18 mmol/l) and BMI (<30, ≥30 kg/m2).

DISCUSSION

These findings suggest that ambient air pollution may have a relatively small impact on the liver, but these changes may have significant impact from a population health perspective, considering the ubiquitous nature of air pollution, or for individuals exposed to very high levels of air pollution.

Characteristics of ambient air quality and its air quality index (AQI) model in Shanghai, China

Long-term observations indicate that, the ambient air quality in Shanghai continues to improve, however the synergistic effects between the air pollutants PM2.5, O3 and NO2 are also increasing. The concentration of chemical components included in PM2.5 is higher in moderately polluted air containing multiple pollutants. This suggests that air pollution metrics based on multi-pollutant synergy are more descriptive of ambient air quality than single-pollutant air quality index (AQI) models that may ignore the effect of synergy between pollutants on ambient air quality forecasts. Therefore, this study proposes a new multi-pollutant air quality index model (NMAQI) based on four air pollutants (PM2.5, SO2, NO2 and O3) that emphasizes the relationship between PM2.5, NO2 and O3 in ambient air. The model successfully categorized observational data into classes of good, moderate, and polluted air quality ratings. Verification of the NMAQI model using the PM2.5 chemical composition spectrum shows that the NMAQI model can more accurately classify samples with high concentrations of chemical components (often misclassified by AQI) into high pollution levels. The model has an improved capacity to assess the degree of pollution in urban ambient air and to reduce the risk of public exposure to highly polluted atmospheric environments.

Air quality health index (AQHI) based on multiple air pollutants and mortality risks in Taiwan: Construction and validation

The currently used air quality index (AQI) is not able to capture the additive effects of air pollution on health risks and reflect non-threshold concentration-response relationships, which has been criticized. We proposed the air quality health index (AQHI) based on daily air pollution-mortality associations, and compared its validity in predicting daily mortality and morbidity risks with the existing AQI. We examined the excess risk (ER) of daily elderly (≥65-year-old) mortality associated with 6 air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) in 72 townships across Taiwan from 2006 to 2014 by performing a time-series analysis using a Poisson regression model. Random effect meta-analysis was used to pool the township-specified ER for each air pollutant in the overall and seasonal scenarios. The integrated ERs for mortality were calculated and used to construct the AQHI. The association of the AQHI with daily mortality and morbidity were compared by calculating the percentage change per interquartile range (IQR) increase in the indices. The magnitude of the ER on the concentration-response curve was used to evaluate the performance of the AQHI and AQI, regarding specific health outcomes. Sensitivity analysis was conducted using coefficients from the single- and two-pollutant models. The coefficients of PM2.5, NO2, SO2, and O3 associated with mortality were included to form the overall and season-specific AQHI. An IQR increase in the overall AQHI at lag 0 was associated with 1.90%, 2.96%, and 2.68% increases in mortality, asthma, and respiratory outpatient visits, respectively. The AQHI had higher ERs for mortality and morbidity on the validity examinations than the current AQI. The AQHI, which captures the combined effects of air pollution, can serve as a health risk communication tool to the public.

Within-city spatial variations in long-term average outdoor oxidant gas concentrations and cardiovascular mortality: Effect modification by oxidative potential in the Canadian Census Health and Environment Cohort

Health effects of oxidant gases may be enhanced by components of particulate air pollution that contribute to oxidative stress. Our aim was to examine if within-city spatial variations in the oxidative potential of outdoor fine particulate air pollution (PM2.5) modify relationships between oxidant gases and cardiovascular mortality.

Methods

We conducted a retrospective cohort study of participants in the Canadian Census Health and Environment Cohort who lived in Toronto or Montreal, Canada, from 2002 to 2015. Cox proportional hazards models were used to estimate associations between outdoor concentrations of oxidant gases (Ox, a redox-weighted average of nitrogen dioxide and ozone) and cardiovascular deaths. Analyses were performed across strata of two measures of PM2.5 oxidative potential and reactive oxygen species concentrations (ROS) adjusting for relevant confounding factors.

Results

PM2.5 mass concentration showed little within-city variability, but PM2.5 oxidative potential and ROS were more variable. Spatial variations in outdoor Ox were associated with an increased risk of cardiovascular mortality [HR per 5 ppb = 1.028, 95% confidence interval (CI): 1.001, 1.055]. The effect of Ox on cardiovascular mortality was stronger above the median of each measure of PM2.5 oxidative potential and ROS (e.g., above the median of glutathione-based oxidative potential: HR = 1.045, 95% CI: 1.009, 1.081; below median: HR = 1.000, 95% CI: 0.960, 1.043).

Conclusion

Within-city spatial variations in PM2.5 oxidative potential may modify long-term cardiovascular health impacts of Ox. Regions with elevated Ox and PM2.5 oxidative potential may be priority areas for interventions to decrease the population health impacts of outdoor air pollution.

Spatial cluster mapping and environmental modeling in pediatric inflammatory bowel disease

BACKGROUND

Geographical (geospatial) clusters have been observed in inflammatory bowel disease (IBD) incidence and linked to environmental determinants of disease, but pediatric spatial patterns in North America are unknown. We hypothesized that we would identify geospatial clusters in the pediatric IBD (PIBD) population of British Columbia (BC), Canada and associate incidence with ethnicity and environmental exposures.

AIM

To identify PIBD clusters and model how spatial patterns are associated with population ethnicity and environmental exposures.

METHODS

One thousand one hundred eighty-three patients were included from a BC Children’s Hospital clinical registry who met the criteria of diagnosis with IBD ≤ age 16.9 from 2001–2016 with a valid postal code on file. A spatial cluster detection routine was used to identify areas with similar incidence. An ecological analysis employed Poisson rate models of IBD, Crohn’s disease (CD), and ulcerative colitis (UC) cases as functions of areal population ethnicity, rurality, average family size and income, average population exposure to green space, air pollution, and vitamin-D weighted ultraviolet light from the Canadian Environmental Health Research Consortium, and pesticide applications.

