Zev Ross

TL;DR: An extended follow-up and spatial analysis of the American Cancer Society Cancer Prevention Study II (CPS-II) cohort was conducted in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities.

TL;DR: Local combustion sources, including traffic and residual oil burning, may play a year-round role in the associations between air pollution and CVD outcomes, but transported aerosols may explain the seasonal variation in associations shown by PM2.5 mass.

TL;DR: In this paper, the authors developed regression equations to predict fine particulate matter (PM 2.5 ) at air monitoring locations in the New York City region using data on nearby traffic and land use patterns.

TL;DR: Evaluation of this land use regression model showed that this method had excellent prediction and robustness in a North American context and may be useful tools in evaluating health effects of long-term exposure to traffic-related pollution.

TL;DR: A hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals is developed to predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.