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 non-kriging models. Among kriging models, conventional cross-validated R2 (R2cv) averaged over all years was 0.85 for the satellite data models and 0.84 for the models without satellite data. Average spatially clustered R2cvwas 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).

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-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 While breast cancer risk overall was not associated with PM2.5 (Hazards ratio [HR] = 1.03; 95% 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.

Rationale: Limited prior data suggest an association between trafficrelated air pollution and incident asthma in adults. No published studies assess the effect of long-term exposures to particulate matter less than 2.5 mm in diameter (PM 2.5 ) on adult incident asthma.Objectives: To estimate the association between ambient air pollution exposures (PM 2.5 and nitrogen dioxide, NO 2 ) and development of asthma and incident respiratory symptoms.

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