Faculty Bibliography

Outdoor air pollution is a major contributor to the burden of disease worldwide. Most of the global population resides in places where air pollution levels, because of emissions from industry, power generation, transportation, and domestic burning, considerably exceed the World Health Organization's health-based air-quality guidelines. Outdoor air pollution poses an urgent worldwide public health challenge because it is ubiquitous and has numerous serious adverse human health effects, including cancer. Currently, there is substantial evidence from studies of humans and experimental animals as well as mechanistic evidence to support a causal link between outdoor (ambient) air pollution, and especially particulate matter (PM) in outdoor air, with lung cancer incidence and mortality. It is estimated that hundreds of thousands of lung cancer deaths annually worldwide are attributable to PM air pollution. Epidemiological evidence on outdoor air pollution and the risk of other types of cancer, such as bladder cancer or breast cancer, is more limited. Outdoor air pollution may also be associated with poorer cancer survival, although further research is needed. This report presents an overview of outdoor air pollutants, sources, and global levels, as well as a description of epidemiological evidence linking outdoor air pollution with cancer incidence and mortality. Biological mechanisms of air pollution-derived carcinogenesis are also described. This report concludes by summarizing public health/policy recommendations, including multilevel interventions aimed at individual, community, and regional scales. Specific roles for medical and health care communities with regard to prevention and advocacy and recommendations for further research are also described.

In Dhaka, Bangladesh, fine particulate matter (PM_2.5) air pollution shows strong seasonal trends, with significantly higher mean concentrations during winter than during the monsoon (winter = 178.1 μg/m³ vs. monsoon = 30.2 μg/m³). Large-scale open burning of post-harvest agricultural waste across the Indo-Gangetic Plain is a major source of PM_2.5 air pollution in northern India during the non-monsoon period. This study evaluates the extent to which the seasonal differences in PM_2.5 pollution concentrations in Dhaka are accounted for by biomass-burning vs. fossil-fuel combustion sources. To assess this, an index was developed based on elemental potassium (K) as a marker for biomass particulate matter, after adjusting for soil-associated K contributions. Alternatively, particulate sulfur was employed as a tracer index for fossil-fuel combustion PM_2.5. By simultaneously regressing total PM_2.5 on S and adjusted K, the PM_2.5 mass for each day was apportioned into: 1) fossil-fuels combustion associated PM_2.5; 2) biomass-burning associated PM_2.5; and, 3) all other PM_2.5. The results indicated that fossil-fuel combustion contributed 21.6% (19.5 μg/m³), while biomass contributed 40.2% (36.3 μg/m³) of overall average PM_2.5 from September 2013 to December 2017. However, the mean source contributions varied by season: PM_2.5 in Dhaka during the monsoon season was dominated by fossil-fuels sources (44.3%), whereas PM_2.5 mass was dominated by biomass-burning (41.4%) during the remainder of the year. The contribution to PM_2.5 and each of its source components by transport of pollution into Dhaka during non-monsoon time was also evaluated by: 1) Conditional bivariate (CBPF) and pollution rose plots; 2) Concentration weighted trajectories (CWT), and; 3) NASA satellite photos to identify aerosol loading and fire locations on high pollution days. The collective evidence indicates that, while the air pollution in Dhaka is contributed to by both local and transboundary sources, the highest pollution days were dominated by biomass-related PM_2.5, during periods of crop-burning in the Indo-Gangetic Plain.

Air pollution is a major contributor to cardiovascular morbidity and mortality. Fine particulate matter <2.5 µm in diameter may be a modifiable risk factor for hypertension. The benefits of in-home air filtration on systolic blood pressure (BP) and diastolic BP are unclear. To examine the effects of in-home personal air cleaner use on fine particulate exposure and BP, we queried PubMed, Web of Science, Cochrane Central Register, Inspec, and EBSCO GreenFILE databases for relevant clinical trials. Included studies were limited to nonsmoking participants in smoke-free homes with active or sham filtration on indoor fine particulate concentrations and changes in systolic and diastolic BP. Of 330 articles identified, 10 trials enrolling 604 participants who met inclusion criteria were considered. Over a median 13.5 days, there was a significant reduction of mean systolic BP by ≈4 mm Hg (-3.94 mm Hg [95% CI, -7.00 to -0.89]; P=0.01) but a nonsignificant difference in mean diastolic BP (-0.95 mm Hg [95% CI, -2.81 to 0.91]; P=0.32). Subgroup analyses indicated no heterogeneity of effect by age, level of particulate exposure, or study duration. Given the variation in study design, additional study is warranted to confirm and better quantify the observed benefits in systolic BP found with personal air cleaner use.

