David Heinold

Emissions of pollutants such as SO2 and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO2 and NO2, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration ...

The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used US air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological ...

ABSTRACT The 1981 VISTTA field study characterized the composition and appearance of particle-rich plumes from three different sources. The paper examines the VISTTA observation from the perspective of an idealized theory, and compares them with the predictions of two plume visibility models. The optics predictions of the two models are less accurate for plumes dominated by particle scattering than they are for plumes dominated by NOâ absorption. Inaccurate prediction of plume particle-size distributions can be identified as one important source of error.