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

The impact of long-term PM2.5 exposure on specific causes of death: exposure-response curves and effect modification among 53 million U.S. Medicare beneficiaries

BACKGROUND

The shape of the exposure-response curve for long-term ambient fine particulate (PM_2.5) exposure and cause-specific mortality is poorly understood, especially for rural populations and underrepresented minorities.

METHODS

We used hybrid machine learning and Cox proportional hazard models to assess the association of long-term PM_2.5 exposures on specific causes of death for 53 million U.S. Medicare beneficiaries (aged ≥65) from 2000 to 2008. Models included strata for age, sex, race, and ZIP code and controlled for neighborhood socio-economic status (SES) in our main analyses, with approximately 4 billion person-months of follow-up, and additionally for warm season average of 1-h daily maximum ozone exposures in a sensitivity analysis. The impact of non-traffic PM_2.5 on mortality was examined using two stage models of PM_2.5 and nitrogen dioxide (NO₂).

RESULTS

A 10 μg /m³ increase in 12-month average PM_2.5 prior to death was associated with a 5% increase in all-cause mortality, as well as an 8.8, 5.6, and 2.5% increase in all cardiovascular disease (CVD)-, all respiratory-, and all cancer deaths, respectively, in age, gender, race, ZIP code, and SES-adjusted models. PM_2.5 exposures, however, were not associated with lung cancer mortality. Results were not sensitive to control for ozone exposures. PM_2.5-mortality associations for CVD- and respiratory-related causes were positive and significant for beneficiaries irrespective of their sex, race, age, SES and urbanicity, with no evidence of a lower threshold for response or of lower Risk Ratios (RRs) at low PM_2.5 levels. Associations between PM_2.5 and CVD and respiratory mortality were linear and were higher for younger, Black and urban beneficiaries, but were largely similar by SES. Risks associated with non-traffic PM_2.5 were lower than that for all PM_2.5 and were null for respiratory and lung cancer-related deaths.

CONCLUSIONS

PM_2.5 was associated with mortality from CVD, respiratory, and all cancer, but not lung cancer. PM_2.5-associated risks of CVD and respiratory mortality were similar across PM_2.5 levels, with no evidence of a threshold. Blacks, urban, and younger beneficiaries were most vulnerable to the long-term impacts of PM_2.5 on mortality.

Burden of Disease Assessment of Ambient Air Pollution and Premature Mortality in Urban Areas: The Role of Socioeconomic Status and Transportation

With recent rapid urbanization, sustainable development is required to prevent health risks associated with adverse environmental exposures from the unsustainable development of cities. Ambient air pollution is the greatest environmental risk factor for human health and is responsible for considerable levels of mortality worldwide. Burden of disease assessment (BoD) of air pollution in and across cities, and how these estimates vary according to socioeconomic status and exposure to road traffic, can help city planners and health practitioners to mitigate adverse exposures and promote public health. In this study, we quantified the health impacts of air pollution exposure (PM2.5 and NO2) at the census tract level in Houston, Texas, employing a standard BoD assessment framework to estimate the premature deaths (adults 30 to 78 years old) attributable to PM2.5 and NO2. We found that 631 (95% CI: 366-809) premature deaths were attributable to PM2.5 in Houston, and 159 (95% CI: 0-609) were attributable to NO2, in 2010. Complying with the World Health Organization air quality guidelines (annual mean: 10 μg/m3 for PM2.5) and the US National Ambient Air Quality standard (annual mean: 12 μg/m3 for PM2.5) could save 82 (95% CI: 42-95) and 8 (95% CI: 6-10) lives in Houston, respectively. PM2.5 was responsible for 7.3% of all-cause premature deaths in Houston, in 2010, which is higher than the death rate associated with diabetes mellites, Alzheimer's disease, or motor vehicle crashes in the US. Households with lower income had a higher risk of adverse exposure and attributable premature deaths. We also showed a positive relationship between health impacts attributable to air pollution and road traffic passing through census tracts, which was more prominent for NO2.

Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California

Exposure models are needed to evaluate health effects of long-term exposure to ambient ultrafine particles (UFP; <0.1 μm) and to disentangle their association from other pollutants, particularly PM_2.5 (<2.5 μm). We developed land use regression (LUR) models to support UFP exposure assessment in the Los Angeles Ultrafines Study, a cohort in Southern California. We conducted a short-term measurement campaign in Los Angeles and parts of Riverside and Orange counties to measure UFP, PM_2.5, and black carbon (BC), collecting three 30-minute average measurements at 215 sites across three seasons. We averaged concentrations for each site and evaluated geographic predictors including traffic intensity, distance to airports, land use, and population and building density by supervised stepwise selection to develop models. UFP and PM_2.5 measurements (r = 0.001) and predictions (r = 0.05) were uncorrelated at the sites. UFP model explained variance was robust (R² = 0.66) and 10-fold cross-validation indicated good performance (R² = 0.59). Explained variation was moderate for PM_2.5 (R² = 0.47) and BC (R² = 0.38). In the cohort, we predicted a 2.3-fold exposure contrast from the 5^th to 95^th percentiles for all three pollutants. The correlation between modeled UFP and PM_2.5 at cohort residences was weak (r = 0.28), although higher than between measured levels. LUR models, particularly for UFP, were successfully developed and predicted reasonable exposure contrasts.

A kriging-calibrated machine learning method for estimating daily ground-level NO2 in mainland China

It is unclear how to develop a model based on the combined satellite data and ground monitoring data to accurately estimate daily NO₂ levels. Furthermore, the conventional cross-validation (CV) results represent average levels but the model performance may vary greatly from grid to grid. It is an essential issue to evaluate model's prediction ability in different grids and determine the factors affecting model extrapolating ability, which have never been well examined to date. The aim of this study was to compare the ability of three different methods to estimate the daily NO₂ across mainland China during 2014-2016; and to develop a novel two-stage meta-analysis method for exploring the influence of the number and the distribution of nearby sites on grid-level prediction accuracy. For better estimating the daily NO₂ level, we developed and compared three methods, including universal kriging model, satellite-based method (Non-linear exposure-lag-response model & Extreme gradient boosting combined technique) and the kriging-calibrated satellite method. For exploring influencing factors, the two-stage meta-analysis method was purposed. The kriging-calibrated satellite method had an overall CV R-square and root mean square error (RMSE) of 0.85 and 7.87μg/m³, better than the Universal Kriging model and the satellite-based method (CV R² = 0.57 and 0.81). The two-stage meta-analysis method revealed that the model performance did decrease with the sparser distribution of nearby sites. And adding 5 sites within 50 km in the random mode can bring 17.51% improvement in model extrapolating ability. The kriging-calibration can help satellite-based machine learning to provide more accurate NO₂ prediction. Our novel evaluation method can provide the suggestion of adding new sites effectively within a limit budget.

Predicting Fine-Scale Daily NO2 for 2005-2016 Incorporating OMI Satellite Data Across Switzerland

Nitrogen dioxide (NO₂) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO₂ concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R² range, 0.56-0.64) of the variation in measured NO₂ concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R² range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO₂ across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO₂. The predicted NO₂ concentrations will be made available to facilitate health research in Switzerland.

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM_2.5) and nitrogen dioxide (NO₂) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM_2.5 and 2399 sites for NO₂), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM_2.5) and 1396 sites (NO₂) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM_2.5, the models performed similarly across algorithms with a mean CV R² of 0.59 and a mean EV R² of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R²~0.63; EV R² 0.58-0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R² 0.48-0.57; EV R² 0.39-0.46). Most of the PM_2.5 model predictions at ESCAPE sites were highly correlated (R² > 0.85, with the exception of predictions from the artificial neural network). For NO₂, the models performed even more similarly across different algorithms, with CV R²s ranging from 0.57 to 0.62, and EV R²s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R² > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM_2.5 models whilst dispersion model estimates and traffic variables were most important for NO₂ models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

Spatial variations in ambient ultrafine particle concentrations and risk of congenital heart defects

BACKGROUND

Cardiovascular malformations account for nearly one-third of all congenital anomalies, making these the most common type of birth defects. Little is known regarding the influence of ambient ultrafine particles (<0.1 μm) (UFPs) on their occurrence.

OBJECTIVE

This population-based study examined the association between prenatal exposure to UFPs and congenital heart defects (CHDs).

