A method for tomographically reconstructing pollutant concentrations in indoor air based on optical detector data was developed. An iterative tomographic reconstruction algorithm, known as the algebraic reconstruction technique, was applied to data obtained from an optical remote sensing system placed in a room study to create two dimensional pollutant concentration (test) maps of the room. Each test map represented a 40 by 40 room with grid cell locations described by a Cartesian coordinate system. The pollutant concentrations were modeled by bivariate normal distributions. Each map could contain one to six randomly located pollutant concentration peaks. Each peak could have a maximum height of 500 parts per million (ppm) and a maximum standard deviation of 6ppm. The method was applied to ten different simulated detector configurations utilizing one to four detectors having scan angles of 90 or 180 degrees (deg). One hundred twenty different test maps were generated. The quality of the maps depended on the number of detectors, location of the detectors, number of peaks in the maps (complexity). Map quality decreased with increasing complexity and increased with increasing number of detectors. Configurations using the same number of detectors also produced test maps of differing quality. The poorest map quality was obtained when the detectors were placed on adjacent walls with scan angles of 180deg.
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