Comparisons of radial plume mapping algorithms for locating gaseous emission sources

This paper presents the simulation and field evaluation results for two mathematical algorithms applied in the horizontal radial plume mapping (HRPM) technique with optical remote sensing instruments. In the simulation study, 450 test maps with skewed distributions (i.e., bivariate lognormal distribution) were generated in a two-dimensional domain. The HRPM techniques with the smooth basis function minimization (SBFM) algorithm and non-negative least square (NNLS) algorithm were then applied to reconstruct the plumes, assuming a nine-beam scanning beam geometry. The SBFM algorithm was able to identify the peak locations better than the NNLS algorithm when the plumes were near the origin. On the other hand, the NNLS performed better when the plumes were away from the origin. In the field validation study, four experiments were conducted in an open space with the same nine-beam geometry. In each experiment, two tracer gases were released simultaneously at two different locations, and an OP-FTIR was set up to collect the IR spectra. The collected path-integrated concentration data were then used to reconstruct the source locations. The results confirm the conclusions obtained in the simulation study of a better performance from the SBFM algorithm than from the NNLS algorithm for plumes near the origin. We also developed a screening criterion to determine which algorithm results should be chosen as the final estimates in future field applications.