METHODS

A total of 158,743 singleton live births occurring in the City of Toronto, Canada between April 1st 2006 and March 31st 2012 were identified from a birth registry. Associations between exposure to ambient UFPs between the 2nd and 8th week post conception when the foetal heart begins to form and CHDs identified at birth were estimated using random-effects logistic regression models, adjusting for personal- and neighbourhood-level covariates. We also investigated multi-pollutant models accounting for co-exposures to PM_2.5, NO₂ and O₃.

RESULTS

A total of 1468 CHDs were identified. In fully adjusted models, UFP exposures during weeks 2 to 8 of pregnancy were not associated with overall CHDs (Odds Ratio (OR) per interquartile (IQR) increase = 1.02, 95% CI: 0.96-1.08). When investigating subtypes of CHDs, UFP exposures were associated with ventricular septal defects (Odds Ratio (OR) per interquartile (IQR) increase = 1.13, 95% CI: 1.03-1.33), but not with atrial septal defect (Odds Ratio (OR) per interquartile (IQR) increase = 0.89, 95% CI: 0.74-1.06).

CONCLUSION

This is the first study to evaluate the association between prenatal exposure to UFPs and the risk of CHDs. UFP exposures during a critical period of embryogenesis were associated with an increased risk of ventricular septal defect.

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

BACKGROUND

Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO₂) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model.

METHODS

Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO₂ and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO₂ concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC).

RESULTS

The NO₂ LUR model predicted 84% of spatial variability in annual mean NO₂ (RMSE: 1.2 ppb; cross-validated R²: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R²: 0.90). The annual average NO₂ concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO₂ cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO₂ cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69.

CONCLUSIONS

Our LUR models explained a high degree of spatial variability in annual mean NO₂ and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO₂ for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

Traffic related air pollution and the burden of childhood asthma in the contiguous United States in 2000 and 2010

BACKGROUND

Asthma is one of the leading chronic airway diseases among children in the United States (US). Emerging evidence indicates that Traffic Related Air Pollution (TRAP), as opposed to ambient air pollution, leads to the onset of childhood asthma. We estimated the number of incident asthma cases among children attributable to TRAP in the contiguous US, for the years 2000 and 2010.

METHODS

The number of incident childhood asthma cases and percentage due to TRAP were estimated using standard burden of disease assessment methods. We combined children (<18 years) counts and pollutant exposures at populated US census blocks with a national asthma incidence rate and meta-analysis derived concentration response functions (CRF). NO₂, PM_2.5 and PM₁₀ were used as surrogates of TRAP exposures, with NO₂ being most specific. Annual average concentrations were obtained from previously validated land-use regression (LUR) models. Asthma incidence rate and a CRF for each pollutant were obtained from the literature. Estimates were stratified by urban or rural living and by median household income. We also estimated the number of preventable cases among blocks that exceeded the limit for two counterfactual scenarios. The first scenario used the recommended air quality annual averages from the World Health Organization (WHO) as a limit. The second scenario used the minimum modeled concentration for each pollutant, in either year, as a limit.

RESULTS

Average concentrations in 2000 and 2010, respectively, were 20.6 and 13.2 μg/m³ for NO₂, 12.1 and 9 μg/m³ for PM_2.5 and 21.5 and 17.9 μg/m³ for PM₁₀. Attributable number of cases ranged between 209,100-331,200 for the year 2000 and 141,900-286,500 for 2010, depending on the pollutant. Asthma incident cases due to TRAP represented 27%-42% of all cases in 2000 and 18%-36% in 2010. Percentage of cases due to TRAP were higher (1) in urban areas than rural areas, and (2) in block groups with lowest median household income. Online open-access interactive maps and tables summarizing findings at the county level and 498 major US cities, are available at [https://carteehdata.org/l/s/TRAP-burden-of-childhood-asthma]. Assuming that pollutants did not exceed WHO air quality recommendations, the number of incident cases that could have been prevented ranged between 300 and 53,400, depending on the pollutant and year. Assuming that pollutant levels were limited to the minimum modeled concentration, the number of childhood asthma incident cases that could have been prevented ranged between 127,700 and 317,600, depending on the pollutant and year.

CONCLUSION

This is the first study to estimate the burden of incident childhood asthma attributable to TRAP at a national scale in the US. The attributable burden of childhood asthma dropped by 33% between 2000 and 2010. However, a significant proportion of cases can be prevented.

