Within- and between-city contrasts in nitrogen dioxide and mortality in 10 Canadian cities; a subset of the Canadian Census Health and Environment Cohort (CanCHEC)

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Air Pollution and Cardiac Diseases: A Review of Experimental Studies

Air pollution is associated with around 6.5 million premature deaths annually, which are directly related to cardiovascular diseases, and the most dangerous atmospheric pollutants to health are as follows: NO2, SO2, CO, and PM. The mechanisms underlying the observed effects have not yet been clearly defined. This work aims to conduct a narrative review of experimental studies to provide a more comprehensive and multiperspective assessment of how the effect of atmospheric pollutants on cardiac activity can result in the development of cardiac diseases. For this purpose, a review was carried out in databases of experimental studies, excluding clinical trials, and epidemiological and simulation studies. After analyzing the available information, the existence of pathophysiological effects of the different pollutants on cardiac activity from exposure during both short-term and long-term is evident. This narrative review based on experimental studies is a basis for the development of recommendations for public health.

Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis

BACKGROUND

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality.

METHODS

The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis.

RESULTS

Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m³, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well.

CONCLUSIONS

The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Assessment of Low-Level Air Pollution and Cardiovascular Incidence in Gdansk, Poland: Time-Series Cross-Sectional Analysis

(1) Background: More than 1.8 million people in the European Union die every year as a result of CVD, accounting for 36% of all deaths with a large proportion being premature (before the age of 65). There are more than 300 different risk factors of CVD, known and air pollution is one of them. The aim of this study was to investigate whether daily cardiovascular mortality was associated with air pollutants and meteorological conditions in an urban environment with a low level of air pollution. (2) Methods: Data on daily incidence of strokes and myocardial infarctions in the city of Gdansk were obtained from the National Health Fund (NHF) and covered the period from 1 January 2014 to 31 December 2018. Data on the level of pollution, i.e., SO2, NO, NO2, NOx, CO, PM10, PM2.5, CO2, O3 and meteorological conditions came from the foundation: Agency of Regional Air Quality Monitoring in the Gdańsk metropolitan area (ARMAG). Using these data, we calculated mean values with standard deviation (SD) and derived the minimum and maximum values and interquartile range (IQR). Time series regression with Poisson distribution was used in statistical analysis. (4) Results: Stroke incidence is significantly affected by an increase in concentrations of NO, NO2 and NOx with RRs equal to 1.019 (95%CI: 1.001–1.036), 1.036 (95%CI: 1.008–1.064) and 1.017 (95%CI: 1.000–1.034) for every increase in IQR by 14.12, 14.62 and 22.62 μg/m3, respectively. Similarly, myocardial infarction incidence is significantly affected by an increase in concentrations of NO, NO2 and NOx with RRs equal to 1.030 (95%CI: 1.011–1.048), 1.053 (95%CI: 1.024–1.082) and 1.027 (95%CI: 1.010–1.045) for every increase in IQR by 14.12, 14.62 and 22.62 μg/m3, respectively. Both PM10 and PM2.5 were positively associated with myocardial infarction incidence. (5) Conclusions: In this time-series cross-sectional study, we found strong evidence that support the hypothesis that transient elevations in ambient PM2.5, PM10, NO2, SO2 and CO are associated with higher relative risk of ischemic stroke and myocardial infarction incidents.

Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study

BACKGROUND

An indirect adjustment method was developed to control for unmeasured confounders in a large administrative cohort study. A previous study that proposed the indirect adjustment method assessed the validity of the method by simulations but did not consider the direction of bias and scenarios with multiple missing confounders. In this study, we evaluated the direction and the magnitude of bias of the indirect adjustment method with multiple correlated unmeasured confounders using simulation and empirical datasets.

METHODS

A simulation study was conducted to compare the bias of the indirect adjustment by varying the number of confounders, magnitude of correlation between confounders, and the number of adjustment variables. An empirical study was conducted by applying the indirect adjustment method to the association between PM₁₀ and mortality using the Korea National Health and Nutrition Examination Survey linked Cause of Death data for 2007-2016.

RESULTS

The simulations of the present study demonstrated that 1) when a confounder is positively associated with both exposure and outcome, indirect adjustment might bias the effect size downward; 2) the magnitude of bias might depend on the correlation between unmeasured confounders; and 3) indirect adjustment for multiple missing confounders at once could result in a higher bias than that for some of the missing confounders. Empirical analyses also showed consistent results, but the bias of indirectly adjusted effect estimates was sometimes larger than that of unadjusted effect estimates.

CONCLUSIONS

The indirect adjustment method is a promising technique to reduce the bias from unmeasured confounding; however, it should be implemented carefully, particularly when there are multiple correlated unmeasured confounders of the same direction.

Global fine-scale changes in ambient NO2 during COVID-19 lockdowns

Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1-9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910-20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Land use regression modelling of NO2 in São Paulo, Brazil

BACKGROUND

Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce.

METHODS

In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO₂) concentration in the study area of the Western Region Birth Cohort in São Paulo. NO₂ measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction.

RESULTS

Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R²) respectively. Cross-holdout validation tests suggest robust models. NO₂ levels ranged from 43.2 μg/m³ to 93.4 μg/m³ in the winter and between 28.1 μg/m³ and 72.8 μg/m³ in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO₂ values over the WHO suggested annual guideline of 40 μg/m³ annual average.

CONCLUSION

In this study we were able to develop robust models to predict NO₂ residential exposure. We could show that average measures, and therefore the predictions of NO_2, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO₂ levels.

