NOVEL-APPROACH FOR TOMOGRAPHIC RECONSTRUCTION OF GAS CONCENTRATION DISTRIBUTIONS IN AIR - USE OF SMOOTH BASIS FUNCTIONS AND SIMULATED ANNEALING

Optical remote sensing and iterative computed tomography (CT) can be a pplied to measure the spatial distribution of gaseous pollutant concen trations. We conducted chamber experiments to test this combination of techniques using an open path Fourier transform infrared spectrometer (OP-FTIR) and a standard algebraic reconstruction technique (ART). Al though ART converged to solutions that showed excellent agreement with the measured ray-integral concentrations, the solutions were inconsis tent with simultaneously gathered point-sample concentration measureme nts. A new CT method was developed that combines (1) the superposition of bivariate Gaussians to represent the concentration distribution an d (2) a simulated annealing minimization routine to find the parameter s of the Gaussian basis functions that result in the best Bt to the ra y-integral concentration data. This method, named smooth basis functio n minimization (SBFM), generated reconstructions that agreed well, bot h qualitatively and quantitatively, with the concentration profiles ge nerated from point sampling. We present an analysis of two sets of exp erimental data that compares the performance of ART and SBFM. We concl ude that SBFM is a superior CT reconstruction method for practical ind oor and outdoor air monitoring applications.