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities - an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R² (10-fold CV R²) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R² for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.

National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging

Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). Land use, traffic and meteorological variables were included for model building. We tested the resulting models in several ways, including (1) comparing models developed using forward variable selection vs. partial least squares (PLS) variable reduction, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), Leave-One-Province-Out CV (LOPO-CV), and Leave-One-City-Out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Variable-selection models performed similarly to PLS models in 10-fold CV, but better in LOPO-CV. Our best models employed forward variable selection and UK, with 10-fold CV R² of 0.89 (for both 2014 and 2015) for PM_2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO₂. Population-weighted concentrations during 2014-2015 decreased for PM_2.5 (58.7 μg/m³ to 52.3 μg/m³) and NO₂ (29.6 μg/m³ to 26.8 μg/m³). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, >80% of the Chinese population lived in areas that exceeded the Chinese national PM_2.5 standard, 35 μg/m³. Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.

Estimation of PM2.5 mortality burden in China with new exposure estimation and local concentration-response function

The estimation of PM_2.5-related mortality is becoming increasingly important. The accuracy of results is largely dependent on the selection of methods for PM_2.5 exposure assessment and Concentration-Response (C-R) function. In this study, PM_2.5 observed data from the China National Environmental Monitoring Center, satellite-derived estimation, widely collected geographic and socioeconomic information variables were applied to develop a national satellite-based Land Use Regression model and evaluate PM_2.5 exposure concentrations within 2013-2015 with the resolution of 1 km × 1 km. Population weighted concentration declined from 72.52 μg/m³ in 2013 to 57.18 μg/m³ in 2015. C-R function is another important section of health effect assessment, but most previous studies used the Integrated Exposure Regression (IER) function which may currently underestimate the excess relative risk of exceeding the exposure range in China. A new Shape Constrained Health Impact Function (SCHIF) method, which was developed from a national cohort of 189,793 Chinese men, was adopted to estimate the PM_2.5-related premature deaths in China. Results showed that 2.19 million (2013), 1.94 million (2014), 1.65 million (2015) premature deaths were attributed to PM_2.5 long-term exposure, different from previous understanding around 1.1-1.7 million. The top three provinces of the highest premature deaths were Henan, Shandong, Sichuan, while the least ones were Tibet, Hainan, Qinghai. The proportions of premature deaths caused by specific diseases were 53.2% for stroke, 20.5% for ischemic heart disease, 16.8% for chronic obstructive pulmonary disease and 9.5% for lung cancer. IER function was also used to calculate PM_2.5-related premature deaths with the same exposed level used in SCHIF method, and the comparison of results indicated that IER had made a much lower estimation with less annual amounts around 0.15-0.5 million premature deaths within 2013-2015.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R² for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R² than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM_2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM_2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM_2.5 ("SAT-PM_2.5"). We aimed to determine the validity of such satellite-based LUR models for PM_2.5 in Australia. We used global SAT-PM_2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM_2.5 predictor variable (and six others) explained the most spatial variability in PM_2.5 (adjusted R² = 0.63, RMSE (μg/m³ [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R² = 0.52, RMSE: 1.15 [16%]). The evaluation R² of the SAT-PM_2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM_2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM_2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM_2.5 exposure assessment in Australia.

Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stability

BACKGROUND

In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM_2.5, NO₂, BC, O₃) across Europe.

METHODS

We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM_2.5, NO₂, BC and O₃ concentrations (including cold and warm season estimates for O₃). The models were based on AirBase routine monitoring data (PM_2.5, NO₂ and O₃) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM_2.5 and NO₂ additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO₂ and O₃: 2000 and 2005; PM_2.5: 2013).

RESULTS

The PM_2.5, BC, NO₂, O₃ annual, O₃ warm season and O₃ cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R² in the PM_2.5, O₃ warm and O₃ cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM_2.5 (65% vs 66%) and lower for NO₂ (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.

CONCLUSIONS

We developed robust PM_2.5, NO₂, O₃ and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.

Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan

Adequate spatial and temporal estimates of NO₂ concentrations are essential for proper prenatal exposure assessment. Here, we develop a spatiotemporal land use random forest (LURF) model of the monthly mean NO₂ over four years in a metropolitan area of Japan. The overall objective is to obtain accurate NO₂ estimates for use in prenatal exposure assessments. We use random forests to convey the non-linear relationship between NO₂ concentrations and predictor variables, and compare the prediction accuracy with that of a linear regression. In addition, we include the distance decay effect of emission sources on NO₂ concentrations for more efficient model construction. The prediction accuracy of the LURF model is evaluated through a leave-one-monitor-out cross validation. We obtain a high R² value of 0.79, which is better than that of the conventional land use regression model using linear regression (R² of 0.73). We also evaluate the LURF model via a temporal and overall cross validation and obtain R² values of 0.84 and 0.92, respectively. We successfully integrate temporal and spatial components into our model, which exhibits higher accuracy than spatial models constructed individually for each month. Our findings illustrate the advantage of using a LURF to model the spatiotemporal variability of NO₂ concentrations.

Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan

BACKGROUND

Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO₂) based on routine and campaign monitoring data obtained from an urban area.

METHOD

We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO₂ concentrations from each model with measured annual average NO₂ concentrations from evaluation sites.

RESULTS

The routine-LUR-All and routine-LUR-BS models both predicted NO₂ concentrations well: adjusted R²=0.68 and 0.76, respectively, and root mean square error=3.4 and 2.1ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO₂ concentrations at evaluation sites. Although the predicted NO₂ concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city.

CONCLUSION

The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged.

Association between long-term exposure to ambient air pollution and diabetes mortality in the US

OBJECTIVE

Recent mechanistic and epidemiological evidence implicates air pollution as a potential risk factor for diabetes; however, mortality risks have not been evaluated in a large US cohort assessing exposures to multiple pollutants with detailed consideration of personal risk factors for diabetes.

RESEARCH DESIGN AND METHODS

We assessed the effects of long-term ambient air pollution exposures on diabetes mortality in the NIH-AARP Diet and Health Study, a cohort of approximately a half million subjects across the contiguous U.S. The cohort, with a follow-up period between 1995 and 2011, was linked to residential census tract estimates for annual mean concentration levels of PM_2.5, NO₂, and O₃. Associations between the air pollutants and the risk of diabetes mortality (N = 3598) were evaluated using multivariate Cox proportional hazards models adjusted for both individual-level and census-level contextual covariates.

RESULTS

Diabetes mortality was significantly associated with increasing levels of both PM_2.5 (HR = 1.19; 95% CI: 1.03-1.39 per 10 μg/m³) and NO₂ (HR = 1.09; 95% CI: 1.01-1.18 per 10 ppb). The strength of the relationship was robust to alternate exposure assessments and model specifications. We also observed significant effect modification, with elevated mortality risks observed among those with higher BMI and lower levels of fruit consumption.

CONCLUSIONS

We found that long-term exposure to PM_2.5 and NO₂, but not O₃, is related to increased risk of diabetes mortality in the U.S, with attenuation of adverse effects by lower BMI and higher fruit consumption, suggesting that air pollution is involved in the etiology and/or control of diabetes.

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006-2011, could capture nitrogen dioxide (NO₂) concentrations during 1990-2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO₂ predictions: (1) 'do nothing' (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable 'year' in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006-2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R² and mean-square error R² (MSE-R²), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R² = 31%) and 80% (2003; MSE-R² = 78%) of spatial variability in NO₂ in a given year, and 76% (MSE-R² = 72%) averaged over 1990-2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO₂ estimates for Australia during 1990-2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO₂ concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R² = 0.62 (RMSE = 13.3 μg/m³) for daily and R² = 0.73 (RMSE = 6.5 μg/m³) for spatial predictions. The nationwide population-weighted multiyear average of NO₂ was predicted to be 30.9 ± 11.7 μg/m³ (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m³/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m³/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO₂ was predicted to be the highest in North China (40.3 ± 10.3 μg/m³) and lowest in Southwest China (24.9 ± 9.4 μg/m³). Approximately 25% of the population lived in nonattainment areas with annual-average NO₂ > 40 μg/m³. A piecewise linear function with an abrupt point around 100 people/km² characterized the relationship between the population density and the NO₂, indicating a threshold of aggravated NO₂ pollution due to urbanization. Leveraging the ground-level NO₂ observations, this study fills the gap of statistically modeling nationwide NO₂ in China, and provides essential data for epidemiological research and air quality management.