RESULTS

Hot spots (high incidence) were identified in Metro Vancouver (IBD, CD, UC), southern Okanagan regions (IBD, CD), and Vancouver Island (CD). Cold spots (low incidence) were identified in Southeastern BC (IBD, CD, UC), Northern BC (IBD, CD), and on BC’s coast (UC). No high incidence hot spots were detected in the densest urban areas. Modeling results were represented as incidence rate ratios (IRR) with 95%CI. Novel risk factors for PIBD included fine particulate matter (PM_2.5) pollution (IRR = 1.294, CI = 1.113-1.507, P < 0.001) and agricultural application of petroleum oil to orchards and grapes (IRR = 1.135, CI = 1.007-1.270, P = 0.033). South Asian population (IRR = 1.020, CI = 1.011-1.028, P < 0.001) was a risk factor and Indigenous population (IRR = 0.956, CI = 0.941-0.971, P < 0.001), family size (IRR = 0.467, CI = 0.268-0.816, P = 0.007), and summer ultraviolet (IBD = 0.9993, CI = 0.9990–0.9996, P < 0.001) were protective factors as previously established. Novel risk factors for CD, as for PIBD, included: PM_2.5 air pollution (IRR = 1.230, CI = 1 .056-1.435, P = 0.008) and agricultural petroleum oil (IRR = 1.159, CI = 1.002-1.326, P = 0.038). Indigenous population (IRR = 0.923, CI = 0.895–0.951, P < 0.001), as previously established, was a protective factor. For UC, rural population (UC IRR = 0.990, CI = 0.983-0.996, P = 0.004) was a protective factor and South Asian population (IRR = 1.054, CI = 1.030–1.079, P < 0.001) a risk factor as previously established.

CONCLUSION

PIBD spatial clusters were identified and associated with known and novel environmental determinants. The identification of agricultural pesticides and PM_2.5 air pollution needs further study to validate these observations.

Air pollution and the onset of balance problems: The Canadian longitudinal study on aging

PURPOSE

To understand the relationship between ambient air pollution and the onset of balance problems.

DESIGN

Population-based prospective cohort study.

METHODS

Baseline and 3-year follow-up data were used from the Canadian Longitudinal Study on Aging. The Comprehensive Cohort included adults aged 45-85 years old recruited from 11 sites across 7 provinces. Data on air pollution came from the Canadian Urban Environmental Health Research Consortium. Annual mean levels of ozone, fine particulate matter (PM_2.5), and sulfur dioxide for each participant's postal code were estimated from satellite data. Balance was measured at both time points using the one-leg balance test with those who could not stand on one leg for at least 60 s defined as failing the balance test. Our outcome was the new development of failing the balance test at follow-up in those who passed the balance test at baseline. Logistic regression was used.

RESULTS

Of the 12,158 people who could stand for 60 s on one leg at baseline, 18% were unable to do so 3 years later. In single pollutant models, living in an area with higher ozone levels was associated with the 3-year onset of balance problems (odds ratio (OR) = 1.13 per interquartile range of ozone, 95% CI 1.02, 1.24) after adjustment for demographic, lifestyle, and health variables. In a multipollutant model, the association with ozone increased slightly (OR = 1.16, 95% CI 1.04, 1.30). There were no associations with PM_2.5 or sulfur dioxide.

CONCLUSION

Our findings provide longitudinal evidence that higher ozone levels are associated with the odds of developing balance problems over a 3-year period. Further work should attempt to confirm our findings and explore the potential mechanism of action.

Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM 2.5 Transition Metals and Oxidative Potential

BACKGROUND

Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O 3 and NO 2 ) and fine particulate air pollution (PM 2.5 ). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM 2.5 is more capable of causing oxidative stress.

METHODS

We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM 2.5 components and oxidative potential. O x exposures (i.e., the redox-weighted average of O 3 and NO 2 ) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average O x and mortality outcomes across strata of transition metals and sulfur in PM 2.5 and three measures of PM 2.5 oxidative potential adjusting for possible confounding factors.

RESULTS

Associations between O x and mortality were consistently stronger in regions with elevated PM 2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in O x was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OP GSH ) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OP GSH levels below the median (interaction P value <0.001).

CONCLUSION

Spatial variations in PM 2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.

Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE): Phase 2

INTRODUCTION

Mortality is associated with long-term exposure to fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter; PM2.5), although the magnitude and form of these associations remain poorly understood at lower concentrations. Knowledge gaps include the shape of concentration-response curves and the lowest levels of exposure at which increased risks are evident and the occurrence and extent of associations with specific causes of death. Here, we applied improved estimates of exposure to ambient PM2.5 to national population-based cohorts in Canada, including a stacked cohort of 7.1 million people who responded to census year 1991, 1996, or 2001. The characterization of the shape of the concentration-response relationship for nonaccidental mortality and several specific causes of death at low levels of exposure was the focus of the Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE) Phase 1 report. In the Phase 1 report we reported that associations between outdoor PM2.5 concentrations and nonaccidental mortality were attenuated with the addition of ozone (O3) or a measure of gaseous pollutant oxidant capacity (Ox), which was estimated from O3 and nitrogen dioxide (NO2) concentrations. This was motivated by our interests in understanding both the effects air pollutant mixtures may have on mortality and also the role of O3 as a copollutant that shares common sources and precursor emissions with those of PM2.5. In this Phase 2 report, we further explore the sensitivity of these associations with O3 and Ox, evaluate sensitivity to other factors, such as regional variation, and present ambient PM2.5 concentration-response relationships for specific causes of death.

METHODS

PM2.5 concentrations were estimated at 1 km2 spatial resolution across North America using remote sensing of aerosol optical depth (AOD) combined with chemical transport model (GEOS-Chem) simulations of the AOD:surface PM2.5 mass concentration relationship, land use information, and ground monitoring. These estimates were informed and further refined with collocated measurements of PM2.5 and AOD, including targeted measurements in areas of low PM2.5 concentrations collected at five locations across Canada. Ground measurements of PM2.5 and total suspended particulate matter (TSP) mass concentrations from 1981 to 1999 were used to backcast remote-sensing-based estimates over that same time period, resulting in modeled annual surfaces from 1981 to 2016.