Electrical detection of ppb region NO2 using Mg-porphyrin-modified graphene field-effect transistors

The trace detection of NO₂ through small sensors is essential for air quality measurement and the health field; however, small sensors based on electrical devices cannot detect NO₂ with the desired selectivity and quantitativity in the parts per billion (ppb) concentration region. In this study, we fabricated metalloporphyrin-modified graphene field-effect transistors (FETs). Mg-, Ni-, Cu-, and Co-porphyrins were deposited on the graphene FETs, and the transfer characteristics were measured. With the introduction of NO₂ in the ppb concentration region, the FETs of pristine graphene and Ni-, Cu-, and Co-porphyrin-modified graphene showed an insufficient response, whereas the Mg-porphyrin-modified graphene exhibited large voltage shifts in the transport characteristics. This indicates that Mg-porphyrin acts as an adsorption site for NO₂ molecules. An analysis of the Dirac-point voltage shifts with the introduction of NO₂ indicates that the shifts were well-fitted with the Langmuir adsorption isotherm model, and the limit of detection for NO₂ was found to be 0.3 ppb in N₂. The relationship between the mobility and the Dirac-point voltage shift with the NO₂ concentration shows that the complex of NO₂ and Mg-porphyrin behaves as a point-like charge impurity. Moreover, the Mg-porphyrin-modified graphene FETs show less response to other gases (O₂, H₂, acetic acid, trimethylamine, methanol, and hexane), thus indicating high sensitivity for NO₂ detection. Furthermore, we successfully demonstrated the quantitative detection of NO₂ in air, which is near the environmental standards. In conclusion, the results of the Mg-porphyrin-modified graphene FETs enable a rapid, easy, and selective detectability.

Risk and Resilience: How Is the Health of Older Adults and Immigrant People Living in Canada Impacted by Climate- and Air Pollution-Related Exposures?

BACKGROUND

In the rapidly shifting Canadian climate, an ageing population, and increased migration, a greater understanding of how local climate and air pollution hazards impact older adults and immigrant populations will be necessary for mitigating and adapting to adverse health impacts.

OBJECTIVES

To explore the reported health impacts of climate change and air pollution exposures in older adults and immigrant people living in Canada, identify known factors influencing risk and resilience in these populations and gaps in the literature.

METHODS

We searched for research focused on older adults and immigrants living in Canada, published from 2010 onward, where the primary exposures were related to climate or air pollution. We extracted data on setting, exposures, health outcomes, and other relevant contextual factors.

RESULTS AND DISCUSSION

We identified 52 eligible studies, most focused in Ontario and Quebec. Older people in Canada experience health risks due to climate and air pollution exposures. The extent of the risk depends on multiple factors. We found little information about the climate- and air pollution-related health impacts experienced by immigrant communities.

CONCLUSIONS

Further research about climate- and air pollution-related exposures, health, and which factors promote or reduce resiliency in Canada's older adults and immigrant communities is necessary.

Health and Climate Incentives for the Deployment of Cleaner On-Road Vehicle Technologies

Reducing greenhouse gas (GHG) emissions of private passenger vehicles, transit buses, and commercial vehicles with newer technology can improve air quality, and, subsequently, population exposure and public health. For the Greater Toronto and Hamilton Area, we estimated the burden of each vehicle fleet on population health in the units of years of life lost and premature deaths. We then assessed the separate health benefits of electrifying private vehicles, transit buses, and replacing the oldest commercial vehicles with newer trucks. A complete deployment of electric passenger vehicles would lead to health benefits similar to replacing all trucks older than 8 years (i.e., about 300 premature deaths prevented) in the first year of implementation; however, GHG emissions would be mainly reduced with passenger fleet electrification. Transit bus electrification has similar health benefits as electrifying half of the passenger fleet (i.e., about 150 premature deaths prevented); however, the GHG emission reductions reached under the bus electrification scenario are lower by 90%. By accelerating policies to electrify cars and buses and renew older trucks, governments can save hundreds of lives per year and mitigate the impacts of climate change.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO₂) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO₂ concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R² = 0.71 for prediction of NO₂ data in 2002 and 2004) uses daily average NO₂ concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO₂ levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO₂ patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

Long-term exposure to NO2 and O3 and all-cause and respiratory mortality: A systematic review and meta-analysis

BACKGROUND

WHO has published several volumes of Global Air Quality Guidelines to provide guidance on the health risks associated with exposure to outdoor air pollution. As new scientific evidence is generated, air quality guidelines need to be periodically revised and, where necessary, updated.

OBJECTIVES

The aims of the study were 1) to summarise the available evidence on the effect of long-term exposure to ozone (O3) and nitrogen dioxide (NO2) on mortality; 2) and to assess concentration response functions (CRF), their shape and the minimum level of exposures measured in studies to support WHO's update of the global air quality guidelines.

DATA SOURCES

We conducted a systematic literature search of the Medline, Embase and Web of Science databases following a protocol proposed by WHO and applied Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines for reporting our results.

STUDY ELIGIBILITY CRITERIA

Cohort studies in human populations (including sub-groups at risk) exposed to long-term concentrations of NO2 and O3. Outcomes assessed were all-cause, respiratory, Chronic Obstructive Pulmonary Disease (COPD) and Acute Lower Respiratory Infection (ALRI) mortality.

STUDY APPRAISAL AND SYNTHESIS METHODS

Studies included in the meta-analyses were assessed using a new Risk of Bias instrument developed by a group of experts convened by WHO. Study results are presented in forest plots and quantitative meta-analyses were conducted using random effects models. The certainty of evidence was assessed using a newly developed adaptation of GRADE.

RESULTS

The review identified 2068 studies of which 95 were subject to full-text review with 45 meeting the inclusion criteria. An update in September 2018 identified 159 studies with 1 meeting the inclusion criteria. Of the 46 included studies, 41 reported results for NO2 and 20 for O3. The majority of studies were from the USA and Europe with the remainder from Canada, China and Japan. Forty-two studies reported results for all-cause mortality and 22 for respiratory mortality. Associations for NO2 and mortality were positive; random-effects summary relative risks (RR) were 1.02 (95% CI: 1.01, 1.04), 1.03 (1.00, 1.05), 1.03 (1.01, 1.04) and 1.06 (1.02, 1.10) per 10 μg/m3 for all-cause (24 cohorts), respiratory (15 cohorts), COPD (9 cohorts) and ALRI (5 cohorts) mortality respectively. The review identified high levels of heterogeneity for all causes of death except COPD. A small number of studies investigated the shape of the concentration-response relationship and generally found little evidence to reject the assumption of linearity across the concentration range. Studies of O3 using annual metrics showed the associations with all-cause and respiratory mortality were 0.97 (0.93, 1.02) and 0.99 (0.89, 1.11) per 10 μg/m3 respectively. For studies using peak O3 metrics, the association with all-cause mortality was 1.01 (1.00, 1.02) and for respiratory mortality 1.02 (0.99, 1.05), each per 10 μg/m3. The review identified high levels of heterogeneity. Few studies investigated the shape of the concentration-response relationship. Certainty in the associations (adapted GRADE) with mortality was rated low to moderate for each exposure-outcome pair, except for NO2 and COPD mortality which was rated high.