Effect modification of perinatal exposure to air pollution and childhood asthma incidence

Perinatal exposure to ambient air pollution has been associated with childhood asthma incidence, however, less is known regarding the potential effect modifiers in this association. We examined whether maternal and infant characteristics modified the association between perinatal exposure to air pollution and development of childhood asthma.761 172 births occurring between 2006 and 2012 were identified in the province of Ontario, Canada. Associations between exposure to ambient air pollutants and childhood asthma incidence (up to age 6) were estimated using Cox regression models.110,981 children with asthma were identified. In models adjusted for postnatal exposures, second trimester exposures to particulate matter with a diameter ≤2.5 μm (PM2.5) (Hazard Ratio (HR) per interquartile (IQR) increase=1.07, 95% CI: 1.06-1.09) and nitrogen dioxide (NO2) (HR per IQR increase=1.06, 95% CI: 1.03-1.08) were associated with childhood asthma development. Enhanced impacts were found among children born to mothers with asthma, those who smoked during pregnancy, boys, those born preterm, of low birth weight and among those born to mothers living in urban areas during pregnancy.Prenatal exposure to air pollution may have a differential impact on the risk of asthma development according to maternal and infant characteristics.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research

Exposure to traffic related nitrogen dioxide (NO₂) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO₂ concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO₂ data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO₂. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

Changes in Transportation-Related Air Pollution Exposures by Race-Ethnicity and Socioeconomic Status: Outdoor Nitrogen Dioxide in the United States in 2000 and 2010

BACKGROUND

Disparities in exposure to air pollution by race-ethnicity and by socioeconomic status have been documented in the United States, but the impacts of declining transportation-related air pollutant emissions on disparities in exposure have not been studied in detail.

OBJECTIVE

This study was designed to estimate changes over time (2000 to 2010) in disparities in exposure to outdoor concentrations of a transportation-related air pollutant, nitrogen dioxide (NO2), in the United States.

METHODS

We combined annual average NO2 concentration estimates from a temporal land use regression model with Census demographic data to estimate outdoor exposures by race-ethnicity, socioeconomic characteristics (income, age, education), and by location (region, state, county, urban area) for the contiguous United States in 2000 and 2010.

RESULTS

Estimated annual average NO2 concentrations decreased from 2000 to 2010 for all of the race-ethnicity and socioeconomic status groups, including a decrease from 17.6 ppb to 10.7 ppb (-6.9 ppb) in nonwhite [non-(white alone, non-Hispanic)] populations, and 12.6 ppb to 7.8 ppb (-4.7 ppb) in white (white alone, non-Hispanic) populations. In 2000 and 2010, disparities in NO2 concentrations were larger by race-ethnicity than by income. Although the national nonwhite-white mean NO2 concentration disparity decreased from a difference of 5.0 ppb in 2000 to 2.9 ppb in 2010, estimated mean NO2 concentrations remained 37% higher for nonwhites than whites in 2010 (40% higher in 2000), and nonwhites were 2.5 times more likely than whites to live in a block group with an average NO2 concentration above the WHO annual guideline in 2010 (3.0 times more likely in 2000).

CONCLUSIONS

Findings suggest that absolute NO2 exposure disparities by race-ethnicity decreased from 2000 to 2010, but relative NO2 exposure disparities persisted, with higher NO2 concentrations for nonwhites than whites in 2010. https://doi.org/10.1289/EHP959.

Associations between multiple green space measures and birth weight across two US cities

INTRODUCTION

Several measures of green space exposure have been used in epidemiological research, but their relevance to health, and representation of exposure pathways, remains unclear. Here we examine the relationships between multiple urban green space metrics and associations with term birth weight across two diverse US cities.

METHODS

We used Vital Statistics data to create a birth cohort from 2005 to 2009 in the cities of Portland, Oregon (n = 90,265) and Austin, Texas (n = 88,807). These cities have similar green space levels but very different population and contextual characteristics. Green space metrics derived from mother's full residential address using multiple buffer distances (50-1000m) included: Landsat Normalized Difference Vegetation Index (NDVI), % tree cover, % green space, % street tree buffering, and access to parks (using US EPA EnviroAtlas Data). Correlation between green space metrics were assessed and mixed models were used to determine associations with term birth weight, controlling for a comprehensive set of individual and neighborhood factors. City-specific models were run to determine how contextual and population differences affected green space associations with birth weight.

