Jonathan Levy

To investigate whether exposure to aircraft noise increases the risk of hospitalization for cardiovascular diseases in older people (≥ 65 years) residing near airports. Multi-airport retrospective study of approximately 6 million older people residing near airports in the United States. We superimposed contours of aircraft noise levels (in decibels, dB) for 89 airports for 2009 provided by the US Federal Aviation Administration on census block resolution population data to construct two exposure metrics applicable to zip code resolution health insurance data: population weighted noise within each zip code, and 90th centile of noise among populated census blocks within each zip code. 2218 zip codes surrounding 89 airports in the contiguous states. 6 027 363 people eligible to participate in the national medical insurance (Medicare) program (aged ≥ 65 years) residing near airports in 2009. Percentage increase in the hospitalization admission rate for cardiovascular disease associated ...

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment

Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential...

... Because the NO2 exposure model was built using data collected in the East Boston and Winthrop communities, and because the Rasch ETV score is employed here as a chronic stressor, we expect both exposure estimates to ... Data provided by P. Barry Ryan, Emory University ...

In the interest of protecting the environment and public health of Northwest Queens, the Natural Resource Defense Council (NRDC) and the Citizens Helping Organize for a Klean Environment (CHOKE) participated in the New York State Article X permitting process for ...

Predicting the human-health effects of reducing atmospheric emissions of nitrogen oxide (NOx) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM2.5) and ozone concentrations that result from control of NOx emissions alone and in conjunction with other pollutants in and outside the mega-city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration-response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NOx emission reductions, valued at $300-$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NOx emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.

ABSTRACT Ultrafine particles (UFP) have complex spatial and temporal patterns that can be difficult to characterize, especially in areas with multiple source types. In this study, we utilized mobile monitoring and statistical modeling techniques to determine the contributions of both roadways and aircraft to spatial and temporal patterns of UFP in the communities surrounding an airport. A mobile monitoring campaign was conducted in five residential areas surrounding T.F. Green International Airport (Warwick, RI, USA) for one week in both spring and summer of 2008. Monitoring equipment and geographical positioning system (GPS) instruments were carried following scripted walking routes created to provide broad spatial coverage while recognizing the complexities of simultaneous spatial and temporal heterogeneity. Autoregressive integrated moving average models (ARIMA) were used to predict UFP concentrations as a function of distance from roadway, landing and take-off (LTO) activity, and meteorology. We found that distance to the nearest Class 2 roadway (highways and connector roads) was inversely associated with UFP concentrations in all neighborhoods. Departures and arrivals on a major runway had a significant influence on UFP concentrations in a neighborhood proximate to the end of the runway, with a limited influence elsewhere. Spatial patterns of regression model residuals indicate that spatial heterogeneity was partially explained by traffic and LTO terms, but with evidence that other factors may be contributing to elevated UFP close to the airport grounds. Regression model estimates indicate that mean traffic contributions exceed mean LTO contributions, but LTO activity can dominate the contribution during some minutes. Our combination of monitoring and statistical modeling techniques demonstrated contributions from major surrounding runways and LTO activity to UFP concentrations near a mid-sized airport, providing a methodology for source attribution within a community with multiple distinct sources.

High blood pressure is associated with exposure to multiple chemical and non-chemical risk factors, but epidemiological analyses to date have not assessed the combined effects of both chemical and non-chemical stressors on human populations in the context of cumulative risk assessment. We developed a novel modeling approach to evaluate the combined impact of lead, cadmium, polychlorinated biphenyls (PCBs), and multiple non-chemical risk factors on four blood pressure measures using data for adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (1999-2008). We developed predictive models for chemical and other stressors. Structural equation models were applied to account for complex associations among predictors of stressors as well as blood pressure. Models showed that blood lead, serum PCBs, and established non-chemical stressors were significantly associated with blood pressure. Lead was the chemical stressor most predictive of diastolic blood pressure and mean arterial pressure, while PCBs had a greater influence on systolic blood pressure and pulse pressure, and blood cadmium was not a significant predictor of blood pressure. The simultaneously fit exposure models explained 34%, 43% and 52% of the variance for lead, cadmium and PCBs, respectively. The structural equation models were developed using predictors available from public data streams (e.g., U.S. Census), which would allow the models to be applied to any U.S. population exposed to these multiple stressors in order to identify high risk subpopulations, direct intervention strategies, and inform public policy.

Interpretation of continuous measurements in microenvironmental studies and exposure assessments can be complicated by autocorrelation, the implications of which are often not fully addressed. We discuss some statistical issues that arose in the analysis of microenvironmental particulate matter concentration data collected in 1998 by the Harvard School of Public Health. We present a simulation study that suggests that Generalized Estimating

In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to evaluate quantitatively these surrogates against measured pollutant concentrations to determine how their use affects the interpretation of epidemiological study results. In this study, we quantified the implications of using exposure models derived from validation studies, and other alternative surrogate models with varying amounts of measurement error on epidemiological study findings. We compared previously developed multiple regression models characterizing residential indoor nitrogen dioxide (NO(2)), fine particulate matter (PM(2.5)), and elemental carbon (EC) concentrations to models with less explanatory power that may be applied in the absence of validation studies. We constructed a hypothetical epidemiological study, under a range of odds ratios, and determined the bias and uncertainty caused by the use of various exposure models predicting residential indoor exposure levels. Our simulations illustrated that exposure models with fairly modest R(2) (0.3 to 0.4 for the previously developed multiple regression models for PM(2.5) and NO(2)) yielded substantial improvements in epidemiological study performance, relative to the application of regression models created in the absence of validation studies or poorer-performing validation study models (e.g., EC). In many studies, models based on validation data may not be possible, so it may be necessary to use a surrogate model with more measurement error. This analysis provides a technique to quantify the implications of applying various exposure models with different degrees of measurement error in epidemiological research.