Michael T. Young

Air pollution exposure may induce procoagulant effects, and chronic exposure may be linked to greater risk of venous thromboembolism (VTE). We tested the hypothesis that air pollution is associated with increased VTE risk in the prospective Multi-Ethnic Study of Atherosclerosis, which has well-characterized air pollution measures and information on potential confounding factors. We included 6651 participants recruited in 2000 to 2002 (baseline age range, 45-84 years; 53% female). Air pollution was assessed with a validated spatiotemporal model that incorporates cohort-specific monitoring. Four indexes of air pollution updated each fortnight over follow-up were averaged to estimate participant-level chronic exposure: fine particulate matter ≤2.5 micrometers in aerodynamic diameter (PM2.5), oxides of nitrogen (NOx), nitrogen dioxide (NO2), and ozone (O3). Mean±SD PM2.5 was 13.5±3.0 μg/m3, NO2 17.9±8.2 parts per billion (ppb), NOx 36.1±19.6 ppb, and O3 22.2±3.7 ppb. Incident VTE was identified using hospitalization discharge codes through 2018. A total of 248 VTE events accrued over a median follow-up of 16.7 years. After adjustment for baseline demographics, health behaviors, and body mass index, the hazard ratio (95% confidence interval) for incident VTE associated per 3.6 μg/m3 higher PM2.5 was 1.39 (1.04-1.86); per 13.3 ppb higher NO2 concentration was 2.74 (1.57-4.77); and per 30 ppb higher NOx was 2.21 (1.42-3.44). O3 was not related. In this prospective community-based cohort with individual-level estimation of chronic air pollution exposure, higher average ambient concentrations of PM2.5, NO2, and NOX were associated with greater risk of developing VTE. Findings add to accumulating evidence of adverse health effects attributed to air pollution exposure.

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. To determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. Randomized crossover trial. (ClinicalTrials.gov: NCT05454930). In-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016. Normotensive persons aged 22 to 45 years (n = 16). 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. 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. 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). Imprecise estimates due to small sample size; seasonal imbalance by exposure order. 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. U.S. Environmental Protection Agency and National Institutes of Health.

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 (PM_2.5), 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. Summary 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.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.

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 R² (R²_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 R²_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).