Annual exposures to PM2.5 were then estimated for subjects in several national population-based Canadian cohorts using residential histories derived from annual postal code entries in income tax files. These cohorts included three census-based cohorts: the 1991 Canadian Census Health and Environment Cohort (CanCHEC; 2.5 million respondents), the 1996 CanCHEC (3 million respondents), the 2001 CanCHEC (3 million respondents), and a Stacked CanCHEC where duplicate records of respondents were excluded (Stacked CanCHEC; 7.1 million respondents). The Canadian Community Health Survey (CCHS) mortality cohort (mCCHS), derived from several pooled cycles of the CCHS (540,900 respondents), included additional individual information about health behaviors. Follow-up periods were completed to the end of 2016 for all cohorts. Cox proportional hazard ratios (HRs) were estimated for nonaccidental and other major causes of death using a 10-year moving average exposure and 1-year lag. All models were stratified by age, sex, immigrant status, and where appropriate, census year or survey cycle. Models were further adjusted for income adequacy quintile, visible minority status, Indigenous identity, educational attainment, labor-force status, marital status, occupation, and ecological covariates of community size, airshed, urban form, and four dimensions of the Canadian Marginalization Index (Can-Marg; instability, deprivation, dependency, and ethnic concentration). The mCCHS analyses were also adjusted for individual-level measures of smoking, alcohol consumption, fruit and vegetable consumption, body mass index (BMI), and exercise behavior.

In addition to linear models, the shape of the concentration-response function was investigated using restricted cubic splines (RCS). The number of knots were selected by minimizing the Bayesian Information Criterion (BIC). Two additional models were used to examine the association between nonaccidental mortality and PM2.5. The first is the standard threshold model defined by a transformation of concentration equaling zero if the concentration was less than a specific threshold value and concentration minus the threshold value for concentrations above the threshold. The second additional model was an extension of the Shape Constrained Health Impact Function (SCHIF), the eSCHIF, which converts RCS predictions into functions potentially more suitable for use in health impact assessments. Given the RCS parameter estimates and their covariance matrix, 1,000 realizations of the RCS were simulated at concentrations from the minimum to the maximum concentration, by increments of 0.1 μg/m3. An eSCHIF was then fit to each of these RCS realizations. Thus, 1,000 eSCHIF predictions and uncertainty intervals were determined at each concentration within the total range.

Sensitivity analyses were conducted to examine associations between PM2.5 and mortality when in the presence of, or stratified by tertile of, O3 or Ox. Additionally, associations between PM2.5 and mortality were assessed for sensitivity to lower concentration thresholds, where person-years below a threshold value were assigned the mean exposure within that group. We also examined the sensitivity of the shape of the nonaccidental mortality-PM2.5 association to removal of person-years at or above 12 μg/m3 (the current U.S. National Ambient Air Quality Standard) and 10 μg/m3 (the current Canadian and former [2005] World Health Organization [WHO] guideline, and current WHO Interim Target-4). Finally, differences in the shapes of PM2.5-mortality associations were assessed across broad geographic regions (airsheds) within Canada.

RESULTS

The refined PM2.5 exposure estimates demonstrated improved performance relative to estimates applied previously and in the MAPLE Phase 1 report, with slightly reduced errors, including at lower ranges of concentrations (e.g., for PM2.5 <10 μg/m3).

Positive associations between outdoor PM2.5 concentrations and nonaccidental mortality were consistently observed in all cohorts. In the Stacked CanCHEC analyses (1.3 million deaths), each 10-μg/m3 increase in outdoor PM2.5 concentration corresponded to an HR of 1.084 (95% confidence interval [CI]: 1.073 to 1.096) for nonaccidental mortality. For an interquartile range (IQR) increase in PM2.5 mass concentration of 4.16 μg/m3 and for a mean annual nonaccidental death rate of 92.8 per 10,000 persons (over the 1991-2016 period for cohort participants ages 25-90), this HR corresponds to an additional 31.62 deaths per 100,000 people, which is equivalent to an additional 7,848 deaths per year in Canada, based on the 2016 population. In RCS models, mean HR predictions increased from the minimum concentration of 2.5 μg/m3 to 4.5 μg/m3, flattened from 4.5 μg/m3 to 8.0 μg/m3, then increased for concentrations above 8.0 μg/m3. The threshold model results reflected this pattern with -2 log-likelihood values being equal at 2.5 μg/m3 and 8.0 μg/m3. However, mean threshold model predictions monotonically increased over the concentration range with the lower 95% CI equal to one from 2.5 μg/m3 to 8.0 μg/m3. The RCS model was a superior predictor compared with any of the threshold models, including the linear model.

In the mCCHS cohort analyses inclusion of behavioral covariates did not substantially change the results for both linear and nonlinear models. We examined the sensitivity of the shape of the nonaccidental mortality-PM2.5 association to removal of person-years at or above the current U.S. and Canadian standards of 12 μg/m3 and 10 μg/m3, respectively. In the full cohort and in both restricted cohorts, a steep increase was observed from the minimum concentration of 2.5 μg/m3 to 5 μg/m3. For the full cohort and the <12 μg/m3 cohort the relationship flattened over the 5 to 9 μg/m3 range and then increased above 9 μg/m3. A similar increase was observed for the <10 μg/m3 cohort followed by a clear decline in the magnitude of predictions over the 5 to 9 μg/m3 range and an increase above 9 μg/m3. Together these results suggest that a positive association exists for concentrations >9 μg/m3 with indications of adverse effects on mortality at concentrations as low as 2.5 μg/m3.

Among the other causes of death examined, PM2.5 exposures were consistently associated with an increased hazard of mortality due to ischemic heart disease, respiratory disease, cardiovascular disease, and diabetes across all cohorts. Associations were observed in the Stacked CanCHEC but not in all other cohorts for cerebrovascular disease, pneumonia, and chronic obstructive pulmonary disease (COPD) mortality. No significant associations were observed between mortality and exposure to PM2.5 for heart failure, lung cancer, and kidney failure.

