Computed tomography of air pollutants using radial scanning path-integrated optical remote sensing

We propose a novel application of computed tomography (CT) for path-integrated optical remote sensing of air pollutants. We conducted a preliminary study with data obtained from simulated scanning of non-overlapping radial beam segments through Gaussian test distributions and experimentally measured test maps. The smooth basis function minimization (SBFM) algorithm, which fits parametric distributions rather than fitting individual pixel concentrations, was used to reconstruct two-dimensional concentration maps from this beam geometry. The results show that quite good reconstructions are possible with this approach. In contrast to the complex beam geometries proposed in the past for CT, this technique could be applied directly to air monitoring data from a variety of current optical sensing instruments. This development could vastly broaden the application of CT to obtain rapid reconstructions of ambient air pollution data.