LIMITATIONS

The substantial heterogeneity for most outcomes in the review requires explanation. The evidence base is limited in terms of the geographical spread of the study populations and, for some outcomes, the small number of independent cohorts for meta-analysis precludes meaningful meta-regression to explore causes of heterogeneity. Relatively few studies assessed specifically the shape of the CRF or multi-pollutant models.

CONCLUSIONS

The short-comings in the existing literature base makes determining the precise nature (magnitude and linearity) of the associations challenging. Certainty of evidence assessments were moderate or low for both NO2 and O3 for all causes of mortality except for NO2 and COPD mortality where the certainty of the evidence was judged as high.

Health and climate benefits of Electric Vehicle Deployment in the Greater Toronto and Hamilton Area

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

Short-term effects of air pollution on daily single- and co-morbidity cardiorespiratory outpatient visits

Several studies have noted that the existence of comorbidities lead to an increase in the risk of premature mortality and morbidity. Most of the studies examining the effects of air pollution on comorbidity visits were from Northern American countries, with scarce literature from Asia. This study contributes to existing, yet limited understanding of air pollution-comorbidity by examining the effects of daily air pollutants on outpatient single morbidity and comorbid cardiorespiratory visits in Japan. A total of 1,452,505 outpatient cardiorespiratory visits were recorded among the 21 Japanese cities from 2013 to 2016. Daily outpatient cardiorespiratory visit data were obtained from a health insurance claims database managed by the Japan Medical Data Center Co., Ltd. (JMDC). A time-stratified case crossover analysis coupled with Generalized Additive Mixed Model was used to analyze the association of daily air pollutants (particulate matter 2.5 μm or less in diameter, ozone and nitrogen dioxide) on daily single (respiratory and cardiovascular) and comorbidity health outcomes. We further examined single and cumulative effects for 0-3 and 0-14 lag periods. Ozone, NO₂, and PM_2.5 were positively associated with cardiorespiratory visits in either shorter or longer lags, with more apparent comorbidity associations with NO₂ exposure. A 10-unit increase in NO₂, after adjusting for ozone, was associated with a 2.24% (95% CI: 1.34-3.15) and 6.49% (95% CI: 5.00-8.01) increase in comorbidity visit at Lag 0 (of Lag 0-3) and cumulative lag 0-3, respectively. Our results contribute to existing evidence suggesting that short-term and extended exposure to air pollution elicit health risks on cardiovascular, respiratory and comorbid clinic visits. Exposure to NO₂, in particular, was associated with increase in the risk of single and comorbidity cardiorespiratory visits. Results can be potentially utilized for both individual health (e.g. risk population health management) and health facility management (e.g. health visit influx determination).

A time-to-event analysis on air pollutants with the risk of cardiovascular disease and mortality: A systematic review and meta-analysis of 84 cohort studies

OBJECTIVE

Air pollution is one of the most substantial problems globally. Aerodynamic toxic of particulate matter with <10 mm in diameter (PM₁₀ ), or <2.5 mm (PM_2.5 ), as well as nitric dioxide (NO₂ ), have been linked with health issues. We aimed to perform a comprehensive analysis of the time-to-event for different types of air pollutants on cardiovascular disease (CVD) events based on cohort studies.

METHODS

A comprehensive search on topics that assesses air pollution and cardiovascular disease with keywords up until July 2019 was performed.

RESULTS

There were a total of 28 215 394 subjects from 84 cohorts. Increased PM_2.5 was associated with composite CVD [HR 1.10 (1.02, 1.19)], acute coronary events [HR 1.15 (1.12, 1.17)], stroke [HR 1.13 (1.06, 1.19)], and hypertension [HR 1.07 (1.01, 1.14)], all-cause mortality [HR 1.07 (1.04, 1.09)], CVD mortality [HR 1.10 (1.07, 1.12)], and ischemic heart disease (IHD) mortality [HR 1.11 (1.07, 1.16)]. Association with AF became significant after removal of a study. Increased PM₁₀ was associated with heart failure [HR 1.25 (1.04, 1.50)], all-cause mortality [HR 1.16 (1.06, 1.27)], CVD mortality [HR 1.17 (1.04, 1.30)], and IHD mortality [HR 1.03 (1.01, 1.05)]. Increased of NO₂ was associated with increased composite CVD [HR 1.15 (1.02, 1.29)], atrial fibrillation [HR 1.01 (1.01, 1.02)], acute coronary events [HR 1.08 (1.02, 1.13)], all-cause mortality [HR 1.23 (1.14, 1.32)], CVD mortality [HR 1.17 (1.10, 1.25)], and IHD mortality [HR 1.05 (1.03, 1.08)].

CONCLUSION

Air pollutants are associated with an increased incidence of cardiovascular diseases, all-cause mortality, and CVD mortality.

Traffic-Related Air Pollution and Carotid Plaque Burden in a Canadian City With Low-Level Ambient Pollution

Background

The association between fine particulate matter and cardiovascular disease has been convincingly demonstrated. The role of traffic‐related air pollutants is less clear. To better understand the role of traffic‐related air pollutants in cardiovascular disease development, we examined associations between NO₂, carotid atherosclerotic plaque, and cardiometabolic disorders associated with cardiovascular disease.

