Long-term exposure to ambient air pollution and measures of central hemodynamics and arterial stiffness among multiethnic Chicago residents.

OBJECTIVES: To examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters. METHODS: We assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5 µm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes. RESULTS: The cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013-2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01). CONCLUSION: This population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans.

Integrating traffic pollution dispersion into spatiotemporal NO2 prediction.

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.

Blood Pressure Effect of Traffic-Related Air Pollution : A Crossover Trial of In-Vehicle Filtration.

BACKGROUND: Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes. OBJECTIVE: To determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. DESIGN: Randomized crossover trial. (ClinicalTrials.gov: NCT05454930). SETTING: In-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016. PARTICIPANTS: Normotensive persons aged 22 to 45 years (n = 16). INTERVENTION: On 2 days, on-road air was entrained into the vehicle. On another day, the vehicle was equipped with high-efficiency particulate air (HEPA) filtration. Participants were blinded to the exposure and were randomly assigned to the sequence. MEASUREMENTS: Fourteen 3-minute periods of blood pressure were recorded before, during, and up to 24 hours after a drive. Image-based central retinal arteriolar equivalents (CRAEs) were measured before and after. Brachial artery diameter and gene expression were also measured and will be reported separately. RESULTS: Mean age was 29.7 years, predrive systolic blood pressure was 122.7 mm Hg, predrive diastolic blood pressure was 70.8 mm Hg, and drive duration was 122.3 minutes (IQR, 4 minutes). Filtration reduced particle count by 86%. Among persons with complete data (n = 13), at 1 hour, mean diastolic blood pressure, adjusted for predrive levels, order, and carryover, was 4.7 mm Hg higher (95% CI, 0.9 to 8.4 mm Hg) for unfiltered drives compared with filtered drives, and mean adjusted systolic blood pressure was 4.5 mm Hg higher (CI, -1.2 to 10.2 mm Hg). At 24 hours, adjusted mean diastolic blood pressure (unfiltered) was 3.8 mm Hg higher (CI, 0.02 to 7.5 mm Hg) and adjusted mean systolic blood pressure was 1.1 mm Hg higher (CI, -4.6 to 6.8 mm Hg). Adjusted mean CRAE (unfiltered) was 2.7 µm wider (CI, -1.5 to 6.8 µm). LIMITATIONS: Imprecise estimates due to small sample size; seasonal imbalance by exposure order. CONCLUSION: Filtration of TRAP may mitigate its adverse effects on blood pressure rapidly and at 24 hours. Validation is required in larger samples and different settings. PRIMARY FUNDING SOURCE: U.S. Environmental Protection Agency and National Institutes of Health.

Long-term exposure to ambient air pollution and measures of central hemodynamics and arterial stiffness among multiethnic Chicago residents.

Objectives: To examine whether air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters. Methods: We assessed central hemodynamic parameters, brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5µm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes. Results: The cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013-2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01). Conclusion: This population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Associations of Pre- and Postnatal Air Pollution Exposures with Child Behavioral Problems and Cognitive Performance: A U.S. Multi-Cohort Study.