In sensitivity analyses, the addition of O3 and Ox attenuated associations between PM2.5 and mortality. When analyses were stratified by tertiles of copollutants, associations between PM2.5 and mortality were only observed in the highest tertile of O3 or Ox. Across broad regions of Canada, linear HR estimates and the shape of the eSCHIF varied substantially, possibly reflecting underlying differences in air pollutant mixtures not characterized by PM2.5 mass concentrations or the included gaseous pollutants. Sensitivity analyses to assess regional variation in population characteristics and access to healthcare indicated that the observed regional differences in concentration-mortality relationships, specifically the flattening of the concentration-mortality relationship over the 5 to 9 μg/m3 range, was not likely related to variation in the makeup of the cohort or its access to healthcare, lending support to the potential role of spatially varying air pollutant mixtures not sufficiently characterized by PM2.5 mass concentrations.

CONCLUSIONS

In several large, national Canadian cohorts, including a cohort of 7.1 million unique census respondents, associations were observed between exposure to PM2.5 with nonaccidental mortality and several specific causes of death. Associations with nonaccidental mortality were observed using the eSCHIF methodology at concentrations as low as 2.5 μg/m3, and there was no clear evidence in the observed data of a lower threshold, below which PM2.5 was not associated with nonaccidental mortality.

Fine Particulate Matter and Age-Related Eye Disease: The Canadian Longitudinal Study on Aging

Purpose

To determine the relationship between fine particulate matter (PM2.5) and ocular outcomes such as visual impairment and age-related eye disease.

Methods

Baseline data were used from the Canadian Longitudinal Study on Aging. The Comprehensive Cohort consisted of 30,097 adults ages 45 to 85 years. Annual mean PM2.5 levels (µg/m3) for each participant's postal code were estimated from satellite data. Ozone, sulfur dioxide, and nitrogen dioxide levels were also estimated. Binocular presenting visual acuity was measured using a visual acuity chart. Intraocular pressure (IOP) was measured in millimeters of mercury using the Reichart Ocular Response Analyzer. Participants were asked about a diagnosis of glaucoma, macular degeneration, or cataract. Logistic and linear regression models were used.

Results

The overall mean PM2.5 level was 6.5 µg/m3 (SD = 1.8). In the single pollutant models, increased PM2.5 levels (per interquartile range) were associated with visual impairment (odds ratio [OR] = 1.12; 95% confidence interval [CI], 1.02-1.24), glaucoma (OR = 1.14; 95% CI, 1.01-1.29), and visually impairing age-related macular degeneration (OR = 1.52; 95% CI, 1.10-2.09) after adjustment for sociodemographics and disease. PM2.5 had a borderline adjusted association with cataract (OR = 1.06; 95% CI, 0.99-1.14). In the multi-pollutant models, increased PM2.5 was associated with glaucoma and IOP only after adjustment for sociodemographics and disease (OR = 1.24; 95% CI, 1.05-1.46 and β = 0.24; 95% CI, 0.12-0.37).

Conclusions

Increased PM2.5 is associated with glaucoma and IOP. These associations should be confirmed using longitudinal data and potential mechanisms should be explored. If confirmed, this work may have relevance for revision of World Health Organization thresholds to protect human health.

Does exposure to air pollution increase the risk of acute care in young children with asthma? An Ontario, Canada study

Owing to their greater outdoor activity and ongoing lung development, children are particularly vulnerable to the harmful effects of exposure to fine particulate matter (PM_2.5)_. However, the effects of PM_2.5 components are poorly understood. This study aimed to use a longitudinal birth cohort of children with physician-diagnosed incident asthma to investigate the effect of PM_2.5 components at birth on morbidity measured by health services utilization. Of 1277 Toronto Child Health Evaluation Questionnaire (T-CHEQ) participants, the study population included 362 children diagnosed with asthma who were followed for a mean of 13 years from birth until March 31, 2016, or loss-to-follow-up. Concentrations of PM_2.5 and its components were assigned based on participants' postal codes at birth. Study outcomes included counts of asthma, asthma-related, and all-cause health services use. Poisson regression in single-, two-, and multi-pollutant models was used to estimate rate ratios (RR) per interquartile range (IQR) increase of exposures. Covariates were included in all models to further adjust for potential confounding. The adjusted RR for sulfate (SO₄) and all-cause hospitalizations was statistically significant with RR = 2.23 (95% confidence interval [CI]: 1.25-3.96) in a multi-pollutant model with nitrogen dioxide (NO₂) and ozone (O₃). In multi-pollutant models with oxidants, the adjusted RRs for SO₄ of all-cause hospitalizations and emergency department (ED) visits were also statistically significant with RR = 2.31 (95% CI: 1.32-4.03) and RR = 1.39 (95% CI: 1.02-1.90), respectively. While unadjusted single-pollutant RRs for asthma-specific and asthma-related health services use with the SO₄ component of PM_2.5 were above one, none were statistically significant. This study found significant associations with exposure to SO₄ in PM_2.5 and all-cause acute care, chiefly for hospitalizations, in children with asthma.

Outdoor Air Pollution and Depression in Canada: A Population-Based Cross-Sectional Study from 2011 to 2016

To assess whether exposure to increased levels of outdoor air pollution is associated with psychological depression, six annual iterations of the Canadian Community Health Survey (n ≈ 127,050) were used to estimate the prevalence of a major depressive episode (2011-2014) or severity of depressive symptoms (2015-2016). Survey data were linked with outdoor air pollution data obtained from the Canadian Urban Environmental Health Research Consortium, with outdoor air pollution represented by fine particulate matter ≤2.5 micrometers (μm) in diameter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂). Log-binomial models were used to estimate the association between outdoor air pollution and depression, and included adjustment for age, sex, marital status, income, education, employment status, urban versus rural households, cigarette smoking, and chronic illness. No evidence of associations for either depression outcomes were found. Given the generally low levels of outdoor air pollution in Canada, these findings should be generalized with caution. It is possible that a meaningful association with major depression may be observed in regions of the world where the levels of outdoor air pollution are greater, or during high pollution events over brief time intervals. Future research is needed to replicate these findings and to further investigate these associations in other regions and populations.

Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality

Objective

To determine whether long term exposure to outdoor nitrogen dioxide (NO₂) is associated with all-cause or cause-specific mortality.

Methods

MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran’s Q and I² measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined and publication bias was evaluated using Funnel plots, Begg’s and Egger’s tests, and trim and fill.

Results

Seventy-nine studies based on 47 cohorts, plus one set of pooled analyses of multiple European cohorts, met inclusion criteria. There was a consistently high degree of heterogeneity. After excluding studies with probably high or high risk of bias in the confounding domain (n = 12), pooled hazard ratios (HR) indicated that long term exposure to NO₂ was significantly associated with mortality from all/ natural causes (pooled HR 1.047, 95% confidence interval (CI), 1.023–1.072 per 10 ppb), cardiovascular disease (pooled HR 1.058, 95%CI 1.026–1.091), lung cancer (pooled HR 1.083, 95%CI 1.041–1.126), respiratory disease (pooled HR 1.062, 95%CI1.035–1.089), and ischemic heart disease (pooled HR 1.111, 95%CI 1.079–1.144). Pooled estimates based on multi-pollutant models were consistently smaller than those from single pollutant models and mostly non-significant.

Conclusions

For all causes of death other than cerebrovascular disease, the overall quality of the evidence is moderate, and the strength of evidence is limited, while for cerebrovascular disease, overall quality is low and strength of evidence is inadequate. Important uncertainties remain, including potential confounding by co-pollutants or other concomitant exposures, and limited supporting mechanistic evidence. (PROSPERO registration number CRD42018084497)

Isolating the impact of COVID-19 lockdown measures on urban air quality in Canada

We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada's four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO₂, PM_2.5, and O₃ during a "pre-lockdown" period (15 February-14 March 2020) and a "lockdown" period (22 March-2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010-2019). Higher-than-usual seasonal declines in mean daily NO₂ were observed for the pre-lockdown to lockdown periods in 2020. For PM_2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O₃ all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO₂ surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM_2.5. O₃ surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but O_x (odd oxygen), like NO₂ and PM_2.5, decreased as expected over these cities.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01039-1.

Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO2) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO2 emissions due to the lockdown were seen, with an average 40% (roughly 10 kt[NO2]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO2]/yr to 40 kt[NO2]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO2 columns.

Self-rated stress, distress, mental health, and health as modifiers of the association between long-term exposure to ambient pollutants and mortality

BACKGROUND

Individual and neighbourhood-scale socioeconomic characteristics modify associations between exposure to air pollution and mortality. The role of stress, which may integrate effects of social and environmental exposures on health, is unknown. We examined whether an individual's perspective on their own well-being, as assessed using self-rated measures of stress and health, modifies the pollutant-mortality relationship.

METHODS

The Canadian Community Health Survey (CCHS)-mortality cohort includes respondents from surveys administered between 2001 and 2012 linked to vital statistics and postal codes from 1981 until 2016. Annual fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) exposure estimates were attached to a sample of cohort members aged 30-89 years (n = 398,300 respondents/3,848,400 person-years). We examined whether self-rated stress, distress, mental health, and general health modified associations between long-term exposure to each pollutant (three-year moving average with one-year lag) and non-accidental mortality using Cox survival models, adjusted for individual- (i.e. socioeconomic and behavioural) and neighbourhood-scale covariates.

RESULTS

In fully-adjusted models, the relationship between exposure to pollutants and mortality was stronger among those with poor self-rated mental health, including a significant difference for NO₂ (hazard ratio (HR) = 1.15, 95% CI 1.06-1.25 per IQR) compared to those with very good/excellent mental health (HR = 1.05, 95% CI 1.01-1.08; Cochran's Q = 4.01; p < 0.05). Poor self-rated health was similarly associated with higher pollutant-associated HRs, but only in unadjusted models. Stress and distress did not modify pollutant-mortality associations.

CONCLUSIONS

Poor self-rated mental and general health were associated with increased mortality attributed to exposure to ambient pollutants.

Evaluating the U.S. Air Quality Index as a risk communication tool: Comparing associations of index values with respiratory morbidity among adults in California

Background

The Air Quality Index (AQI) in the United States is widely used to communicate daily air quality information to the public. While use of the AQI has led to reported changes in individual behaviors, such behavior modifications will only mitigate adverse health effects if AQI values are indicative of public health risks. Few studies have assessed the capability of the AQI to accurately predict respiratory morbidity risks.

Methods and findings

In three major regions of California, Poisson generalized linear models were used to assess seasonal associations between 1,373,165 respiratory emergency department visits and short-term exposure to multiple metrics between 2012–2014, including: daily concentrations of NO₂, O₃, and PM_2.5; the daily reported AQI; and a newly constructed health-based air quality index. AQI values were positively associated (average risk ratio = 1.03, 95% CI 1.02–1.04) during the cooler months of the year (November-February) in all three regions when the AQI was very highly correlated with PM_2.5 (R² ≥ 0.89). During the warm season (March-October) in the San Joaquin Valley region, neither AQI values nor the individual underlying air pollutants were associated with respiratory morbidity. Additionally, AQI values were not positively associated with respiratory morbidity in the Southern California region during the warm season, despite strong associations of the individual underlying air pollutants with respiratory morbidity; in contrast, health-based index values were observed to be significantly associated with respiratory morbidity as part of an applied policy analysis in this region, with a combined risk ratio of 1.02 (95% CI: 1.01–1.03).

Conclusions

In regions where individual air pollutants are associated with respiratory morbidity, and during seasons with relatively simple air mixtures, the AQI can effectively serve as a risk communication tool for respiratory health risks. However, the predictive ability of the AQI and any other index is contingent upon the monitored values being representative of actual population exposures. Other approaches, such as health-based indices, may be needed in order to effectively communicate health risks of air pollution in regions and seasons with more complex air mixtures.