RESULTS

We observed moderate to high degrees of correlation between different green space metrics (except park access), with similar patterns between cities. Unadjusted associations demonstrated consistent protective effects of NDVI, % green space, % tree cover, and % street tree buffering for most buffer sizes on birth weight; however, in fully adjusted models most metrics were no longer statistically significant and no clear patterns remained. For example, in Austin the difference in birth weight for the highest versus lowest quartile of % green space within 50m was 38.3g (95% CI: 30.4, 46.1) in unadjusted and -1.5g (98% CI: -8.8, 6.3) in adjusted models compared to 55.7g (95%CI: 47.9, -63.6) and 12.9g (95% CI: 4.4, 21.4) in Portland. Maternal race, ethnicity and education had the largest impact on reducing green space and birth weight associations. However, consistent positive associations were observed for the high density areas of both cities using several green space metrics at small buffer distances.

CONCLUSIONS

This study highlights the importance of understanding the individual and contextual factors that may confound and/or modify green space and birth weight associations.

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.

Long-Term Exposure to NO2 and Ozone and Hypertension Incidence in the Black Women's Health Study

BACKGROUND

Evidence shows that exposure to air pollutants can increase blood pressure in the short and long term. Some studies show higher levels of hypertension prevalence in areas of high pollution. Few data exist on the association of air pollution with hypertension incidence. The purpose of the present study was to prospectively assess the associations of the traffic-related nitrogen dioxide (NO2) and of ozone with the incidence of hypertension in the Black Women's Health Study (BWHS), a large cohort study of African American women.

METHODS

We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CI) for hypertension associated with exposure to NO2 and ozone among 33,771 BWHS participants. NO2 and ozone levels at participant residential locations were estimated with validated models.

RESULTS

From 1995 to 2011, 9,570 incident cases of hypertension occurred in a total of 348,154 person-years (median follow-up time, 11 years). The multivariable HRs per interquartile range of NO2 (9.7 ppb) and ozone (6.7 ppb) were 0.92 (95% CI = 0.86, 0.98) and 1.09 (95% CI = 1.00, 1.18).

CONCLUSIONS

In this large cohort of African American women, higher ozone levels were associated with an increase in hypertension incidence. Higher NO2 levels were not associated with greater hypertension incidence; indeed, incidence was lower at higher NO2 levels.

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R² of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R² was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R² of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R² was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

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.

Associations between residential greenness and birth outcomes across Texas

BACKGROUND

The amount of greenness around mothers' residences has been associated with positive birth outcomes; however, findings are inconclusive. Here we examine residential greenness and birth outcomes in a population-based birth cohort in Texas, a state with large regional variation in greenness levels, several distinct cities, and a diverse population.

METHODS

We used Vital Statistics data to create a birth cohort (n=3,026,603) in Texas from 2000 to 2009. Greenness exposure measures were estimated from full residential addresses across nine months of pregnancy, and each trimester specifically, using the mean of corresponding MODIS satellite 16-day normalized difference vegetation index (NDVI) surfaces at a 250m resolution, which have not been previously used. Logistic and linear mixed models were used to determine associations with preterm birth, small for gestational age (SGA) and term birth weight, controlling for individual and neighborhood factors.

RESULTS

Unadjusted results demonstrated consistent protective effects of residential greenness on adverse birth outcomes for all of Texas and the four largest cities (Houston, San Antonio, Dallas, and Austin). However, in fully adjusted models these effects almost completely disappeared. For example, mothers with the highest (>0.52) compared to the lowest (<0.37) NDVI quartiles had a 24.4g (95% CI: 22.7, 26.1) increase in term birth weight in unadjusted models, which was attenuated to 1.9g (95% CI: 0.1, 3.7) in fully adjusted models. Maternal and paternal race, ethnicity and education had the largest impact on reducing associations. Trimester-specific greenness exposures showed similar results to nine-month average exposures. Some evidence was seen for protective effects of greenness for Hispanics, mothers with low education and mothers living in low income neighborhoods.