BACKGROUND: Population studies support the adverse associations of air pollution exposures with child behavioral functioning and cognitive performance, but few studies have used spatiotemporally resolved pollutant assessments. OBJECTIVES: We investigated these associations using more refined exposure assessments in 1,967 mother-child dyads from three U.S. pregnancy cohorts in six cities in the ECHO-PATHWAYS Consortium. METHODS: Pre- and postnatal nitrogen dioxide (NO2) and particulate matter (PM) ≤2.5µm in aerodynamic diameter (PM2.5) exposures were derived from an advanced spatiotemporal model. Child behavior was reported as Total Problems raw score using the Child Behavior Checklist at age 4-6 y. Child cognition was assessed using cohort-specific cognitive performance scales and quantified as the Full-Scale Intelligence Quotient (IQ). We fitted multivariate linear regression models that were adjusted for sociodemographic, behavioral, and psychological factors to estimate associations per 2-unit increase in pollutant in each exposure window and examined modification by child sex. Identified critical windows were further verified by distributed lag models (DLMs). RESULTS: Mean NO2 and PM2.5 ranged from 8.4 to 9.0 ppb and 8.4 to 9.1 µg/m3, respectively, across pre- and postnatal windows. Average child Total Problems score and IQ were 22.7 [standard deviation (SD): 18.5] and 102.6 (SD: 15.3), respectively. Children with higher prenatal NO2 exposures were likely to have more behavioral problems [ß: 1.24; 95% confidence interval (CI): 0.39, 2.08; per 2 ppb NO2], particularly NO2 in the first and second trimester. Each 2-µg/m3 increase in PM2.5 at age 2-4 y was associated with a 3.59 unit (95% CI: 0.35, 6.84) higher Total Problems score and a 2.63 point (95% CI: -5.08, -0.17) lower IQ. The associations between PM2.5 and Total Problems score were generally stronger in girls. Most predefined windows identified were not confirmed by DLMs. DISCUSSION: Our study extends earlier findings that have raised concerns about impaired behavioral functioning and cognitive performance in children exposed to NO2 and PM2.5 in utero and in early life. https://doi.org/10.1289/EHP10248.

Associations of perinatal exposure to PM2.5 with gestational weight gain and offspring birth weight.

BACKGROUND: PM2.5 have been associated with weight change in animal models and non-pregnant populations. Evidence of associations between PM2.5 and gestational weight gain (GWG), an important determinant of course and outcomes of pregnancy, and subsequent birth outcomes is limited. METHODS: The study was conducted among a subset of participants from the Omega Study, a prospective pregnancy cohort. Exposure to PM2.5 (µg/m3) was ascertained for participants (N = 855) based on their residential address using a validated national spatiotemporal model. Adjusted multivariable linear regression models were used to estimate associations of trimester-specific and pregnancy-month PM2.5 exposures with early (<20 weeks gestation), late (≥20 weeks gestation), and total GWG and infant birth weight. Stratified models and product terms were used to examine whether pre-pregnancy BMI (ppBMI) and infant sex modified the associations. RESULTS: Average monthly PM2.5 exposure during the first, second, and third trimesters were 7.3 µg/m3, 7.9 µg/m3, and 7.7 µg/m3, respectively. Higher third trimester PM2.5 exposure was associated with higher late (0.40 kg per 5 µg/m (McDowell et al., 2018); 95%CI: 0.12, 0.67) and total (0.35 kg; 95%CI: 0.01, 0.70) GWG among participants with normal ppBMI. Higher second month PM2.5 exposure was associated with lower early (-0.70 kg; 95%CI: 1.22, -0.18), late (-0.84 kg; 95% CI: 1.54, -0.14), and total (-1.70 kg; 95%CI: 2.57, -0.82) GWG among participants with overweight/obese ppBMI. Product terms between PM2.5 and ppBMI were significant for second month PM2.5 exposure and early (p-value = 0.01) and total GWG (p-value<0.01). Higher third trimester PM2.5 exposure was associated with higher birth weight, though higher fourth month PM2.5 exposure was associated with lower birth weight, particularly among those with normal ppBMI and male infants. CONCLUSIONS: Associations of PM2.5 with GWG vary by exposure window and ppBMI, while associations of PM2.5 with birth weight potentially vary by exposure window, ppBMI and infant sex. Further exploration of associations between PM2.5 and maternal/child health outcomes are needed.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

PURPOSE OF REVIEW: Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. RECENT FINDINGS: Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 µm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Associations of Pre- and Postnatal Air Pollution Exposures with Child Blood Pressure and Modification by Maternal Nutrition: A Prospective Study in the CANDLE Cohort.