Public health issues from crude-oil production in the Ecuadorian Amazon territories

Crude oil production (COP) is a high-pollution industry but the vast Amazon rainforest has been an active COP zone for South America. Although COP has been associated with a variety of health effects among workers around the world, such effects have not been adequately investigated in the Amazon region, especially at the community level. Therefore, this review was conducted to provide a report about COP in the Amazon of Ecuador and about its association with health status of indigenous human populations. Some epidemiological surveys in the Amazonian Territories indicate that COP has been associated with health problems in the surrounding populations, e.g. cancers in the stomach, rectum, skin, soft tissue, kidney and cervix in adults, and leukemia in children. In addition, some biomarkers and mechanistic studies show exposure effects. However, due to limitations from these studies, contradictory associations have been reported. Our review indicates that COP in the Amazonian territories of northern Ecuador was characterised by contamination which could have affected the indigenous and non-indigenous populations. However, there have not been dedicated investigations to provide relationships between the contamination and the subsequent exposure-health effects. Since indigenous populations have different lifestyle and cultures from regular city dwellers, systematic studies on their potential health hazards need to be conducted. Due to the remote locations and sparse populations, these new studies may involve the use of novel and genomic-based biomarkers as well as using high technology in the remote regions.

An overview of selected emerging outdoor airborne pollutants and air quality issues: The need to reduce uncertainty about environmental and human impacts

According to the literature, it is estimated that outdoor air pollution is responsible for the premature death in a range from 3.7 to 8.9 million persons on an annual basis across the world. Although there is uncertainty on this figure, outdoor air pollution represents one of the greatest global risks to human health. In North America, the rapid evolution of technologies (e.g., nanotechnology, unconventional oil and gas rapid development, higher demand for fertilizers in agriculture) and growing demand for ground, marine and air transportation may result in significant increases of emissions of pollutants that have not been carefully studied so far. As a result, these atmospheric pollutants insufficiently addressed by science in Canada and elsewhere are becoming a growing issue with likely human and environmental impacts in the near future. Here, an emerging pollutant is defined as one that meets the following criteria: 1) potential or demonstrated risk for humans or the environment, 2) absence of Canada-wide national standard, 3) insufficient routine monitoring, 4) yearly emissions greater than one ton in Canada, 5) insufficient data concerning significant sources, fate, and detection limit, and 6) insufficiently addressed by epidemiological studies. A new methodology to rank emerging pollutants is proposed here based on weighting multiple criteria. Some selected emerging issues are also discussed here and include the growing concern of ultrafine or nanoparticles, growing ammonia emissions (due to rapid expansion of the agriculture), increased methane/ethane/propane emissions (due to the expanding hydraulic fracturing in the oil and gas sector) and the growing transportation sector. Finally, the interaction between biological and anthropogenic pollution has been found to be a double threat for public health. Here, a multidisciplinary and critical overview of selected emerging pollutants and related critical issues is presented with a focus in Canada.Implications: This overview paper provides a selection methodology for emerging pollutants in the atmospheric environment. It also provides a critical discussion of some related issues. The ultimate objective is to inform about the need to 1) address emerging issues through adequate surface monitoring and modeling in order to inform the development of regulations, 2) reduce uncertainties by geographically mapping emerging pollutants (e.g., through data fusion, data assimilation of observations into air quality models) which can improve the scientific support of epidemiological studies and policies. This review also highlights some of the difficulties with the management of these emerging pollutants, and the need for an integrated approach.

Early life exposure to air pollution and incidence of childhood asthma, allergic rhinitis and eczema

Rationale

There is growing evidence that air pollution may contribute to the development of childhood asthma and other allergic diseases. In this follow-up of the Toronto Child Health Evaluation Questionnaire (T-CHEQ) study, we examined associations between early life exposures to air pollution and incidence of asthma, allergic rhinitis and eczema from birth through adolescence.

Methods

1286 T-CHEQ participants were followed from birth until outcome (March 31, 2016) or loss to follow-up, with a mean of 17 years of follow-up. Concentrations of nitrogen dioxide (NO₂), ozone (O₃) and particulate matter with a 50% cut-off aerodynamic diameter of 2.5 µm (PM_2.5) from January 1, 1999 to December 31, 2012 were assigned to participants based on their postal codes at birth using ground observations, chemical/meteorological models, remote sensing and land-use regression models. Study outcomes included incidence of physician-diagnosed asthma, allergic rhinitis and eczema. Cox proportional hazard regression models were used to estimate hazard ratios per interquartile range of exposures and outcomes, adjusting for potential confounders.

Results

Hazard ratios of 1.17 (95% CI 1.05–1.31) for asthma and 1.07 (95% CI 0.99–1.15) for eczema were observed for total oxidants (O₃ and NO₂) at birth. No significant increase in risk was found for PM_2.5.

Conclusions

Exposures to oxidant air pollutants (O₃ and NO₂) but not PM_2.5 were associated with an increased risk of incident asthma and eczema in children. This suggests that improving air quality may contribute to the prevention of asthma and other allergic disease in childhood and adolescence.

This study found that exposure to total oxidants at birth increased the risk of developing asthma by 17% and eczema by 7%. Adverse impacts of exposure to air pollutants, particularly ozone and nitrogen dioxide, may have their origins in early life.http://bit.ly/33PClYN

Analysis of Spatio-temporal Characteristics and Driving Forces of Air Quality in the Northern Coastal Comprehensive Economic Zone, China

Comprehensive analysis of air quality is essential to underpin knowledge-based air quality conservation policies and funding decisions by governments and managers. In this paper, air quality change characteristics for the Northern Coastal Comprehensive Economic Zone from 2008 to 2018 were analyzed using air quality indices. The spatio-temporal pattern of air quality was identified using centroid migration, spatial autocorrelation analysis and spatial analysis in a geographic information system (GIS). A spatial econometric model was established to confirm the natural and anthropogenic factors affecting air quality. Results showed that air pollution decreased significantly. PM2.5, PM10, and O3 were the primary pollutants. The air quality exhibited an inverted U-shaped trend from January to December, with the highest quality being observed in summer and the lowest during winter. Spatially, the air quality showed an increasing trend from inland to the coast and from north to south, with significant spatial autocorrelation and clustering. Population, energy consumption, temperature, and atmospheric pressure had significant negative impacts on air quality, while wind speed had a positive impact. This study offers an efficient and effective method to evaluate air quality change. The research provides important scientific information necessary for developing future air pollution prevention and control.

