A Variational Technique for Mesoscale Objective Analysis of Air Pollution

Abstract

A technique for the objective analysis of air pollution based on the calculus of variations is described. Rather than find a system of diffusion coefficients which best fit a source inventory and receptor sampling, a series of hypothetical sources and a background contamination are optimized to best fit the receptor sampling and the diffusion model applied. The advantage of this approach is that no source inventory is necessary. A low-pass filter is described which removes small-scale phenomena from the resulting ensemble of optimized plumes and adjusts the analyzed patterns to better fit the input air quality data. These methods are applied to SO2 data in the vicinity of a zinc smelter at Blackwell in north-central Oklahoma. The resulting objective analyses demonstrate that reasonable patterns of contamination can be extrapolated from sparse air quality data through the use of this technique.