Methods and Results

Cross‐sectional analyses were conducted among 2227 patients (62.9±13.8 years; 49.5% women) from the Stroke Prevention and Atherosclerosis Research Centre (SPARC) in London, Ontario, Canada. Total carotid plaque area measured by ultrasound, cardiometabolic disorders, and residential locations were provided by SPARC medical records. Long‐term outdoor residential NO₂ concentrations were generated by a land use regression model. Associations between NO₂, total carotid plaque area, and cardiometabolic disorders were examined using multiple regression models adjusted for age, sex, smoking, and socioeconomic status. Mean NO₂ was 5.4±1.6 ppb in London, Ontario. NO₂ was associated with a significant increase in plaque (3.4 mm² total carotid plaque area per 1 ppb NO₂), exhibiting a linear dose‐response. NO₂ was also positively associated with triglycerides, total cholesterol, and the ratio of low‐ to high‐density lipoprotein cholesterol (P<0.05). Diabetes mellitus mediated the relationship between NO₂ and total carotid plaque area (P<0.05).

Conclusions

Our results demonstrate that even low levels of traffic‐related air pollutants are linked to atherosclerotic plaque burden, an association that may be partially attributable to pollution‐induced diabetes mellitus. Our findings suggest that reducing ambient concentrations in cities with NO₂ below current standards would result in additional health benefits. Given the billions of people exposed to traffic emissions, our study supports the global public health significance of reducing air pollution.

Quantifying the air quality and health benefits of greening freight movements

Commercial vehicle movements have a large effect on traffic-related air pollution in metropolitan areas. In the Greater Toronto and Hamilton Area (GTHA), commercial vehicles include large and medium diesel trucks as well as light-duty gasoline-fuelled trucks. In this study, the emissions of various air pollutants associated with diesel commercial vehicles were estimated and their impacts on urban air quality, population exposure, and public health were quantified. Using data on diesel trucks in the GTHA and a chemical transport model at a spatial resolution of 1 km², the contribution of commercial diesel movements to air quality was estimated. This contribution amounts to about 6-22% of the mean population exposure to nitrogen dioxide (NO₂) and black carbon (BC), depending on the municipality, but is systematically lower than 3% for fine particulate matter (PM_2.5) and ozone (O₃). Using a comparative risk assessment approach, we estimated that the emissions of all diesel commercial vehicles within the GTHA are responsible for an annual total of at least 9810 Years of Life Lost (YLL), corresponding to $3.2 billion of annual social costs. We also assessed the impact of decreasing freeway-sourced diesel emissions along Highway 401, one of the busiest highways in North America. This is comparable with a removal of 250 to 1000 diesel trucks per day along that corridor, which could be replaced by alternative technologies. The mean NO₂ and BC exposures of the population living within 500 m of the highway would decrease by 9% and 11%, respectively, with reductions as high as 22%. Such a measure would save 1310 YLL annually, equivalent to $428 million in social benefits.

Evaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality

BACKGROUND

Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time-varying exposure data, and proportional weighting of datasets.

METHODS

We used the association between fine particle air pollution (PM_2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables.

RESULTS

At baseline (2001) both cohorts had very similar PM_2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8-2.0 μg/m³ lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM_2.5 mortality hazard ratios of 0.4-0.6% using a static exposure, and 1.7-3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of -0.7 to -1.1% and downward bias of 1.3-2.3% using the static and time-varying approaches respectively.

CONCLUSIONS

The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding.

A comprehensive evaluation of the association between ambient air pollution and adverse health outcomes of major organ systems: a systematic review with a worldwide approach

Ambient air pollution is nowadays one of the most crucial contributors to deteriorating health status worldwide. The components of air pollution include PM_2.5 and PM₁₀, NO₂, SO₂, CO, O₃, and organic compounds. They are attributed to several health outcomes, for instance, cardiovascular diseases (CVD), respiratory diseases, birth outcomes, neurologic diseases, and psychiatric diseases. The objective of this study is to evaluate the association between different ambient air pollutants and the above-mentioned health outcomes. In this systematic review, a total of 76 articles was ultimately selected from 2653 articles, through multiple screening steps by the aid of a set of exclusion criteria as non-English articles, indoor air pollution assessment, work-related, occupational and home-attributed pollution, animal studies, tobacco smoking effects, letters to editors, commentaries, animal experiments, reviews, case reports and case series, out of 19,862 published articles through a systematic search in PubMed, Web of Science, and Scopus. Then, the associations between air pollution and different health outcomes were measured as relative risks and odds ratios. The association between air pollutants, PM_2.5 and PM₁₀, NO₂, SO₂, CO, O₃, and VOC with major organ systems health was investigated through the gathered studies. Relative risks and/or odds ratios attributed to each air pollutant/outcome were ultimately reported. In this study, a thorough and comprehensive discussion of all aspects of the contribution of ambient air pollutants in health outcomes was proposed. To our knowledge up to now, there is no such comprehensive outlook on this issue. Growing concerns in concert with air pollution-induced health risks impose a great danger on the life of billions of people worldwide. Should we propose ideas and schemes to reduce ambient air pollutant, there will be dramatic reductions in the prevalence and occurrence of health-threatening conditions.

Applying the concept of "number needed to treat" to the formulation of daily ambient air quality standards

The World Health Organization sets up the Ambient Air Quality Guidelines mainly based on short-term and long-term health effects of air pollution. Previous studies, however, have generally revealed a non-threshold concentration-response relationship between air pollution and health, making it difficult to determine a concentration, below which no obvious health effects can be observed. Here we proposed a novel approach based on the concept of "number needed to treat", specifically, we calculated the reduction in air pollution concentrations needed to avoid one death corresponding to different hypothetical concentration standards; the one with the smallest value would be the most practical concentration standard. As an example, we applied this approach to the daily standard of ambient PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) in four Chinese cities. The calculation was based on the association between daily mortality and ambient PM_2.5, which was examined by a generalized additive model with adjustment of important covariates. Significant associations were observed between PM_2.5 and mortality. Our analyses suggested that it is appropriate to have 50 μg/m³ as the daily standard of ambient PM_2.5 for the study area, compared to the current standard of which were directly adopted from the national standard of 75 μg/m³. This novel approach should be considered when planning and/or revising the ambient air quality guidelines/standards.

