During the past decade several studies have observed that increased vegetation or ‘greenness’ around mothers’ residences is associated with healthier birth weights. However, for the most part, these studies did not take into account the potential confounding role of air pollution, nor evaluated other birth outcomes. We examined associations between pregnancy outcomes and measures of residential greenness among 2.2 million singleton live births among all census metropolitan areas in Canada between 1999 and 2008. Pregnancy outcomes included: preterm birth, small for gestational age, and birth weight. Environmental exposures to greenness, fine particulate matter, and nitrogen dioxide were assigned to the postal code of place of residence of the mother at the time of birth. Exposure to greenness was based on the Normalized Difference Vegetation Index, while estimates of PM2.5 and NO2were estimated using remote sensing, and a national land-use regression model, respectively. Multivariable regressions were conducted to characterize associations between residential greenness and the birth outcomes, and stratified analyses done by neighbourhood measures of socio-economic status. Mothers’ who lived in greener areas had lower risks of preterm birth, low birth weight and small for gestational age. These associations persisted after adjustment for ambient NO2 and PM2.5. Specifically, in fully adjusted models, an interquartile range (IQR=0.16) increase in greenness yielded odds ratios of 0.93 (95% CI= 0.92, 0.94), 0.94 (95% CI= 0.92, 0.95), and 0.94 (95% CI=0.93, 0.95) for preterm birth, low birth weight, and small for gestational age respectively. Similarly, an IQR increase in greenness was also associated with a 16.3g (95%=CI 15.3, 17.4) increase in birthweight. Stratified analyses by neighbourhood income indicated that there were beneficial impacts of residential greenness on birth outcomes across all income categories. The findings of this population-based national study support the hypothesis that residential proximity to vegetation contributes to healthier pregnancies.

Numerous studies have examined the association of air pollution with preterm birth and birth weight outcomes. Traffic-related air pollution has also increasingly been identified as an important contributor to adverse health effects of air pollution. We employed a national nitrogen dioxide (NO2) exposure model to examine the association between NO2 and pregnancy outcomes in Canada between 1999 and 2008. National models for NO2 (and particulate matter of median aerodynamic diameter <2.5µm (PM2.5) as a covariate) were developed using ground-based monitoring data, estimates from remote-sensing, land use variables and, for NO2, deterministic gradients relative to road traffic sources. Generalized estimating equations were used to examine associations with preterm birth, term low birth weight (LBW), small for gestational age (SGA) and term birth weight, adjusting for covariates including infant sex, gestational age, maternal age and marital status, parity, urban/rural place of residence, maternal place of birth, season, year of birth and neighbourhood socioeconomic status and per cent visible minority. Associations were reduced considerably after adjustment for individual covariates and neighbourhood per cent visible minority, but remained significant for SGA (odds ratio 1.04, 95%CI 1.02–1.06 per 20ppb NO2) and term birth weight (16.2g reduction, 95% CI 13.6–18.8g per 20ppb NO2). Associations with NO2 were of greater magnitude in a sensitivity analysis using monthly monitoring data, and among births to mothers born in Canada, and in neighbourhoods with higher incomes and a lower proportion of visible minorities. In two pollutant models, associations with NO2 were less sensitive to adjustment for PM2.5 than vice versa, and there was consistent evidence of a dose-response relationship for NO2 but not PM2.5. In this study of approximately 2.5 million Canadian births between 1999 and 2008, we found significant associations of NO2 with SGA and term birth weight which remained significant after adjustment for PM2.5, suggesting that traffic may be a particularly important source with respect to the role of air pollution as a risk factor for adverse pregnancy outcomes.