CONCLUSIONS

In this large population-based study, across multiple urban areas in Texas and diverse populations, we did not observe consistent associations between residential greenness and birth outcomes.

Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers

Including satellite observations of nitrogen dioxide (NO₂) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO₂ samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO₂ estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R² (predicted NO₂ regressed on independent measurements of NO₂), mean-square-error R² (MSE-R²), RMSE, and bias. Our models captured up to 69% of spatial variability in NO₂ at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R²) was similar to their correlation (measured by R²). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO₂ monitors. We have demonstrated that such models are a valid approach for estimating NO₂ exposures in Australian cities.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM_2.5 and NO₂ are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM_2.5 and NO₂ models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM_2.5 and NO₂ ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM_2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM_2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR²: 0.33-0.38). For NO₂ CTM improved prediction modestly (adjR²: 0.58) compared to models without SAT and CTM (adjR²: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM_2.5 and NO₂ significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

Air Pollution During Pregnancy and Cord Blood Immune System Biomarkers

OBJECTIVES

We aimed to determine whether average and trimester-specific exposures to ambient measures of nitrogen dioxide (NO2) and particular matter (PM2.5) were associated with elevated cord blood concentrations of immunoglobulin E (IgE) and two epithelial cell produced cytokines: interleukin-33 (IL-33) and thymic stromal lymphopoietin (TSLP).

METHODS

This study utilized data and biospecimens from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study. There were 2001 pregnant women recruited between 2008 and 2011 from 10 Canadian cities. Maternal exposure to NO2 and PM2.5 was estimated using land use regression and satellite-derived models.

RESULTS

We observed statistically significant associations between maternal NO2 exposure and elevated cord blood concentrations of both IL-33 and TSLP among girls but not boys.

CONCLUSIONS

Maternal NO2 exposure may impact the development of the newborn immune system as measured by cord blood concentrations of two cytokines.

A national study of the association between traffic-related air pollution and adverse pregnancy outcomes in Canada, 1999-2008

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.

Ambient air pollution and adverse birth outcomes: Differences by maternal comorbidities

BACKGROUND

Prenatal exposure to ambient air pollution has been associated with adverse birth outcomes, but the potential modifying effect of maternal comorbidities remains understudied. Our objective was to investigate whether associations between prenatal air pollution exposures and birth outcomes differ by maternal comorbidities.

METHODS

A total of 818,400 singleton live births were identified in the province of Ontario, Canada from 2005 to 2012. We assigned exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) to maternal residences during pregnancy. We evaluated potential effect modification by maternal comorbidities (i.e. asthma, hypertension, pre-existing diabetes mellitus, heart disease, gestational diabetes and preeclampsia) on the associations between prenatal air pollution and preterm birth, term low birth weight and small for gestational age.

RESULTS

Interquartile range (IQR) increases in PM2.5 (2μg/m(3)), NO2 (9ppb) and O3 (5ppb) over the entire pregnancy were associated with a 4% (95% CI: 2.4-5.6%), 8.4% (95% CI: 5.5-10.3%) and 2% (95% CI: 0.5-4.1%) increase in the odds of preterm birth, respectively. Increases of 10.6% (95% CI: 0.2-2.1%) and 23.8% (95% CI: 5.5-44.8%) in the odds of preterm birth were observed among women with pre-existing diabetes while the increases were of 3.8% (95% CI: 2.2-5.4%) and 6.5% (95% CI: 3.7-8.4%) among women without this condition for pregnancy exposure to PM2.5 and NO2, respectively (Pint<0.01). The increase in the odds of preterm birth for exposure to PM2.5 during pregnancy was higher among women with preeclampsia (8.3%, 95% CI: 0.8-16.4%) than among women without (3.6%, 95% CI: 1.8-5.3%) (Pint=0.04). A stronger increase in the odds of preterm birth was found for exposure to O3 during pregnancy among asthmatic women (12.0%, 95% CI: 3.5-21.1%) compared to non-asthmatic women (2.0%, 95% CI: 0.1-3.5%) (Pint<0.01). We did not find statistically significant effect modification for the other outcomes investigated.

CONCLUSIONS

Findings of this study suggest that associations of ambient air pollution with preterm birth are stronger among women with pre-existing diabetes, asthma, and preeclampsia.