Long-term Concentrations of Nitrogen Dioxide and Mortality: A Meta-analysis of Cohort Studies

Supplemental Digital Content is available in the text.

Background:

Concentrations of outdoor nitrogen dioxide (NO₂) have been associated with increased mortality. Hazard ratios (HRs) from cohort studies are used to assess population health impact and burden. We undertook meta-analyses to derive concentration–response functions suitable for such evaluations and assessed their sensitivity to study selection based upon cohort characteristics.

Methods:

We searched online databases and existing reviews for cohort studies published to October 2016 that reported HRs for NO₂ and mortality. We calculated meta-analytic summary estimates using fixed/random-effects models.

Results:

We identified 48 articles analyzing 28 cohorts. Meta-analysis of HRs found positive associations between NO₂ and all cause (1.02 [95% confidence interval (CI): 1.01, 1.03]; prediction interval [PI]: [0.99, 1.06] per 10 µg/m³ increment in NO₂), cardiovascular (1.03 [95% CI: 1.02, 1.05]; PI: [0.98, 1.08]), respiratory (1.03 [95% CI: 1.01, 1.05]; PI: [0.97, 1.10]), and lung cancer mortality (1.05 [95% CI: 1.02, 1.08]; PI: [0.94, 1.17]) with evidence of substantial heterogeneity between studies. In subgroup analysis, summary HRs varied by age at cohort entry, spatial resolution of pollution estimates, and adjustment for smoking and body mass index at the individual level; for some subgroups, the HR was close to unity, with lower confidence limits below 1.

Conclusions:

Given the many uncertainties inherent in the assessment of this evidence base and the sensitivity of health impact calculations to small changes in the magnitude of the HRs, calculation of the impact on health of policies to reduce long-term exposure to NO₂ should use prediction intervals and report ranges of impact rather than focusing upon point estimates.

Repeated Measures Regression in Laboratory, Clinical and Environmental Research: Common Misconceptions in the Matter of Different Within- and between-Subject Slopes

When using repeated measures linear regression models to make causal inference in laboratory, clinical and environmental research, it is typically assumed that the within-subject association of differences (or changes) in predictor variable values across replicates is the same as the between-subject association of differences in those predictor variable values. However, this is often false. For example, with body weight as the predictor variable and blood cholesterol (which increases with higher body fat) as the outcome: (i) a 10-lb weight increase in the same adult affects more greatly an increase in cholesterol in that adult than does (ii) one adult weighing 10 lbs more than a second indicate higher cholesterol in the heavier adult. A 10-lb weight gain in the first adult more likely reflects a build-up of body fat in that person, while a second person being 10 lbs heavier than the first could be influenced by other factors, such as the second person being taller. Hence, to make causal inferences, different within- and between-subject slopes should be separately modeled. A related misconception commonly made using generalized estimation equations (GEE) and mixed models on repeated measures (i.e., for fitting cross-sectional regression) is that the working correlation structure only influences variance of the parameter estimates. However, only independence working correlation guarantees that the modeled parameters have interpretability. We illustrate this with an example where changing working correlation from independence to equicorrelation qualitatively biases parameters of GEE models and show that this happens because within- and between-subject slopes for the outcomes regressed on the predictor variables differ. We then systematically describe several common mechanisms that cause within- and between-subject slopes to differ: change effects, lag/reverse-lag and spillover causality, shared within-subject measurement bias or confounding, and predictor variable measurement error. The misconceptions we describe should be better publicized. Repeated measures analyses should compare within- and between-subject slopes of predictors and when they do differ, investigate the causal reasons for this.

Causality Advocacy: Workers' Compensation Cases as Resources for Identifying and Preventing Diseases of Modernity

An appeal process for an injured worker compensation case is a unique opportunity to debate and integrate evidence concerning a potential causal relationship between observations of occupational disease and exposures to various putative risk factors that may also be of significance in public health protection. Through application of Hill's indicia to the evidence presented in a recent appeal process concerning a breast cancer case for a female border guard, a novel epidemic, tentatively called "occupational BRCAness" has been identified and a causal relationship with exposures to traffic-related air pollution and shift work and possibly secondhand tobacco smoke is inferred. Application of the audit method by worker advocates to other compensation appeals processes for other diseases might similarly yield causal relations with exposures to occupational risk factors with relevance to public health.

Air Pollution and Cardiovascular Disease: A Focus on Vulnerable Populations Worldwide

Purpose of review

Certain subgroups defined by sociodemographics (race/ethnicity, age, sex and socioeconomic status [SES]), geographic location (rural vs. urban), comorbid conditions and country economic conditions (developed vs. developing) may disproportionately suffer the adverse cardiovascular effects of exposure to ambient air pollution. Yet, previous reviews have had a broad focus on the general population without consideration of these potentially vulnerable populations.

Recent findings

Over the past decade, a wealth of epidemiologic studies have linked air pollutants including particulate matter, oxides of nitrogen, and carbon monoxide to cardiovascular disease (CVD) risk factors, subclinical CVD, clinical cardiovascular outcomes and cardiovascular mortality in certain susceptible populations. Highest risk for poor CVD outcomes from air pollution exist in racial/ethnic minorities, especially in blacks compared to whites in the U.S, those at low SES, elderly populations, women, those with certain comorbid conditions and developing countries compared to developed countries. However, findings are less consistent for urban compared to rural populations.

Summary

Vulnerable subgroups including racial/ethnic minorities, women, the elderly, smokers, diabetics and those with prior heart disease had higher risk for adverse cardiovascular outcomes from exposure to air pollution. There is limited data from developing countries where concentrations of air pollutants are more extreme and cardiovascular event rates are higher than that of developed countries. Further epidemiologic studies are needed to understand and address the marked disparities in CVD risk conferred by air pollution globally, particularly among these vulnerable subgroups.

The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health

BACKGROUND

Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

METHODS

We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

DISCUSSION

CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.