BACKGROUND:Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality. OBJECTIVES:We compared relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information. METHODS:We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM2.5 with circulatory mortality and ischemic heart disease. RESULTS:Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m3 increment in PM2.5]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m3 increment in PM2.5). CONCLUSIONS:We found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Introduction: Associations between long-term exposure to ambient fine particulate matter air pollution (PM2.5) and cardiovascular mortality are clearly established in the literature. However, further work is needed to investigate possible joint effects of PM2.5 and cigarette smoking for the disease. We recently reported a greater-than-additive relationship between PM2.5 and cigarette smoking for lung cancer mortality in the American Cancer Society Cancer Prevention Study-II (CPS-II) and extend this work to mortality from circulatory diseases here. Methods: Included in this analysis were 429,406 current or never cigarette smokers among whom 32,354 died of circulatory disease during 22 years of follow-up (1982-2004). We estimated PM2.5 concentrations from a national hybrid land use-kriging interpolation model for the years 1999-2004. We fit Cox proportional hazards regression models to estimate mortality associations and interactions on both the additive and multiplicative scales. Results: The hazard ratio (HR) (95% confidence interval (CI)) for high vs. low PM2.5 exposure (according to the 75th and 25th percentile) was 1.10 (95% CI 1.07-1.13) in never smokers. The HR for cigarette smoking was 1.89 (95% CI 1.82-1.96) in those with low PM2.5. The HR for both high PM2.5 and cigarette smoking was 2.10 (95% CI 2.02-2.17). There was a significant relative excess risk due to interaction (RERI) of 0.11 (95% CI 0.03-0.19) observed. There was a stronger RERI for mortality from dysrhythmias, heart failure, cardiac arrest more specifically (RERI = 0.20, 95% CI 0.01-0.40). Results using an additive hazards model will also be presented. Conclusions: There was some evidence for a small additive interaction between PM2.5 and cigarette smoking for circulatory mortality.

Traffic-related air pollution (TRAP) likely exerts a large burden of disease globally, and in many places, traffic is increasing dramatically. The impact, however, of urban form on the portion of population potentially exposed to TRAP remains poorly understood. In this study, we estimate portions of population potentially exposed to TRAP across seven global cities of various urban forms. Data on population distributions and road networks were collected from the best available sources in each city and from remote sensing analysis. Using spatial mapping techniques, we first overlaid road buffers onto population data to estimate the portions of population potentially exposed for four plausible impact zones. Based on a most likely scenario with impacts from highways up to 300meters and major roadways up to 50meters, we identified that the portions of population potentially exposed for the seven cities ranged from 23 to 96%. High-income North American cities had the lowest potential exposure portions, while those in Europe had the highest. Second, we adjusted exposure zone concentration levels based on a literature suggested multiplier for each city using corresponding background concentrations. Though Beijing and Mexico City did not have the highest portion of population exposure, those in their exposure zones had the highest levels of exposure. For all seven cities, the portion of population potentially exposed was positively correlated with roadway density and, to a lesser extent, with population density. These analyses suggest that urban form may influence the portion of population exposed to TRAP and vehicle emissions and other factors may influence the exposure levels. Greater understanding of urban form and other factors influencing potential exposure to TRAP may help inform interventions that protect public health.

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Background Numerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors. Objectives We examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas. Methods Analyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother’s postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth (gestational age < 37 weeks), term low birth weight (< 2,500 g), small for gestational age (SGA; < 10th percentile of birth weight for gestational age), and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES). Results In fully adjusted models, a 10-μg/m3 increase in PM2.5 over the entire pregnancy was associated with SGA (odds ratio = 1.04; 95% CI 1.01, 1.07) and reduced term birth weight (–20.5 g; 95% CI –24.7, –16.4). Associations varied across subgroups based on maternal place of birth and period (1999–2003 vs. 2004–2008). Conclusions This study, based on approximately 3 million births across Canada and employing PM2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes. Citation Stieb DM, Chen L, Beckerman BS, Jerrett M, Crouse DL, Omariba DW, Peters PA, van Donkelaar A, Martin RV, Burnett RT, Gilbert NL, Tjepkema M, Liu S, Dugandzic RM. 2016. Associations of pregnancy outcomes and PM2.5 in a National Canadian Study. Environ Health Perspect 124:243–249; http://dx.doi.org/10.1289/ehp.1408995

RATIONALE:Tropospheric ozone (O3) is potentially associated with cardiovascular disease risk and premature death. Results from long-term epidemiological studies on O3 are scarce and inconclusive.OBJECTIVES:In this study, we examined associations between chronic ambient O3 exposure and all-cause and cause-specific mortality in a large cohort of U.S. adults.METHODS:Cancer Prevention Study II participants were enrolled in 1982. A total of 669,046 participants were analyzed, among whom 237,201 deaths occurred through 2004. We obtained estimates of O3 concentrations at the participant's residence from a hierarchical Bayesian space-time model. Estimates of fine particulate matter (particulate matter with an aerodynamic diameter of up to 2.5 μm [PM2.5]) and NO2 concentrations were obtained from land use regression. Cox proportional hazards regression models were used to examine mortality associations adjusted for individual- and ecological-level covariates.MEASUREMENTS AND MAIN RESULTS:In single-pollutant models, we observed significant positive associations between O3, PM2.5, and NO2 concentrations and all-cause and cause-specific mortality. In two-pollutant models adjusted for PM2.5, significant positive associations remained between O3 and all-cause (hazard ratio [HR] per 10 ppb, 1.02; 95% confidence interval [CI], 1.01-1.04), circulatory (HR, 1.03; 95% CI, 1.01-1.05), and respiratory mortality (HR, 1.12; 95% CI, 1.08-1.16) that were unchanged with further adjustment for NO2. We also observed positive mortality associations with both PM2.5 (both near source and regional) and NO2 in multipollutant models.CONCLUSIONS:Findings derived from this large-scale prospective study suggest that long-term ambient O3 contributes to risk of respiratory and circulatory mortality. Substantial health and environmental benefits may be achieved by implementing further measures aimed at controlling O3 concentrations.