Mortality-Air Pollution Associations in Low-Exposure Environments (MAPLE): Phase 1

INTRODUCTION

Fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter, or PM2.5) is associated with mortality, but the lower range of relevant concentrations is unknown. Novel satellite-derived estimates of outdoor PM2.5 concentrations were applied to several large population-based cohorts, and the shape of the relationship with nonaccidental mortality was characterized, with emphasis on the low concentrations (<12 μg/m3) observed throughout Canada.

METHODS

Annual satellite-derived estimates of outdoor PM2.5 concentrations were developed at 1-km2 spatial resolution across Canada for 2000-2016 and backcasted to 1981 using remote sensing, chemical transport models, and ground monitoring data. Targeted ground-based measurements were conducted to measure the relationship between columnar aerosol optical depth (AOD) and ground-level PM2.5. Both existing and targeted ground-based measurements were analyzed to develop improved exposure data sets for subsequent epidemiological analyses.

Residential histories derived from annual tax records were used to estimate PM2.5 exposures for subjects whose ages ranged from 25 to 90 years. About 8.5 million were from three Canadian Census Health and Environment Cohort (CanCHEC) analytic files and another 540,900 were Canadian Community Health Survey (CCHS) participants. Mortality was linked through the year 2016. Hazard ratios (HR) were estimated with Cox Proportional Hazard models using a 3-year moving average exposure with a 1-year lag, with the year of follow-up as the time axis. All models were stratified by 5-year age groups, sex, and immigrant status. Covariates were based on directed acyclical graphs (DAG), and included contextual variables (airshed, community size, neighborhood dependence, neighborhood deprivation, ethnic concentration, neighborhood instability, and urban form). A second model was examined including the DAG-based covariates as well as all subject-level risk factors (income, education, marital status, indigenous identity, employment status, occupational class, and visible minority status) available in each cohort. Additional subject-level behavioral covariates (fruit and vegetable consumption, leisure exercise frequency, alcohol consumption, smoking, and body mass index [BMI]) were included in the CCHS analysis.

Sensitivity analyses evaluated adjustment for covariates and gaseous copollutants (nitrogen dioxide [NO2] and ozone [O3]), as well as exposure time windows and spatial scales. Estimates were evaluated across strata of age, sex, and immigrant status. The shape of the PM2.5-mortality association was examined by first fitting restricted cubic splines (RCS) with a large number of knots and then fitting the shape-constrained health impact function (SCHIF) to the RCS predictions and their standard errors (SE). This method provides graphical results indicating the RCS predictions, as a nonparametric means of characterizing the concentration-response relationship in detail and the resulting mean SCHIF and accompanying uncertainty as a parametric summary.

Sensitivity analyses were conducted in the CCHS cohort to evaluate the potential influence of unmeasured covariates on air pollution risk estimates. Specifically, survival models with all available risk factors were fit and compared with models that omitted covariates not available in the CanCHEC cohorts. In addition, the PM2.5 risk estimate in the CanCHEC cohort was indirectly adjusted for multiple individual-level risk factors by estimating the association between PM2.5 and these covariates within the CCHS.

RESULTS

Satellite-derived PM2.5 estimates were low and highly correlated with ground monitors. HR estimates (per 10-μg/m3 increase in PM2.5) were similar for the 1991 (1.041, 95% confidence interval [CI]: 1.016-1.066) and 1996 (1.041, 1.024-1.059) CanCHEC cohorts with a larger estimate observed for the 2001 cohort (1.084, 1.060-1.108). The pooled cohort HR estimate was 1.053 (1.041-1.065). In the CCHS an analogous model indicated a HR of 1.13 (95% CI: 1.06-1.21), which was reduced slightly with the addition of behavioral covariates (1.11, 1.04-1.18). In each of the CanCHEC cohorts, the RCS increased rapidly over lower concentrations, slightly declining between the 25th and 75th percentiles and then increasing beyond the 75th percentile. The steepness of the increase in the RCS over lower concentrations diminished as the cohort start date increased. The SCHIFs displayed a supralinear association in each of the three CanCHEC cohorts and in the CCHS cohort.

In sensitivity analyses conducted with the 2001 CanCHEC, longer moving averages (1, 3, and 8 years) and smaller spatial scales (1 km2 vs. 10 km2) of exposure assignment resulted in larger associations between PM2.5 and mortality. In both the CCHS and CanCHEC analyses, the relationship between nonaccidental mortality and PM2.5 was attenuated when O3 or a weighted measure of oxidant gases was included in models. In the CCHS analysis, but not in CanCHEC, PM2.5 HRs were also attenuated by the inclusion of NO2. Application of the indirect adjustment and comparisons within the CCHS analysis suggests that missing data on behavioral risk factors for mortality had little impact on the magnitude of PM2.5-mortality associations. While immigrants displayed improved overall survival compared with those born in Canada, their sensitivity to PM2.5 was similar to or larger than that for nonimmigrants, with differences between immigrants and nonimmigrants decreasing in the more recent cohorts.

CONCLUSIONS

In several large population-based cohorts exposed to low levels of air pollution, consistent associations were observed between PM2.5 and nonaccidental mortality for concentrations as low as 5 μg/m3. This relationship was supralinear with no apparent threshold or sublinear association.

Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort

BACKGROUND

Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5.

METHODS

Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates.

RESULTS

In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04-1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models.

CONCLUSIONS

Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.