Does Urban Form Affect Urban NO2? Satellite-Based Evidence for More than 1200 Cities

Modifying urban form may be a strategy to mitigate urban air pollution. For example, evidence suggests that urban form can affect motor vehicle usage, a major contributor to urban air pollution. We use satellite-based measurements of urban form and nitrogen dioxide (NO₂) to explore relationships between urban form and air pollution for a global data  set of 1274 cities. Three of the urban form metrics studied (contiguity, circularity, and vegetation) have a statistically significant relationship with urban NO₂; their combined effect could be substantial. As illustration, if findings presented here are causal, that would suggest that if Christchurch, New Zealand (a city at the 75th percentile for all three urban-form metrics, and with a network of buses, trams, and bicycle facilities) was transformed to match the urban form of Indio - Cathedral City, California, United States (a city at the 25th percentile for those same metrics, and exhibiting sprawl-like suburban development), our models suggest that Christchurch's NO₂ concentrations would be ∼60% higher than its current level. We also find that the combined effect of urban form on NO₂ is larger for small cities (β × IQR = -0.46 for cities < ∼300 000 people, versus -0.22 for all cities), an important finding given that cities less than 500 000 people contain a majority of the urban population and are where much of the future urban growth is expected to occur. This work highlights the need for future study of how changes in urban form and related land use and transportation policies impact urban air pollution, especially for small cities.

Urban greenness and mortality in Canada's largest cities: a national cohort study

BACKGROUND

Findings from published studies suggest that exposure to and interactions with green spaces are associated with improved psychological wellbeing and have cognitive, physiological, and social benefits, but few studies have examined their potential effect on the risk of mortality. We therefore undertook a national study in Canada to examine associations between urban greenness and cause-specific mortality.

METHODS

We used data from a large cohort study (the 2001 Canadian Census Health and Environment Cohort [2001 CanCHEC]), which consisted of approximately 1·3 million adult (aged ≥19 years), non-immigrant, urban Canadians in 30 cities who responded to the mandatory 2001 Statistics Canada long-form census. The cohort has been linked by Statistics Canada to the Canadian mortality database and to annual income tax filings through 2011. We measured greenness with images from the moderate-resolution imaging spectroradiometer from NASA's Aqua satellite. We assigned estimates of exposure to greenness derived from remotely sensed Normalized Difference Vegetation Index (NDVI) within both 250 m and 500 m of participants' residences for each year during 11 years of follow-up (between 2001 and 2011). We used Cox proportional hazards models to estimate associations between residential greenness (as a continuous variable) and mortality. We estimated hazard ratios (HRs) and corresponding 95% CIs per IQR (0·15) increase in NDVI adjusted for personal (eg, education and income) and contextual covariates, including exposures to fine particulate matter, ozone, and nitrogen dioxide. We also considered effect modification by selected personal covariates (age, sex, household income adequacy quintiles, highest level of education, and marital status).

FINDINGS

Our cohort consisted of approximately 1 265 000 individuals at baseline who contributed 11 523 770 person-years. We showed significant decreased risks of mortality in the range of 8-12% from all causes of death examined with increased greenness around participants' residence. In the fully adjusted analyses, the risk was significantly decreased for all causes of death (non-accidental HR 0·915, 95% CI 0·905-0·924; cardiovascular plus diabetes 0·911, 0·895-0·928; cardiovascular 0·911, 0·894-0·928; ischaemic heart disease 0·904, 0·882-0·927; cerebrovascular 0·942, 0·902-0·983; and respiratory 0·899, 0·869-0·930). Greenness associations were more protective among men than women (HR 0·880, 95% CI 0·868-0·893 vs 0·955, 0·941-0·969), and among individuals with higher incomes (highest quintile 0·812, 0·791-0·834 vs lowest quintile 0·991, 0·972-1·011) and more education (degree or more 0·816, 0·791-0·842 vs did not complete high school 0·964, 0·950-0·978).

INTERPRETATION

Increased amounts of residential greenness were associated with reduced risks of dying from several common causes of death among urban Canadians. We identified evidence of inequalities, both in terms of exposures to greenness and mortality risks, by personal socioeconomic status among individuals living in generally similar environments, and with reasonably similar access to health care and other social services. The findings support the development of policies related to creating greener and healthier cities.

FUNDING

None.

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO₂ exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO₂) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO₂ variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R² = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R² within 2%) but not for Africa and Oceania (adjusted R² within 11%) where NO₂ monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO₂ concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO₂ were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO₂ monitoring data or models.

Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting Cities

BACKGROUND

Providing infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied.

OBJECTIVES

Our goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment.

METHODS

We employed facility-demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600-1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., "walkability" or "bikeability") to assess exposure.

RESULTS

Active travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20-42% of active travel occurs on blocks with high population-level exposure). Only 2-3% of blocks (3-8% of total active travel) are "sweet spots" (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use.

CONCLUSIONS

Public health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.

Maternal exposure to ambient air pollution and risk of early childhood cancers: A population-based study in Ontario, Canada

BACKGROUND

There are increasing concerns regarding the role of exposure to ambient air pollution during pregnancy in the development of early childhood cancers.

OBJECTIVE

This population based study examined whether prenatal and early life (<1year of age) exposures to ambient air pollutants, including nitrogen dioxide (NO₂) and particulate matter with aerodynamic diameters ≤2.5μm (PM_2.5), were associated with selected common early childhood cancers in Canada.

METHODS

2,350,898 singleton live births occurring between 1988 and 2012 were identified in the province of Ontario, Canada. We assigned temporally varying satellite-derived estimates of PM_2.5 and land-use regression model estimates of NO₂ to maternal residences during pregnancy. Incident cases of 13 subtypes of pediatric cancers among children up to age 6 until 2013 were ascertained through administrative health data linkages. Associations of trimester-specific, overall pregnancy and first year of life exposures were evaluated using Cox proportional hazards models, adjusting for potential confounders.

RESULTS

A total of 2044 childhood cancers were identified. Exposure to PM_2.5, per interquartile range increase, over the entire pregnancy, and during the first trimester was associated with an increased risk of astrocytoma (hazard ratio (HR) per 3.9μg/m3=1.38 (95% CI: 1.01, 1.88) and, HR per 4.0μg/m³=1.40 (95% CI: 1.05-1.86), respectively). We also found a positive association between first trimester NO₂ and acute lymphoblastic leukemia (ALL) (HR=1.20 (95% CI: 1.02-1.41) per IQR (13.3ppb)).