The International Agency for Research on Cancer recently classified outdoor air pollution and airborne particulate matter as carcinogenic to humans. However, there are gaps in the epidemiologic literature, including assessment of possible joint effects of cigarette smoking and fine particulate matter (particulate matter less than or equal to 2.5 µm in diameter) on lung cancer risk. We present estimates of interaction on the additive scale between these risk factors from Cancer Prevention Study II, a large prospective US cohort study of nearly 1.2 million participants recruited in 1982. Estimates of the relative excess risk of lung cancer mortality due to interaction, the attributable proportion due to interaction, and the synergy index were 2.19 (95% confidence interval (CI): -0.10, 4.83), 0.14 (95% CI: 0.00, 0.25), and 1.17 (95% CI: 1.00, 1.37), respectively, using the 25th and 75th percentiles as cutpoints for fine particulate matter. This suggests small increases in lung cancer risk among persons with both exposures beyond what would be expected from the sum of the effects of the individual exposures alone. Although reductions in cigarette smoking will achieve the greatest impact on lung cancer rates, these results suggest that attempted reductions in lung cancer risk through both tobacco control and air quality management may exceed expectations based on reducing exposure to either risk factor alone.

Nitrogen dioxide (NO2), a surrogate measure of traffic-related air pollution (TRAP), has been associated with incident childhood asthma. Timing of exposure and atopic status may be important effect modifiers. We collected cross-sectional data on asthma outcomes from Toronto school children aged 5–9years in 2006. Lifetime home, school and daycare addresses were obtained to derive birth and cumulative NO2 exposures for a nested case–control subset of 1497 children. Presence of other allergic disease (a proxy for atopy) was defined as self-report of one or more of doctor-diagnosed rhinitis, eczema, or food allergy. Generalized estimating equations were used to adjust for potential confounders, and examine hypothesized effect modifiers while accounting for clustering by school. In children with other allergic disease, birth, cumulative and 2006 NO2 were associated with lifetime asthma (OR 1.46, 95% CI 1.08–1.98; 1.37, 95% CI 1.00–1.86; and 1.60, 95% CI 1.09–2.36 respectively per interquartile range increase) and wheeze (OR 1.44, 95% CI 1.10–1.89; 1.31, 95% CI 1.02–1.67; and 1.60, 95% CI 1.16–2.21). No or weaker effects were seen in those without allergic disease, and effect modification was amplified when a more restrictive algorithm was used to define other allergic disease (at least 2 of doctor diagnosed allergic rhinitis, eczema or food allergy). The effects of modest NO2 levels on childhood asthma were modified by the presence of other allergic disease, suggesting a probable role for allergic sensitization in the pathogenesis of TRAP initiated asthma.

