This paper reports on a series of large-eddy simulations of a round jet issuing normally into a crossflow. Simulations were performed at two jet-to-crossflow velocity ratios, 2.0 and 3.3, and two Reynolds numbers, 1050 and 2100, based on crossflow velocity and jet diameter. Mean and turbulent statistics computed from the simulations match experimental measurements reasonably well. Large-scale coherent structures observed in experimental flow visualizations are reproduced by the simulations, and the mechanisms by which these structures form are described. The effects of coherent structures upon the evolution of mean velocities, resolved Reynolds stresses, and turbulent kinetic energy along the centreplane are discussed. In this paper, the ubiquitous far-field counter-rotating vortex pair is shown to originate from a pair of quasi-steady ‘hanging’ vortices. These vortices form in the skewed mixing layer that develops between jet and crossflow fluid on the lateral edges of the jet. Axial flow through the hanging vortex transports vortical fluid from the near-wall boundary layer of the incoming pipe flow to the back side of the jet. There, the hanging vortex encounters an adverse pressure gradient and breaks down. As this breakdown occurs, the vortex diameter expands dramatically, and a weak counter-rotating vortex pair is formed that is aligned with the jet trajectory.

We recognized that many health outcomes are associated with air pollution, but in this project launched by the US EPA, the intent was to assess the role of exposure to ambient air pollutants as risk factors only for respiratory effects in children. The NHANES-III database is a valuable resource for assessing children's respiratory health and certain risk factors, but lacks monitoring data to estimate subjects' exposures to ambient air pollutants. Since the 1970s, EPA has regularly monitored levels of several ambient air pollutants across the country and these data may be used to estimate NHANES subject's exposure to ambient air pollutants. The first stage of the project eventually evolved into assessing different estimation methods before adopting the estimates to evaluate respiratory health. Specifically, this paper describes an effort using EPA's AIRS monitoring data to estimate ozone and PM10 levels at census block groups. We limited those block groups to counties visited by NHANES-III to make the project more manageable and apply four different interpolation methods to the monitoring data to derive air concentration levels. Then we examine method-specific differences in concentration levels and determine conditions under which different methods produce significantly different concentration values. We find that different interpolation methods do not produce dramatically different estimations in most parts of the US where monitor density was relatively low. However, in areas where monitor density was relatively high (i.e., California), we find substantial differences in exposure estimates across the interpolation methods. Our results offer some insights into terms of using the EPA monitoring data for the chosen spatial interpolation methods.

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