CONCLUSIONS

In this population-based study in the largest province of Canada, results suggest an association between exposure to ambient air pollution during pregnancy, especially in the first trimester and an increased risk of astrocytoma and ALL. Further studies are required to replicate the findings of this study with adjustment for important individual-level confounders.

Acute Effects of Nitrogen Dioxide on Cardiovascular Mortality in Beijing: An Exploration of Spatial Heterogeneity and the District-specific Predictors

The exploration of spatial variation and predictors of the effects of nitrogen dioxide (NO₂) on fatal health outcomes is still sparse. In a multilevel case-crossover study in Beijing, China, we used mixed Cox proportional hazard model to examine the citywide effects and conditional logistic regression to evaluate the district-specific effects of NO₂ on cardiovascular mortality. District-specific predictors that could be related to the spatial pattern of NO₂ effects were examined by robust regression models. We found that a 10 μg/m³ increase in daily mean NO₂ concentration was associated with a 1.89% [95% confidence interval (CI): 1.33–2.45%], 2.07% (95% CI: 1.23–2.91%) and 1.95% (95% CI: 1.16–2.72%) increase in daily total cardiovascular (lag03), cerebrovascular (lag03) and ischemic heart disease (lag02) mortality, respectively. For spatial variation of NO₂ effects across 16 districts, significant effects were only observed in 5, 4 and 2 districts for the above three outcomes, respectively. Generally, NO₂ was likely having greater adverse effects on districts with larger population, higher consumption of coal and more civilian vehicles. Our results suggested independent and spatially varied effects of NO₂ on total and subcategory cardiovascular mortalities. The identification of districts with higher risk can provide important insights for reducing NO₂ related health hazards.

Inhaling Benzene, Toluene, Nitrogen Dioxide, and Sulfur Dioxide, Disrupts Thyroid Function in Captive American Kestrels (Falco sparverius)

Research investigating the effects of air contaminants on biota has been limited to date. Captive adult female American kestrels (Falco sparverius) were exposed to a mixture of benzene (0.6 ppm), toluene (1 ppm), nitrogen dioxide (NO₂; 2 ppm) and sulfur dioxide (SO₂; 5.6 ppm), in a whole-body inhalation chamber. Thyroid axis responses to meet metabolic demands were examined through thyroid histology, plasma thyroxine (T₄), and triiodothyronine (T₃), and hepatic outer ring deiodination (T₄-ORD). Plasma free (F) T₃ and T₄ were measured at baseline, and at 9 days and 18 days of exposure, whereas total (T) T₃ and TT₄, thyroid histology and hepatic T₄-ORD were determined at the final 18 day exposure. Inhalation of these contaminants significantly suppressed plasma FT₄ and TT₄, and depleted follicular colloid and increased epithelial cell height at 18 days, and significantly altered the temporal pattern of plasma FT₄. Significant histological changes in the follicular colloid:epithelial cell height ratio indicated sustained T₄ production and release by the thyroid glands. There was no effect on plasma FT₃, TT₃, or hepatic T₄-ORD. We hypothesize that contaminant-related activation of the hypothalamus-pituitary-thyroid axis in the kestrels increased elimination of plasma T₄ through Phase II enzymes. Further research is required to test this hypothesis in wild birds.

Long-term exposure to fine particulate matter air pollution and the risk of lung cancer among participants of the Canadian National Breast Screening Study

Recently, air pollution has been classified as a carcinogen largely on the evidence of epidemiological studies of lung cancer. However, there have been few prospective studies that have evaluated associations between fine particulate matter (PM2.5 ) and cancer at lower concentrations. We conducted a prospective analysis of 89,234 women enrolled in the Canadian National Breast Screening Study between 1980 and 1985, and for whom residential measures of PM2.5 could be assigned. The cohort was linked to the Canadian Cancer Registry to identify incident lung cancers through 2004. Surface PM2.5 concentrations were estimated using satellite data. Cox proportional hazards models were used to characterize associations between PM2.5 and lung cancer. Hazard ratios (HRs) and 95% confidence intervals (CIs) computed from these models were adjusted for several individual-level characteristics, including smoking. The cohort was composed predominantly of Canadian-born (82%), married (80%) women with a median PM2.5 exposure of 9.1 µg/m(3) . In total, 932 participants developed lung cancer. In fully adjusted models, a 10 µg/m(3) increase in PM2.5 was associated with an elevated risk of lung cancer (HR: 1.34; 95% CI = 1.10, 1.65). The strongest associations were observed with small cell carcinoma (HR: 1.53; 95% CI = 0.93, 2.53) and adenocarcinoma (HR: 1.44; 95% CI = 1.06, 1.97). Stratified analyses suggested increased PM2.5 risks were limited to those who smoked cigarettes. Our findings are consistent with previous epidemiological investigations of long-term exposure to PM2.5 and lung cancer. Importantly, they suggest associations persist at lower concentrations such as those currently found in Canadian cities.

Spatial associations between socioeconomic groups and NO2 air pollution exposure within three large Canadian cities

Previous studies of environmental justice in Canadian cities have linked lower socioeconomic status to greater air pollution exposures at coarse geographic scales, (i.e., Census Tracts). However, studies that examine these associations at finer scales are less common, as are comparisons among cities. To assess differences in exposure to air pollution among socioeconomic groups, we assigned estimates of exposure to ambient nitrogen dioxide (NO2), a marker for traffic-related pollution, from city-wide land use regression models to respondents of the 2006 Canadian census long-form questionnaire in Toronto, Montreal, and Vancouver. Data were aggregated at a finer scale than in most previous studies (i.e., by Dissemination Area (DA), which includes approximately 400-700 persons). We developed simultaneous autoregressive (SAR) models, which account for spatial autocorrelation, to identify associations between NO2 exposure and indicators of social and material deprivation. In Canada's three largest cities, DAs with greater proportions of tenants and residents who do not speak either English or French were characterised by greater exposures to ambient NO2. We also observed positive associations between NO2 concentrations and indicators of social deprivation, including the proportion of persons living alone (in Toronto), and the proportion of persons who were unmarried/not in a common-law relationship (in Vancouver). Other common measures of deprivation (e.g., lone-parent families, unemployment) were not associated with NO2 exposures. DAs characterised by selected indicators of deprivation were associated with higher concentrations of ambient NO2 air pollution in the three largest cities in Canada.