Background and Aims: Remote sensing (RS) and atmospheric transport (AT) modeling are increasingly used for exposure assessment in epidemiological and health burden studies. Here we aimed to assess whether modeled estimates of fine particulate matter from RS and AT differed from models based only on ground information in the health risks associated with exposure. Methods: We geocoded the baseline residential address of 669,047 American Cancer Society Cancer Prevention II (ACS CPS-II) Cohort subjects and assigned several exposure models to them. These estimates included a PM2.5 model based on RS (resolution ~ 9.8 km girds), an Environmental Protection Agency Hierarchical Bayesian Model (HBM) that combines atmospheric chemistry models with ground observations (~36 km), a gesotatistical kriging model based on ground observations (~ 9.8 km), a hybrid land use regression model using only ground-based inputs, and another land use regression model that combined ground-based information with the RS (<100 m) — all assigned to the geocoded point. We used Cox proportional hazards regression to examine associations with mortality from diseases of the circulatory system (plus diabetes) while controlling for likely confounders (e.g., smoking history). Follow up went from 1982 to 2004. Exposure models were averaged for the years 2002-2004. Results: Relative risks are reported over a 10 ug/m3 exposure increment. Health effects differed between the models, with the smallest risks being observed for the remote sensing models (relative risks or RR = 1.05, 95% CI: 1.03-1.07) and the EPA model (RR = 1.09, 95% CI: 1.06-1.11). Results from the geostatistical model and the hybrid land use regression that included RS information were larger and similar to each other (significant RR = 1.12). The largest risks were observed for the land use regression model that included only ground-based information and whose most significant predictor was traffic within 1 km of monitor (RR = 1.15, 95% CI: 1.13-1.18). Conclusion: Those exposure estimates using ground-based information had larger risks than either the RS or the AT models, which both predicted over relatively large areas. Smaller effects from these models may have resulted from their inability to detect fine-scale variations from emission sources such as traffic.

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Background. Laboratory evidence suggests that fine particulate matter PM2.5 may contribute to the genesis of chronic conditions like diabetes and hypertension. Although air pollution has been associated with the prevalence of these conditions, the few epidemiologic data on its relation to incident diabetes and hypertension have been inconsistent. Aims. We assessed the association of ambient PM2.5 and incident type 2 diabetes and hypertension in the Black Women's Health Study (BWHS), a follow-up study of African American women. Methods. BWHS participants reported incident diabetes and hypertension on biennial questionnaires from 1995-2011. We estimated long-term average PM2.5 levels at participant addresses from 1998-2009 in 56 cities across the U.S. using a hybrid model combining land use regression and Bayesian Maximum Entropy kriging. We used Cox regression models to estimate city-specific hazard ratios (HR) associated with a 1.75 µg/m3 increase in PM2.5, the mean interquartile range in the major cities. City-specific HRs were then pooled in a summary estimate. Results. Of 33,621 women free of diabetes in 1995, 4078 developed diabetes over 16 years of follow-up; of 32,638 hypertension-free women in 1995, 9588 developed hypertension. The age-adjusted HR for diabetes was 1.09 (95% CI 1.03-1.15). After control for age, body mass index, and neighborhood socioeconomic status, it was 1.02 (95% CI 0.96-1.08). For hypertension, the respective HRs were 1.03 (95% CI 1.00-1.07) and 0.98 (95% CI 0.95-1.02). Conclusions. These results do not provide evidence of an association between PM2.5 and incident diabetes or hypertension in black women. Future work will assess the effects of traffic-related air pollutants, which were previously associated with incident diabetes in BWHS participants living in Los Angeles (Coogan et al., Circulation 2012;125:767-772).

Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts.

RATIONALE Although substantial scientific evidence suggests that chronic exposure to ambient air pollution contributes to premature mortality, uncertainties exist in the size and consistency of this association. Uncertainty may arise from inaccurate exposure assessment. OBJECTIVES To assess the associations of three types of air pollutants (fine particulate matter, ozone [O3], and nitrogen dioxide [NO2]) with the risk of mortality in a large cohort of California adults using individualized exposure assessments. METHODS For fine particulate matter and NO2, we used land use regression models to derive predicted individualized exposure at the home address. For O3, we estimated exposure with an inverse distance weighting interpolation. Standard and multilevel Cox survival models were used to assess the association between air pollution and mortality. MEASUREMENTS AND MAIN RESULTS Data for 73,711 subjects who resided in California were abstracted from the American Cancer Society Cancer Prevention II Study cohort, with baseline ascertainment of individual characteristics in 1982 and follow-up of vital status through to 2000. Exposure data were derived from government monitors. Exposure to fine particulate matter, O3, and NO2 was positively associated with ischemic heart disease mortality. NO2 (a marker for traffic pollution) and fine particulate matter were also associated with mortality from all causes combined. Only NO2 had significant positive association with lung cancer mortality. CONCLUSIONS Using the first individualized exposure assignments in this important cohort, we found positive associations of fine particulate matter, O3, and NO2 with mortality. The positive associations of NO2 suggest that traffic pollution relates to premature death.

Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO2], fine particulate matter [PM2.5], and ozone [O3]) in a population of susceptible subjects in Toronto. Local (NO2) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM2.5, O3) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412–414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO2 was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM2.5 and O3 were not associated with risk of IHD.

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO2) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO2 levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO2 model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO2 and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.