Although it is well accepted that air pollution exposure exacerbates preexisting airway disease, it has not been firmly established that long-term pollution exposure increases the risk of new-onset asthma or chronic obstruction pulmonary disease (COPD). This Workshop brought together experts on mechanistic, epidemiological, and clinical aspects of airway disease to review current knowledge regarding whether air pollution is a causal factor in the development of asthma and/or COPD. Speakers presented recent evidence in their respective areas of expertise related to air pollution and new airway disease incidence, followed by interactive discussions. A writing committee summarized their collective findings. The Epidemiology Group found that long-term exposure to air pollution, especially metrics of traffic-related air pollution such as nitrogen dioxide and black carbon, is associated with onset of childhood asthma. However, the evidence for a causal role in adult-onset asthma or COPD remains insufficient. The Mechanistic Group concluded that air pollution exposure can cause airway remodeling, which can lead to asthma or COPD, as well as asthma-like phenotypes that worsen with long-term exposure to air pollution, especially fine particulate matter and ozone. The Clinical Group concluded that air pollution is a plausible contributor to the onset of both asthma and COPD. Available evidence indicates that long-term exposure to air pollution is a cause of childhood asthma, but the evidence for a similar determination for adult asthma or COPD remains insufficient. Further research is needed to elucidate the exact biological mechanism underlying incident childhood asthma, and the specific air pollutant that causes it.

Since 2009, unconventional natural gas development (UNGD) has significantly increased in Appalachia's Marcellus Shale formation. Elevations of fine particulate matter <2.5 µm (PM2.5), have been documented in areas surrounding drilling operations during well stimulation. Furthermore, many communities are experiencing increased industrial activities and probable UNGD air pollutant exposures. Recent studies have associated UNGD emissions with health effects based on distances from well pads. In this study, PM2.5 filter samples were collected on an active gas well pad in Morgantown, West Virginia, and three locations downwind during hydraulic stimulation. Fine particulate samples were analyzed for major and trace elements. An experimental source identification model was developed to determine which elements appeared to be traceable downwind of the UNGD site and whether these elements corresponded to PM2.5 measurements. Results suggest that 1) magnesium may be useful for detecting the reach of UNGD point source emissions, 2) complex surface topographic and meteorological conditions in the Marcellus Shale region could be modeled and confounding sources discounted, and 3) well pad emissions may be measurable at distances of at least 7 km. If shown to be more widely applicable, future tracer studies could enhance epidemiological studies showing health effects of UNGD-associated emissions at ≥15 km.

BACKGROUND:Ambient air pollution is a modifiable risk factor for cardiovascular disease, yet uncertainty remains about the size of risks at lower levels of fine particulate matter (PM2.5) exposure which now occur in the USA and elsewhere. METHODS:We investigated the relationship of ambient PM2.5 exposure with cause-specific cardiovascular disease mortality in 565 477 men and women, aged 50 to 71 years, from the National Institutes of Health-AARP Diet and Health Study. During 7.5 x 106 person-years of follow up, 41 286 cardiovascular disease deaths, including 23 328 ischaemic heart disease (IHD) and 5894 stroke deaths, were ascertained using the National Death Index. PM2.5 was estimated using a hybrid land use regression (LUR) geostatistical model. Multivariate Cox regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CI). RESULTS:Each increase of 10  μg/m3 PM2.5 (overall range, 2.9-28.0  μg/m3) was associated, in fully adjusted models, with a 16% increase in mortality from ischaemic heart disease [hazard ratio (HR) 1.16; 95% CI 1.09-1.22] and a 14% increase in mortality from stroke (HR 1.14; CI 1.02-1.27). Compared with PM2.5 exposure <8  μg/m3 (referent), risks for CVD were increased in relation to PM2.5 exposures in the range of 8-12  μg/m3 (CVD: HR 1.04; 95% CI 1.00-1.08), in the range 12-20  μg/m3 (CVD: HR 1.08; 95% CI 1.03-1.13) and in the range 20+ μg/m3 (CVD: HR 1.19; 95% CI 1.10-1.28). Results were robust to alternative approaches to PM2.5 exposure assessment and statistical analysis. CONCLUSIONS:Long-term exposure to fine particulate air pollution is associated with ischaemic heart disease and stroke mortality, with excess risks occurring in the range of and below the present US long-term standard for ambient exposure to PM2.5 (12  µg/m3), indicating the need for continued improvements in air pollution abatement for CVD prevention.

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.

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.