BACKGROUND: Limited data suggest air pollution exposures may contribute to pediatric high blood pressure (HBP), a known predictor of adult cardiovascular diseases. METHODS: We investigated this association in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study, a sociodemographically diverse pregnancy cohort in the southern United States with participants enrolled from 2006 to 2011. We included 822 mother-child dyads with available address histories and a valid child blood pressure measurement at 4-6 y. Systolic (SBP) and diastolic blood pressures (DBP) were converted to age-, sex-, and height-specific percentiles for normal-weight U.S. children. HBP was classified based on SBP or DBP ≥90th percentile. Nitrogen dioxide (NO2) and particulate matter ≤2.5µm in aerodynamic diameter (PM2.5) estimates in both pre- and postnatal windows were obtained from annual national models and spatiotemporal models, respectively. We fit multivariate Linear and Poisson regressions and explored multiplicative joint effects with maternal nutrition, child sex, and maternal race using interaction terms. RESULTS: Mean PM2.5 and NO2 in the prenatal period were 10.8 [standard deviation (SD): 0.9] µg/m3 and 10.0 (SD: 2.4) ppb, respectively, and 9.9 (SD: 0.6) µg/m3 and 8.8 (SD: 1.9) ppb from birth to the 4-y-old birthday. On average, SBP percentile increased by 14.6 (95% CI: 4.6, 24.6), and DBP percentile increased by 8.7 (95% CI: 1.4, 15.9) with each 2-µg/m3 increase in second-trimester PM2.5. PM2.5 averaged over the prenatal period was only significantly associated with higher DBP percentiles [ß= 11.6 (95% CI: 2.9, 20.2)]. Positive associations of second-trimester PM2.5 with SBP and DBP percentiles were stronger in children with maternal folate concentrations in the lowest quartile (pinteraction= 0.05 and 0.07, respectively) and associations with DBP percentiles were stronger in female children (pinteraction= 0.05). We did not detect significant association of NO2, road proximity, and postnatal PM2.5 with any outcomes. CONCLUSIONS: The findings suggest that higher prenatal PM2.5 exposure, particularly in the second trimester, is associated with elevated early childhood blood pressure. This adverse association could be modified by pregnancy folate concentrations. https://doi.org/10.1289/EHP7486.

Deep clustering of protein folding simulations.

BACKGROUND: We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. RESULTS: We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 µs aggregate sampling), villin head piece (single trajectory of 125 µs) and ß- ß- α (BBA) protein (223 + 102 µs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. CONCLUSIONS: Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

Ambient Air Pollution and Chronic Bronchitis in a Cohort of U.S. Women.

BACKGROUND: Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis. OBJECTIVES: Our objective was to estimate the association between long-term exposure to particulate matter (diameter <10 µm, PM10; <2.5µm, PM2.5), nitrogen dioxide (NO2), and both incident and prevalent chronic bronchitis. METHODS: We estimated annual average PM2.5, PM10, and NO2 concentrations using a national land-use regression model with spatial smoothing at home addresses of participants in a prospective nationwide U.S. cohort study of sisters of women with breast cancer. Incident chronic bronchitis and prevalent chronic bronchitis, cough and phlegm, were assessed by questionnaires. RESULTS: Among 47,357 individuals with complete data, 1,383 had prevalent chronic bronchitis at baseline, and 647 incident cases occurred over 5.7-y average follow-up. No associations with incident chronic bronchitis were observed. Prevalent chronic bronchitis was associated with PM10 [adjusted odds ratio (aOR) per interquartile range (IQR) difference (5.8 µg/m3)=1.07; 95% confidence interval (CI): 1.01, 1.13]. In never-smokers, PM2.5 was associated with prevalent chronic bronchitis (aOR=1.18 per IQR difference; 95% CI: 1.04, 1.34), and NO2 was associated with prevalent chronic bronchitis (aOR=1.10; 95% CI=1.01, 1.20), cough (aOR=1.10; 95% CI: 1.05, 1.16), and phlegm (aOR=1.07; 95% CI: 1.01, 1.14); interaction p-values (nonsmokers vs. smokers) <0.05. CONCLUSIONS: PM10 exposure was related to chronic bronchitis prevalence. Among never-smokers, PM2.5 and NO2 exposure was associated with chronic bronchitis and component symptoms. Results may have policy ramifications for PM10 regulation by providing evidence for respiratory health effects related to long-term PM10 exposure. https://doi.org/10.1289/EHP2199.

