Innovation

This report describes semiparametric methods for epidemiologic investigations of the short-term effects of air pollution on health, intended specifically to improve the reliability of point estimates and confidence intervals. Dr. James Robins of the Harvard School of Public Health and colleagues developed the new methods, used simulations to compare them with other methods, and applied them to a large epidemiologic data set from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to assess their effectiveness. The report is accompanied by a short editorial to assist the reader in understanding this study and its contributions to epidemiologic methods for air pollution.

This report describes a study in which a nano aerosol mass spectrometer (NAMS) was used to study composition of nanoparticles in real time near a major roadway intersection. Dr. Murray V. Johnston of the University of Delaware, Newark, and colleagues conducted a field test in Wilmington, Delaware, to evaluate performance of the instrument in a real-world setting and to assess whether it could aid in identifying the major source contributions to nanoparticle spikes and background levels, including distinguishing diesel from gasoline vehicles.

This report describes a study that analyzed the relationships between reductions in pollutants from power plants and fine particulate matter (PM_2.5) concentrations in the eastern United States between 1999 and 2005. Dr. Richard D. Morgenstern of Resources for the Future and colleagues used a novel data-driven source-receptor model to explore the statistical relationships between source emissions of sulfur dioxide (SO₂) and nitrogen oxides and monitored concentrations of PM_2.5. They performed various external comparisons of their models, and compared the reductions to an estimated counterfactual scenario in which no mandated reductions in SO₂ occurred.