Risk estimates of mortality attributed to low concentrations of ambient fine particulate matter in the Canadian community health survey cohort

BACKGROUND

Understanding the shape of the relationship between long-term exposure to ambient fine particulate matter (PM2.5) concentrations and health risks is critical for health impact and risk assessment. Studies evaluating the health risks of exposure to low concentrations of PM2.5 are limited. Further, many existing studies lack individual-level information on potentially important behavioural confounding factors.

METHODS

A prospective cohort study was conducted among a subset of participants in a cohort that linked respondents of the Canadian Community Health Survey to mortality (n = 299,500) with satellite-derived ambient PM2.5 estimates. Participants enrolled between 2000 and 2008 were followed to date of death or December 31, 2011. Cox proportional hazards models were used to estimate hazard ratios (HRs) for mortality attributed to PM2.5 exposure, adjusted for individual-level and contextual covariates, including smoking behaviour and body mass index (BMI).

RESULTS

Approximately 26,300 non-accidental deaths, of which 32.5 % were due to circulatory disease and 9.1 % were due to respiratory disease, occurred during the follow-up period. Ambient PM2.5 exposures were relatively low (mean = 6.3 μg/m(3)), yet each 10 μg/m(3) increase in exposure was associated with increased risks of non-accidental (HR = 1.26; 95 % CI: 1.19-1.34), circulatory disease (HR = 1.19; 95 % CI: 1.07-1.31), and respiratory disease mortality (HR = 1.52; 95 % CI: 1.26-1.84) in fully adjusted models. Higher hazard ratios were observed for respiratory mortality among respondents who never smoked (HR = 1.97; 95 % CI: 1.24-3.13 vs. HR = 1.45; 95 % CI: 1.17-1.79 for ever smokers), and among obese (BMI ≥ 30) respondents (HR = 1.76; 95 % CI: 1.15-2.69 vs. HR = 1.41; 95 % CI: 1.04-1.91 for normal weight respondents), though differences between groups were not statistically significant. A threshold analysis for non-accidental mortality estimated a threshold concentration of 0 μg/m(3) (+95 % CI = 4.5 μg/m(3)).

CONCLUSIONS

Increased risks of non-accidental, circulatory, and respiratory mortality were observed even at very low concentrations of ambient PM2.5. HRs were generally greater than most literature values, and adjusting for behavioural covariates served to reduce HR estimates slightly.

A New Method to Jointly Estimate the Mortality Risk of Long-Term Exposure to Fine Particulate Matter and its Components

Most studies on the association between exposure to fine particulate matter (PM2.5) and mortality have considered only total concentration of PM2.5 or individual components of PM2.5, and not the combined effects of concentration and particulate composition. We sought to develop a method to estimate the risk of death from long-term exposure to PM2.5 and the distribution of its components, namely: sulphate, nitrate, ammonium, organic mass, black carbon, and mineral dust. We decomposed PM2.5 exposure into the sum of total concentration and the proportion of each component. We estimated the risk of death due to exposure using a cohort of ~2.4 million Canadians who were followed for vital status over 16 years. Modelling the concentration of PM2.5 with the distribution of the proportions of components together was a superior predictor for mortality than either total PM2.5 concentration alone, or all component concentrations modelled together. Our new approach has the advantage of characterizing the toxicity of the atmosphere in its entirety. This is required to fully understand the health benefits associated with strategies to improve air quality that may result in complex changes not only in PM2.5 concentration, but also in the distribution of particle components.

National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010

Land-use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically for long-term averages. Here we present NO2 surfaces for the continental United States with excellent spatial resolution (∼100 m) and monthly average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model [WRF-Chem]), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas, monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO2 measurements from U.S. EPA monitors (2000-2010) to build a spatial LUR and to calculate spatially varying temporal scaling factors. The model captures 82% of the spatial and 76% of the temporal variability (population-weighted average) of monthly mean NO2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error 2-3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than far from monitors (mean bias 3% versus 40%) and is better for urban and suburban locations (1-6%) than for rural locations (78%, reflecting the relatively clean conditions in many rural areas). During 2000-2010, population-weighted mean NO2 exposure decreased 42% (1.0 ppb [∼5.2%] per year), from 23.2 ppb (year 2000) to 13.5 ppb (year 2010). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO2 concentration estimates.

Comparing individual- and area-based socioeconomic measures for the surveillance of health disparities: A multilevel analysis of Massachusetts births, 1989-1991

The absence of individual-level socioeconomic information in most US health surveillance data necessitates using area-based socioeconomic measures (ABSMs) to monitor health inequalities. Using the 1989-1991 birth weight data from Massachusetts, the authors compared estimates of health disparities detected with census tract- and block group-level ABSMs pertaining to poverty and education, as well as parental education, both independently and together. In separate models, adjusted for infant's sex, mother's age, and parents' race/ethnicity, worst-off categories of census tract ABSMs and parental education had a comparable birth weight deficit of approximately 70 g. Similar results were observed for low birth weight (<2,500 g), with worst-off categories of census tract ABSMs and parental education having an odds ratio of approximately 1.37 (p < 0.001). In mutually adjusted models for birth weight and low birth weight, census tract ABSMs still detected an effect estimate nearly 50% of that detected by parental education. Additionally, census tract ABSMs detected socioeconomic gradients in birth weight among births to mothers aged less than 25 years, an age group in which educational attainment is unlikely to be completed. These results suggest that aptly chosen ABSMs can be used to monitor socioeconomic inequalities in health. The risk, if any, in the absence of individual-level socioeconomic information is a conservative estimate of socioeconomic inequalities in health.