Hierarchical attention networks for information extraction from cancer pathology reports.

OBJECTIVE: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. MATERIALS AND METHODS: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1-G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. RESULTS: Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macro F-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). CONCLUSIONS: HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

Ambient Air Pollution Exposures and Risk of Parkinson Disease.

BACKGROUND: Few epidemiologic studies have evaluated the effects of air pollution on the risk of Parkinson disease (PD). OBJECTIVE: We investigated the associations of long-term residential concentrations of ambient particulate matter (PM) < 10 µm in diameter (PM10) and < 2.5 µm in diameter (PM2.5) and nitrogen dioxide (NO2) in relation to PD risk. METHODS: Our nested case-control analysis included 1,556 self-reported physician-diagnosed PD cases identified between 1995 and 2006 and 3,313 controls frequency-matched on age, sex, and race. We geocoded home addresses reported in 1995-1996 and estimated the average ambient concentrations of PM10, PM2.5, and NO2 using a national fine-scale geostatistical model incorporating roadway information and other geographic covariates. Air pollutant exposures were analyzed as both quintiles and continuous variables, adjusting for matching variables and potential confounders. RESULTS: We observed no statistically significant overall association between PM or NO2 exposures and PD risk. However, in preplanned subgroup analyses, a higher risk of PD was associated with higher exposure to PM10 (ORQ5 vs. Q1 = 1.65; 95% CI: 1.11, 2.45; p-trend = 0.02) among women, and with higher exposure to PM2.5 (ORQ5 vs. Q1 = 1.29; 95% CI: 0.94, 1.76; p-trend = 0.04) among never smokers. In post hoc analyses among female never smokers, both PM2.5 (ORQ5 vs. Q1 = 1.79; 95% CI: 1.01, 3.17; p-trend = 0.05) and PM10 (ORQ5 vs. Q1 = 2.34; 95% CI: 1.29, 4.26; p-trend = 0.01) showed positive associations with PD risk. Analyses based on continuous exposure variables generally showed similar but nonsignificant associations. CONCLUSIONS: Overall, we found limited evidence for an association between exposures to ambient PM10, PM2.5, or NO2 and PD risk. The suggestive evidence that exposures to PM2.5 and PM10 may increase PD risk among female never smokers warrants further investigation. Citation: Liu R, Young MT, Chen JC, Kaufman JD, Chen H. 2016. Ambient air pollution exposures and risk of Parkinson disease. Environ Health Perspect 124:1759-1765; http://dx.doi.org/10.1289/EHP135.

Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression.

Epidemiological studies increasingly rely on exposure prediction models. Predictive performance of satellite data has not been evaluated in a combined land-use regression/spatial smoothing context. We performed regionalized national land-use regression with and without universal kriging on annual average NO2 measurements (1990-2012, contiguous U.S. EPA sites). Regression covariates were dimension-reduced components of 418 geographic variables including distance to roadway. We estimated model performance with two cross-validation approaches: using randomly selected groups and, in order to assess predictions to unmonitored areas, spatially clustered cross-validation groups. Ground-level NO2 was estimated from satellite-derived NO2 and was assessed as an additional regression covariate. Kriging models performed consistently better than nonkriging models. Among kriging models, conventional cross-validated R(2) (R(2)cv) averaged over all years was 0.85 for the satellite data models and 0.84 for the models without satellite data. Average spatially clustered R(2)cv was 0.74 for the satellite data models and 0.64 for the models without satellite data. The addition of either kriging or satellite data to a well-specified NO2 land-use regression model each improves prediction. Adding the satellite variable to a kriging model only marginally improves predictions in well-sampled areas (conventional cross-validation) but substantially improves predictions for points far from monitoring locations (clustered cross-validation).