Examining the Shape of the Association between Low Levels of Fine Particulate Matter and Mortality across Three Cycles of the Canadian Census Health and Environment Cohort

BACKGROUND

Ambient fine particulate air pollution with aerodynamic diameter ≤2.5 μm (PM2.5) is an important contributor to the global burden of disease. Information on the shape of the concentration-response relationship at low concentrations is critical for estimating this burden, setting air quality standards, and in benefits assessments.

OBJECTIVES

We examined the concentration-response relationship between PM2.5 and nonaccidental mortality in three Canadian Census Health and Environment Cohorts (CanCHECs) based on the 1991, 1996, and 2001 census cycles linked to mobility and mortality data.

METHODS

Census respondents were linked with death records through 2016, resulting in 8.5 million adults, 150 million years of follow-up, and 1.5 million deaths. Using annual mailing address, we assigned time-varying contextual variables and 3-y moving-average ambient PM2.5 at a 1×1 km spatial resolution from 1988 to 2015. We ran Cox proportional hazards models for PM2.5 adjusted for eight subject-level indicators of socioeconomic status, seven contextual covariates, ozone, nitrogen dioxide, and combined oxidative potential. We used three statistical methods to examine the shape of the concentration-response relationship between PM2.5 and nonaccidental mortality.

RESULTS

The mean 3-y annual average estimate of PM2.5 exposure ranged from 6.7 to 8.0 μg/m3 over the three cohorts. We estimated a hazard ratio (HR) of 1.053 [95% confidence interval (CI): 1.041, 1.065] per 10-μg/m3 change in PM2.5 after pooling the three cohort-specific hazard ratios, with some variation between cohorts (1.041 for the 1991 and 1996 cohorts and 1.084 for the 2001 cohort). We observed a supralinear association in all three cohorts. The lower bound of the 95% CIs exceeded unity for all concentrations in the 1991 cohort, for concentrations above 2 μg/m3 in the 1996 cohort, and above 5 μg/m3 in the 2001 cohort.

DISCUSSION

In a very large population-based cohort with up to 25 y of follow-up, PM2.5 was associated with nonaccidental mortality at concentrations as low as 5 μg/m3. https://doi.org/10.1289/EHP5204.

Geospatial analyses of adverse birth outcomes in Southwestern Ontario: Examining the impact of environmental factors

BACKGROUND

A growing body of research has examined the association between exposure to environmental factors during pregnancy and adverse birth outcomes; however, many studies do not control for potential covariates and findings vary considerably.

OBJECTIVE

To test the relative influence of environmental factors including exposure to air pollution, major roads, highways, industry, parks, greenspaces, and food retailers on low birth weight (LBW) and preterm birth (PTB) in Southwestern Ontario (SWO), Canada, while accounting for medical (e.g., previous preterm birth, gestational diabetes), behavioral (e.g., alcohol, smoking), demographic (e.g., maternal age, body mass index), and neighborhood-level socioeconomic (e.g., household income, education) factors.

METHODS

This retrospective cohort study consisted of a large sample of pregnant women from SWO who gave birth to singleton newborns between February 2009 and February 2014 at London Health Sciences Centre. Data on maternal postal codes were entered into a Geographic Information System to map the distribution of maternal residences and determine selected characteristics of their neighborhood environments (i.e., socioeconomic, built, natural). These variables were developed based on postal codes where the mothers lived prior to giving birth. Logistic regression was used to assess the relative effects of the physical environment, socioeconomic status, clinical history, and behavioral risk factors on mothers having a LBW or PTB infant.

RESULTS

Out of 25,263 live births, 5.7% were LBW and 7.5% were PTB. Exposure to sulfur dioxide was a top predictor of both LBW and PTB. For every one-unit increase in sulfur dioxide, the odds of a LBW and PTB were 3.4 (95% CI: 2.2, 5.2) and 2.0 (95% CI: 1.4, 3.0) times higher, after controlling for other variables in the model, respectively (p < 0.001). Previous PTB was also highly associated with both birth outcomes.

CONCLUSIONS

Health care providers should be informed about the hazards of air pollution to developing fetuses so that recommendations can be made to their pregnant patients about limiting exposure when air quality is poor.

A Measurement-Model Fusion Approach for Improved Wet Deposition Maps and Trends

Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation-based gridded precipitation data generated by the Parameter-elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross-validation analysis shows that the adjusted annual accumulated WD for NO3 -, NH4 +, and SO4 2- from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO3 - and SO4 2- are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH4 + are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south.

Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular, we use the variance of passive observation-minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

Surface data assimilation of chemical compounds over North America and its impact on air quality and Air Quality Health Index (AQHI) forecasts

The aim of this paper is to analyze the impact of initializing GEM-MACH, Environment and Climate Change Canada's air quality (AQ) forecast model, with multi-pollutant surface objective analyses (MPSOA). A series of 48-h air quality forecasts were launched for July 2012 (summer case) and January 2014 (winter case) for ozone, NO₂, and PM_2.5. In this setup, the GEM-MACH model (version 1.3.8.2) was initialized with surface analysis increments (from MPSOA) which were projected in the vertical by applying an appropriate fractional weighting in order to obtain 3D analyses in the lower troposphere. Here, we have used a methodology based on sensitivity tests to obtain the optimum vertical correlation length (VCL). Overall, results showed that for PM_2.5, more specifically for sulfate and crustal materials, AQ forecasts initialized with MPSOA showed a very significant improvement compared to forecasts without data assimilation, which extended beyond 48 h in all seasons. Initializing the model with ozone analyses also had a significant impact but on a shorter time scale than that of PM_2.5. Finally, assimilation of NO₂ was found to have much less impact than longer-lived species. The impact of simultaneous assimilation of the three pollutants (PM_2.5, ozone, and NO₂) was also examined and found very significant in reducing the total error of the Air Quality Health Index (AQHI) over 48 h and beyond. We suggest that the period over which there is a significant improvement due to assimilation could be an adequate measure of the pollutant atmospheric lifetime.

A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada

An objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth-Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and the [Formula: see text] diagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, or [Formula: see text] should be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values.

IMPLICATIONS

This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme.

Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)'s AirNow program.