Novel algorithm for tomographic reconstruction of atmospheric chemicals with sparse sampling.

Numerical studies were performed to evaluate a new air monitoring method for reconstructing chemical exposures and source emissions, based upon optical remote sensing (ORS) and computed tomography (CT). With an ORS-CT system, two-dimensional maps of chemical concentrations can be created that have good spatial and temporal resolution. The mathematical algorithm used to compute the distribution is critical for accurate and useable reconstructions of the concentrations. In this research, a novel reconstruction method was tested that uses maximum likelihood expectation maximization (MLEM) combined with two techniques called grid-translation and multi-grid (GT-MG). To evaluate this method, computer simulations were performed using 120 test maps of varying complexity and a simulated ORS system with four instruments and a total of 40 path-integrated measurements. The results were quantitatively compared with two previously used reconstruction methods (single-grid and grid-translation). Results using the GT-MG method were dramatically improved over previously used methods. Quantitatively, peak exposure errors were reduced by up to 85% and artifacts were dramatically minimized.