Breast Cancer Risk in Relation to Ambient Air Pollution Exposure at Residences in the Sister Study Cohort.

BACKGROUND: Some but not all past studies reported associations between components of air pollution and breast cancer, namely fine particulate matter ≤2.5 µm (PM2.5) and nitrogen dioxide (NO2). It is yet unclear whether risks differ according to estrogen receptor (ER) and progesterone receptor (PR) status. METHODS: This analysis includes 47,591 women from the Sister Study cohort enrolled from August 2003 to July 2009, in whom 1,749 invasive breast cancer cases arose from enrollment to January 2013. Using Cox proportional hazards and polytomous logistic regression, we estimated breast cancer risk associated with residential exposure to NO2, PM2.5, and PM10. RESULTS: Although breast cancer risk overall was not associated with PM2.5 [HR = 1.03; 95% confidence intervals (CI), 0.96-1.11], PM10 (HR = 0.99; 95% CI, 0.98-1.00), or NO2 (HR = 1.02; 95% CI, 0.97-1.07), the association with NO2 differed according to ER/PR subtype (P = 0.04). For an interquartile range (IQR) difference of 5.8 parts per billion (ppb) in NO2, the relative risk (RR) of ER(+)/PR(+) breast cancer was 1.10 (95% CI, 1.02-1.19), while there was no evidence of association with ER(-)/PR(-) (RR = 0.92; 95% CI, 0.77-1.09; Pinteraction = 0.04). CONCLUSIONS: Within the Sister Study cohort, we found no significant associations between air pollution and breast cancer risk overall. But we observed an increased risk of ER(+)/PR(+) breast cancer associated with NO2. IMPACT: Though these results suggest there is no substantial increased risk for breast cancer overall in relation to air pollution, NO2, a marker of traffic-related air pollution, may differentially affect ER(+)/PR(+) breast cancer.

Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S. women.

RATIONALE: Limited prior data suggest an association between traffic-related air pollution and incident asthma in adults. No published studies assess the effect of long-term exposures to particulate matter less than 2.5 µm in diameter (PM2.5) on adult incident asthma. OBJECTIVES: To estimate the association between ambient air pollution exposures (PM2.5 and nitrogen dioxide, NO2) and development of asthma and incident respiratory symptoms. METHODS: The Sister Study is a U.S. cohort study of risk factors for breast cancer and other health outcomes (n = 50,884) in sisters of women with breast cancer (enrollment, 2003-2009). Annual average (2006) ambient PM2.5 and NO2 concentrations were estimated at participants' addresses, using a national land-use/kriging model incorporating roadway information. Outcomes at follow-up (2008-2012) included incident self-reported wheeze, chronic cough, and doctor-diagnosed asthma in women without baseline symptoms. MEASUREMENTS AND MAIN RESULTS: Adjusted analyses included 254 incident cases of asthma, 1,023 of wheeze, and 1,559 of chronic cough. For an interquartile range (IQR) difference (3.6 µg/m(3)) in estimated PM2.5 exposure, the adjusted odds ratio (aOR) was 1.20 (95% confidence interval [CI] = 0.99-1.46, P = 0.063) for incident asthma and 1.14 (95% CI = 1.04-1.26, P = 0.008) for incident wheeze. For NO2, there was evidence for an association with incident wheeze (aOR = 1.08, 95% CI = 1.00-1.17, P = 0.048 per IQR of 5.8 ppb). Neither pollutant was significantly associated with incident cough (PM2.5: aOR = 0.95, 95% CI = 0.88-1.03, P = 0.194; NO2: aOR = 1.00, 95% CI = 0.93-1.07, P = 0.939). CONCLUSIONS: Results suggest that PM2.5 exposure increases the risk of developing asthma and that PM2.5 and NO2 increase the risk of developing wheeze, the cardinal symptom of asthma, in adult women.