Faculty Bibliography

RATIONALE/BACKGROUND:Many studies have linked short-term exposure to ozone (O3) with morbidity and mortality, but epidemiological evidence of associations between long-term ozone exposure and mortality is more limited. OBJECTIVES/OBJECTIVE:We investigated associations of long-term (annual or warm season average) O3 exposure with all-cause and cause-specific mortality in the NIH-AARP Diet and Health Study, a large prospective cohort of U.S. adults with 17 years of follow-up from 1995 to 2011. METHODS:The cohort (N=548,780) was linked to census tract-level estimates for O3. Associations between long-term O3 exposure (averaged values from 2002-2010) and multiple causes of death were evaluated using multivariate Cox proportional hazards models, adjusted for both individual- and census tract-level covariates, as well as potentially confounding co-pollutants and temperature. MEASUREMENTS AND MAIN RESULTS/RESULTS:Long-term annual average exposure to O3 was significantly associated with deaths due to cardiovascular disease (per 10 ppb, HR=1.03; 95% CI: 1.01-1.06), ischemic heart disease (HR=1.06; 95% CI: 1.02-1.09), respiratory disease (HR=1.04; 95% CI: 1.00-1.09), and chronic obstructive pulmonary disease (HR=1.09; 95% CI: 1.03-1.15) in single-pollutant models. The results were robust to alternative models and adjustment for co-pollutants (fine particulate matter and nitrogen dioxide), although some evidence of confounding by temperature was observed. Significantly elevated respiratory disease mortality risk associated with long-term O3 exposure was found among those living in locations with high temperature (p-interaction<0.05). CONCLUSIONS:This study found that long-term exposure to O3 is associated with increased risk for multiple causes of mortality, suggesting that establishment of annual and/or seasonal federal O3 standard(s) are needed to more adequately protect public health from ambient O3 exposures.

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

BACKGROUND:Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. OBJECTIVE:A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. METHODS:at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. RESULTS:differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. CONCLUSIONS:showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Background: Land use regression (LUR) has been widely used to estimate air pollution exposure in recent epidemiology studies. However, few LUR studies were conducted in China, and even fewer used purposefully designed monitoring networks. The objectives of this study are to obtain preliminary understanding of fine-scale air pollution distributions, and to provide a foundation for a future extended study in Lanzhou, China, a major industrial city. Method(s): A pilot monitoring network was designed using stratified-random sampling, and purposeful selection in gaps of spatial predictor distributions. Based on this network, NO2 were measured using Palmes tubes for 2 weeks in summer 2015, which were used to develop a pilot LUR model considering spatial information of traffic and population densities, elevation, land cover, and land use. We developed linear regression, kriging models, including ordinary kriging, universal kriging, and compared them using AIC. Result(s): The sampling sites of the pilot monitoring network represented wide ranges of spatial predictors (N = 47). The pilot LUR model explained 71% of the variance in the measured NO2 at the sampling sites. The spatial predictors in the model included road densities, elevation, and district indicator. Predicted NO2 concentrations were higher in the east of the city, which is more developed and has dense road networks. Linear regression model performed better than the kriging models due to the lowest AIC. Conclusion(s): This study developed a pilot monitoring network that can effectively capture variability in spatial characteristics and developed a robust LUR model capturing small-scale spatial variations of air pollution in an understudied area. The predicted and measured NO2 showed substantial spatial heterogeneity that was not captured by the limited government monitors. A future study with extended monitoring network and measurements from more seasons is needed to fully understand the distribution of air pollution in Lanzhou, China.

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM_2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R² values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R² values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.

PURPOSE OF REVIEW/OBJECTIVE:Ambient air pollution is strongly linked to cardiovascular and respiratory diseases. We summarize available published evidence regarding similar associations with diabetes across the life course. RECENT FINDINGS/RESULTS:) exposure contributes to more than 200,000 deaths from diabetes annually. There is a growing body of literature linking air pollution exposure during childhood and adulthood with diabetes etiology and related cardiometabolic biomarkers. A small number of studies found that exposure to air pollution during pregnancy is associated with elevated gestational diabetes risk among mothers. Studies examining prenatal air pollution exposure and diabetes risk among the offspring, as well as potential transgenerational effects of air pollution exposure, are very limited thus far. This review provides insight into how air pollutants affect diabetes and other metabolic dysfunction-related diseases across the different life stages.

BACKGROUND:With the number of annual global travelers reaching 1.2 billion, many individuals encounter greater levels of air pollution when they travel abroad to megacities around the world. This study's objective was to determine if visits to cities abroad with greater levels of air pollution adversely impacts cardiopulmonary health. METHODS:Thirty-four non-smoking, adult, healthy participants who traveled abroad to selected cities from the NYC metropolitan area were pre-trained to measure lung function, blood pressure, heart rate/variability, and record symptoms before, during, and after traveling abroad. Outdoor PM2.5 concentrations were obtained from central monitors in each city. Associations between PM exposure concentrations and cardiopulmonary health endpoints were analyzed using a mixed effects statistical design. RESULTS:East and South Asian cities had significantly higher PM2.5 concentrations compared to pre-travel NYC PM2.5 levels, with maximum concentrations reaching 503 μg/m3. PM exposure-related associations for lung function were statistically significant and strongest between evening FEV1 and same day morning PM2.5 concentrations: a 10 μg/m3 increase in outdoor PM2.5 was associated with a mean decrease of 7 ml. Travel to a highly polluted city (PM2.5 > 100 μg/m3) was associated with a 209 ml reduction in evening FEV1 compared to a low polluted city (PM2.5 < 35 μg/m3). In general, participants who traveled to East and South Asian cities experienced increased respiratory symptoms/scores and changes in heart rate and heart rate variability. CONCLUSIONS:Exposure to increased levels of PM2.5 in cities abroad caused small but statistically significant acute changes in cardiopulmonary function and respiratory symptoms in healthy young adults. These data suggest that travel-related exposure to increased PM2.5 adversely impacts cardiopulmonary health, which may be particularly important for travelers with pre-existing respiratory or cardiac disease.

BACKGROUND:Recent experimental evidence suggests that nutritional supplementation can blunt adverse cardiopulmonary effects induced by acute air pollution exposure. However, whether usual individual dietary patterns can modify the association between long-term air pollution exposure and health outcomes have not been previously investigated. We assessed, in a large cohort with detailed diet information at the individual level, whether a Mediterranean diet modifies the association between long-term exposure to ambient air pollution and cardiovascular disease mortality risk. METHODS:air pollution at the residential census-tract level. The alternative Mediterranean Diet Index (aMED), which uses a 9-point scale to assess conformity with a Mediterranean-style diet, was constructed for each participant from information in cohort baseline dietary questionnaires. We evaluated mortality risks for cardiovascular disease (CVD), ischemic heart disease (IHD), cerebrovascular disease (CER), or cardiac arrest (CAR) associated with long-term air pollution exposure. Effect modification of the associations between exposure and the mortality outcomes by aMED was examined via interaction terms. RESULTS:, we found significant associations with CVD (HR=1.06; 95% CI: 1.04-1.08), and IHD (HR=1.08; 95% CI: 1.05-1.11). Analyses indicated that Mediterranean diet modified these relationships, as those with a higher aMED score had significantly lower rates of air pollution related mortality ( p interaction<0.05). CONCLUSIONS:Mediterranean diet reduced cardiovascular disease mortality risk related to longterm exposure to air pollutants in a large prospective U.S cohort. Increased consumption of foods rich in antioxidant compounds may aid in reducing the considerable disease burden associated with ambient air pollution.